sensemaking is the first step of problem solving

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Sensemaking Strategies for Ethical Decision-making

Jay j. caughron.

Radford University

Alison L. Antes

Northern Kentucky University

Cheryl K. Stenmark

San Angelo State University

Chaise E. Thiel

The University of Oklahoma

Xiaoqian Wang

Michael d. mumford.

The current study uses a sensemaking model and thinking strategies identified in earlier research to examine ethical decision-making. Using a sample of 163 undergraduates, a low fidelity simulation approach is used to study the effects personal involvement (in causing the problem and personal involvement in experiencing the outcomes of the problem) could have on the use of cognitive reasoning strategies that have been shown to promote ethical decision-making. A mediated model is presented which suggests that environmental factors influence reasoning strategies, reasoning strategies influence sensemaking, and sensemaking in turn influences ethical decision-making. Findings were mixed but generally supported the hypothesized model. Interestingly, framing the outcomes of ethically charged situations in terms of more global organizational outcomes rather than personal outcomes was found to promote the use of pro-ethical cognitive reasoning strategies.

When addressing ethical conduct within organizations, it is important to note the nature of organizational settings. The old model of organizations as bloated bureaucracies has faded. Many of the structures within organizations that individuals relied on as they approached their tasks have evaporated. The boundaries between organizations have blurred. The line between management and workforce has become fuzzy. The relationship between providers of services and products and their customers or clients has developed a new intimacy and complexity. These factors combine to create an environment that is dynamic and complex. People are increasingly finding themselves in situations marked by competing interests, values, and goals, where stakeholders of multiple stripes come together to achieve personal as well as organizational goals

In this complex environment, individuals must make decisions that are responsive to multiple competing demands in a timely manner. It is not difficult to see how the complexity and ambiguity of modern organizations can thrust people into situations that call for ethical decision-making. Circumstances in which a variety of people have competing interests, the outcomes are difficult to predict, and there is sufficient ambiguity to obscure ethical procedures are ripe for ethical misconduct to occur ( Mumford, Connelly, Brown, Murphy, Hill, Antes, Waples, & Devenport, 2008 ).

Previous research has identified a wide array of cognitive reasoning strategies that can promote ethical decision-making. Using these studies to inform their work, Mumford and colleagues consolidated the list of reasoning strategies to a set of seven distinct cognitive reasoning strategies and established that these strategies can promote ethicality (cf., Antes, Brown, Murphy, Hill, Waples, Mumford, Connelly, & Devenport, 2007 ). The strategies are 1) recognizing personal circumstances, 2) anticipating consequences, 3) considering others’ perspectives, 4) seeking help, 5) questioning your own judgment, 6) dealing with emotions, and 7) examining personal values. Extended definitions of these strategies are provided in Table 1 .

Expanded definitions of cognitive reasoning strategies relevant for ethical decision-making

Note: Adapted with permission from Mumford, Connelly, Brown, Murphy, Hill, Antes, Waples, & Devenport, 2007

Research has shown that these strategies are related to ethical decision-making, however several questions remain. For instance, why are these strategies used in some situations but not in others? Also, why do these strategies have an effect on ethical decision-making? This study was designed to address how the use of these reasoning strategies influences the earliest stages of ethical decision-making (i.e., sensemaking) and to help define the conditions in which an individual is more or less likely to use these strategies.

Sensemaking

When faced with a novel, complex, or ambiguous set of circumstances, people tend to move very quickly to develop an understanding of their situation. What is happening? Why is it important? How will this impact me? Do I need to continue to monitor this situation? How can I act to stabilize this situation? All of these are questions people must answer in order to understand unusual circumstances and direct their own behavior. Seeking to understand novel and ambiguous situations is often called sensemaking.

Sensemaking is a complex cognitive process by which an individual develops an understanding of a vexing set of circumstances. The process of making sense of an emergent situation helps people figure out what caused the situation, what the likely outcome of the situation is, and how they can influence the developing situation ( Weick, 1995 ; Weick, Sutcliffe, & Obstfeld, 2005 ). More simply, sensemaking begins when an individual realizes something abnormal is happening and ends when that individual understands the situation well enough to make a decision to act, monitor, or ignore the situation.

Sensemaking can be broken down into three components: problem recognition, information gathering, and information integration. Problem recognition is the first step of sensemaking ( Weick, 1995 ; Weick, Sutcliffe, & Obstfeld, 2005 ). During this stage, the individual recognizes that the status quo has been disturbed and that attention should be paid to this developing situation. Eventually a decision can be made regarding whether or not action is necessary. However, problem recognition is merely the act of realizing that something in a given situation is out of the ordinary.

After an individual recognizes that something is awry, the second and third stages of sensemaking can begin. Namely, information is gathered and integrated ( Mumford, Baughman, Supinski, & Maher, 1996 ). This involves looking for information that can help the individual understand how this situation differs from expectations. Once this information has been gathered the individual can assign meaning to the information and decide how important each piece of information is. Once meaning has been given, the individual can put these pieces of information together to see if larger patterns can be identified. Ultimately, the goal of sensemaking is to identify how important the emergent situation is, why it differs from the norm, and what can be done to influence the outcome of the situation if that is necessary.

Even though these three processes represent a very early stage of the decision-making process, it is important to note that personal biases and situational factors can influence how one makes sense of their circumstances. Not surprisingly, how one interprets an emergent situation is likely to impact the ethicality of decisions regarding acting, monitoring, or ignoring that situation. For example, an individual may demonstrate a tendency to seek out certain types of information and disregard others. Similarly, there may be environmental pressures on the individual that dictate which outcomes are valued. This can result in overemphasizing some aspects of the problem and deemphasizing others. Stated more directly, early attempts at sensemaking can set the trajectory a given decision path takes in complex, ambiguous situations. It is important that we understand how people use sensemaking in these types of situations to inform their subsequent ethical decision-making.

With regard to information gathering, given that ethical events often involve multiple, competing goals, it is likely that those who consider fewer variables are more likely to take a narrow-minded or short-sighted view of a given situation. As such, these individuals are less likely to consider the downstream consequences of their actions (as well as the actions of others), and are more likely to underestimate the importance of neglected stakeholders’ concerns and goals.

Similarly, making an ethical decision often involves considering multiple options, weighing the pros and cons of those options in light of their feasibility, and rendering a decision that recognizes the concerns of multiple parties. Of course, it is rare that all stakeholders can get everything they want. However, making sure to consider as many concerns as possible, from a variety of stakeholders is likely to prevent people from rendering decisions that unduly harm stakeholders. Thus the integration of information aspect of sensemaking also appears to be an important issue to consider with regard to ethical decision-making.

We suggest that sensemaking is the process by which the previously stated reasoning strategies have their beneficial effects on ethical decision-making. Take recognizing circumstances as an example strategy. Given that the execution of the recognizing circumstances strategy involves considering the origins of a problem, the individual’s role to play in the unfolding event, and the goals relevant to the situation it is likely that those who employ this strategy will consider a larger number of issues, a wider variety of issues, and integrate those issues into a coherent mental model of the situation as they approach the problem. A similar line of reasoning suggests that the application of the considering others strategy will perform in much the same way. Given these arguments the following two hypotheses are suggested:

• H1: Individuals who engage in more effective sensemaking will also make more ethical decisions.
• H2: Using cognitive reasoning strategies will promote effective gathering and integration of information.

In order to investigate why strategies are used in some circumstances but not in others, we suggest personal involvement may have a role to play. Ethical situations are often emotionally charged and can influence how others perceive the actor in a given situation. Situations involving ethical decision-making often provide a certain degree of personal gain, gain that sometimes must be put aside in order to avoid inconveniencing or even harming others. We suggest two ways in which personal involvement can play a role in ethical decision-making.

We suggest that when an individual feels responsible for causing an event they are more likely to engage in systematic processing, that is think more critically about the situation at hand. Thus, in this case, people are more likely to use the cognitive reasoning strategies suggested by Mumford and colleagues ( Mumford, Connelly, Brown, Murphy, Hill, Antes, Waples, & Devenport, 2008 ). The use of these strategies will enhance the sensemaking process which will in turn promote higher ethicality in subsequent decision-making.

A second way in which personal involvement can influence ethical decision-making is through perceived outcomes. It is likely that when an individual recognizes personally relevant outcomes may result from a given situation, they will think more critically about the situation. Thus, a similar argument as to the one made earlier would suggest that personally relevant outcomes could result in more strategy use, enhanced sensemaking, and more effective and ethical decisions being made.

However, the nature of the strategies themselves suggest otherwise. When an individual engages in recognizing their circumstances, they are involved in considering the roles others have to play in the unfolding drama. Similarly, when an individual anticipates consequences, it is not merely for themselves but for others. Lastly, the considering others strategy involves taking on the perspective of others when examining the problem. Thus we suggest that outcomes that are personally relevant may cause people to become distracted, focus on themselves, and reduce the degree to which individuals use the cognitive reasoning strategies.

• H3a: When the individual is framed as being a cause of an ethical situation that individual will be more motivated to critically examine the situation and thus engage in more strategy use rather than when they do not feel responsible.
• H3b: When the potential outcomes of an ethical situation are framed in terms of the organization the individual will adopt a wider perspective and engage in more effective strategy use as compared to situations in which the outcomes are framed in personal terms.

The three hypotheses set forward essentially argue that reasoning strategies will influence how an individual engages in sensemaking which will, in turn, influence ethical decision-making. Figure 1 represents these hypotheses in a conceptual model. Hypothesis three speaks to the linkage between situational factors and the use of cognitive reasoning strategies. Hypothesis 2 speaks to the linkage between the cognitive reasoning strategies and sensemaking. Finally, hypothesis 1 speaks to the influence sensemaking has on ethical decision-making (see Figure 1 ).

Conceptual model showing the hypothesized role of environmental factors, ethical reasoning strategies, and sensemaking in ethical decision-making.

The sample consisted of 163 undergraduate students (52 males and 111 females) drawn from an introductory psychology course at a large southwestern university. The study was announced via a website posting describing the study as a leadership problem-solving study. Three hours of research credit in their psychology courses were awarded for participation. The mean age of the participants was 19.2 years of age. The mean reported ACT score was 24.96 suggesting that these students represent a set of typical undergraduates in terms of demographics and general intelligence.

General Procedures

Upon arriving at the study location, participants read and signed an informed consent form. The study was conducted in a single 3-hour session divided into two blocks. The first block was half an hour long and involved a proctor guiding the participants through a series of timed individual difference measures. The second block was scheduled for two and a half hours. During this time, the participants were allowed to complete the remainder of the study materials at their own pace.

The primary experimental task was a low fidelity simulation ( Motowidlo, Dunnette, & Carter, 1990 ) consisting of a written scenario in which the participants assumed the role of a manager overseeing the production of a new medicine at a pharmaceutical company. The participants read a brief description of the company involved in the scenario, including a brief statement about the current circumstances the company was facing. Throughout the rest of the vignette the participants read mock emails from the head of the company presenting four separate, albeit related, problems and asking for solutions for each problem. The participants then wrote their solution in the form of an email to the head of the company.

Individual Difference Measures

Measures were administered in order to control for the role of individual differences upon the variables of interest. Participants’ personality, intelligence, need for cognition, planning skill, narcissism, and cynicism were examined as covariates. Need for cognition was measured with Cacioppo and Petty’s (1982) Need for Cognition scale. This measure asks participants to respond to a series of 15 statements, indicating the degree to which they prefer complex versus simple problems. This scale typically produces internal consistency coefficients in the .80s. Evidence for its construct validity may be obtained by consulting Cacioppo and Petty (1982) .

Planning skills were measured using an abbreviated version of Marta, Leritz, and Mumford’s (2005) planning measure. This assessment tool presents participants with a series of business cases. Participants are asked to indicate the actions they would take in developing a plan to solve the problems presented in each case. This measure produces reliability coefficients in the .70s. Evidence for the validity of this measure can be obtained in Marta, Leritz, and Mumford (2005) . Cynicism was measured using the cynicism subscale of Wrightsman’s (1974) Philosophies of Human Nature measure. This is a standard measure for assessing cynicism and evidence bearing on its validity can be obtained in Wrightsman (1974) . Need for Cognition, planning skills, and cynicism were the only covariate control measures to demonstrate significant relationships with the variables of interest. Thus they were the only three that were retained in the final analysis.

Experimental Manipulations

Framing of cause.

Each of the manipulations were written into the scenario. The framing of cause manipulation had two levels, including situational cause and personal cause. In the situational cause condition, the problem was described as being caused by some set of circumstances occurring outside the participant’s control. In the personal cause condition, it was indicated that the problem was caused by the participant’s character in the scenario. In order to present the problem in one of these ways the text was changed in the email presenting each problem. For example, in one of the scenarios participants were presented with a problem in which a new cancer treatment was delayed by one set of experiments. In the personal caused condition, the participants were informed that they were the ones who asked for this line of studies. In the situational cause condition, the experiments are described as part of routine protocol at the organization. A pilot test of the materials showed that participants in the personal cause condition scored higher on a manipulation check measure assessing the degree to which they felt personally responsible for the problem, as indicated by a mean score of 2.70 (SD=1.10) on a 5 point Likert scale as compared to 2.42 (SD=.94) for those in the situational cause condition, a difference that was statistically significant (t(59) = 19.8, p <.01).

Framing of consequences

This manipulation framed the consequences stemming from the problem towards the individual or the individual’s employer. For example, in the same scenario described above, outcomes in the personal framing of consequences condition were framed as being personally relevant (e.g., promotion opportunity, demotion, reputation gains and losses). Alternatively, in the organizational framing of consequence condition, the outcomes are framed in terms of organizational outcomes (e.g., profits, losses, market share gains and losses). A pilot test of the materials showed that participants in the organizational outcomes condition perceived the outcomes as significantly more organizationally relevant than those in the personal outcome condition as indicated by a mean score of 3.10 (SD=1.28) compared to a mean of 2.63 (SD=1.07) on a 5-point Likert scale (t(119) = 26.7, p <.01).

Content coding

Content coding was used to measure three different types of variables in this study: strategy use, sensemaking, and ethicality. The three judges involved in the content coding effort were all senior-level graduate students who received over 20 hours of training. During this training, the judges were introduced to operational definitions regarding the strategies, sensemaking, and ethicality. Additionally, time was spent during each training session rating materials and comparing ratings on a subset of materials drawn from the participants’ responses to the stimulus materials. Ratings for each construct were made on a 5-point Likert scale. Discussions were held when judges did not agree on how to rate a given response until the judges had a minimum reliability of .70 on ten items drawn from the participant materials for each construct they were rating. After this was achieved, the judges were given the rest of the participant materials to rate and reliabilities were checked again at the end of the study. The judges were blind to the participants’ conditions. Judges were each given a manual describing the rating strategy, which included definitions of each construct, markers that highlighted key aspects of the construct, and example materials drawn from participant responses representing high, medium, and low performance on each construct.

Strategy use

With regard to coding the strategies, the judges were familiarized with the definitions for each strategy, as described previously in Table 1 . For example, the recognizing circumstances strategy was defined as the process by which “people think about how their position in their group, organization, and society related to the origins of the problem, individuals involved, and relevant principles, goals, and values.” Some of the markers for this strategy included “defining their role and responsibilities,” “recognizing the causes of the situation,” and “demonstrating knowledge of the potential conflicts between people and goals.”

As mentioned above, examples drawn from participant materials were used to demonstrate examples of high, medium, and low performance for each construct. The examples below were drawn from responses to the first scenario in the participants’ materials in which a new cancer drug has shown some problems when administered to older patients and two of the advisors working on the project have given the participant conflicting advice regarding how to proceed in resolving the issue. The example used from the participant materials denoting a high level of performance reads as follows:

The drug was very effective. It shrank most of the people’s tumors. It was not as effective for some elderly patients and some of them also had side effects. Dr. Garrison wants to hold up the drug to study the side effects. Dr. Miller thinks the drug should go to market because the side effects are minor.

The example denoting a moderate level of performance is as follows:

The drug shrank 90% of the participants’ tumors. Some of the elderly patients experienced side effects and the drug did not work as well on them. People in the company want to see it work.

Lastly, the example demonstrating a low level of performance read as follows:

It didn’t work as well on older participants.

Similar procedures were used to train the judges on each of the seven strategies listed in Table 1 . Cronbach’s alpha was used to assess the interrater reliability for each of the strategies that were coded. Reliabilities for the recognizing circumstances strategy was .73, for anticipating consequences was .80, and for considering others was .66. The other 4 strategies, dealing with emotions, examining personal motives, seeking help, and questioning judgment were not demonstrated by the participants frequently enough to gather reliable data for them, thus these strategies were not examined in the analyses.

Three key elements of sensemaking were coded for in this study: The number of issues participants identified, the variety of issues identified, and the degree to which the issues were taken together or integrated in the participants’ responses. In coding for the former variable, the number of issues identified, judges merely counted the number of different and distinct issues the participants identified when prompted to “summarize the relevant concerns, goals, and opinions of all the people involved… (including your own)” and to predict the “potential outcomes for all parties involved.”

The second sensemaking variable, the variety of issues identified, was rated somewhat differently. The judges were instructed to look for indicators that participants recognized four distinct types of issues in their responses. These issue types were 1) financial, 2) logistical, 3) social, and 4) ethical. Financial issues were elements of the problem involving money. Logistical issues were those issues that dealt with managing resources, manufacturing a product, maintaining communication chains, or other practical concerns with product development and delivery. Social issues were those that involved people, such as managing conflicts between people, managing relationships with customers, or considering the impact of issues on employees. Finally, ethical issues were defined as those that specifically mentioned potential misconduct (e.g., stealing or lying), doing what is ‘right,’ or considering how others could be harmed by pursuing a given course of action. The judges denoted the presence of each of these issue types with a 1 or rated the item as a 0 if the participants did not mention issues of a given category. These ones and zeros were then summed such that the participant was given a score between 0 and 4 indicating how wide a variety of issues they identified.

The third, and final, sensemaking variable was that of information integration. This was rated on a 5-point Likert type scale. The judges were given example materials in which participants exhibited high, moderate, or low levels of integration when responding to the prompts “summarize the relevant concerns, goals, and opinions of all the people involved… (including your own)” and to predict the “potential outcomes for all parties involved.” High levels of integration were demonstrated when the participant 1) addressed the problem as a whole, 2) recognized the relationships between issues in the situation, and 3) addressed multiple issues in any potential actions mentioned. Low levels of integration were demonstrated when the participant 1) discussed issues independently, 2) overlooked relationships between issues, and 3) focused actions on resolving a few or only one issue in the situation.

Once again Cronbach’s alpha was used to assess the reliability of the judges’ ratings on each of these variables. The reliability for the number of issues identified was .68, for the variety of issues identified was .71, and for information integration was .74.

Judges used a 5-point Likert type scale to rate the ethicality of responses to each of the four scenarios presented to the participants. An examination of existing literature regarding the nature of ethical responses in ambiguous circumstances revealed three aspects of problem solutions that should be considered when judging the ethicality of a response ( Darke & Chaiken, 2005 ; Gilligan & Attanucci, 1988 ). First, is holding the welfare of others in high regard, this marker for ethicality was called Regard for Others. Second, was making sure to fulfill personal obligations, this marker was called Attending to Personal Responsibilities. The third, and final, marker of ethicality was called Adherence to/Awareness of Social Obligations and involved being mindful of norms, values, duties, and guidelines within a given social system regardless of whether or not they represent personal values.

Judges were told to consider each of these three aspects of ethicality when making their ethicality rating for each response. Thus responses that knowingly hurt others, willfully disregarded personal commitments, and violated appropriate norms of expected behavior towards an individual or to social groups more generally (e.g., patients) were rated lower in ethicality than responses that either did not mention these things or, in the best case scenario, actively mentioned pursuing actions that took the welfare of others into account, respected personal obligations, and were mindful of broader social norms and values. Cronbach’s alpha was used to assess the reliability of the judges’ ratings for ethicality. The average alpha across the 4 scenarios was .87.

Table 2 presents the correlations between ethicality, the sensemaking variables, and the strategy variables. It is of note that the ethicality variable correlated significantly with all three sensemaking variables ( p <.01). Ethicality was also significantly correlated with the strategy variables as were the sensemaking variables. In order to control for co-variation among predictors and the existence of other relevant control variables, hierarchical multiple regression was used to test the first two hypotheses.

Correlations between the Ethicality, Strategy Use Variables, and Sensemaking Variables

Note: All correlations significant at the p < .01 level.

Hypothesis 1 was examined using a multiple regression approach. In this analysis we predicted ethicality from sensemaking. The first step of the regression contained the control variables need for cognition and cynicism. The second step included the sensemaking variables. The change in R 2 between block 1 and block 2 was significant ( p <.05), indicating that the sensemaking variables are significantly related to ethicality over and above the model with the control variables alone. An examination of the beta weights reveals that the number of elements identified was not related to ethicality, the relationship between ethicality and the number of issue types identified approached statistical significance (β =.151; p <.10), and the relationship between ethicality and information integration was statistically significant (β = .265; p <.05). These results offer partial support for hypothesis 1, higher quality sensemaking does appear to be related to higher levels of ethicality in decision-making. Figure 2 offers a pictorial representation of these relationships.

Summary of multiple regression findings predicting Ethicality from Sensemaking variables.

The next set of analyses examined hypothesis 2. As with hypothesis 1, we used multiple regression but this time we examined the relationships between the cognitive reasoning strategies identified in Table 1 with sensemaking. In each of these analyses need for cognition and planning skill were retained as covariates in the first step of the regression. The change in R 2 for the second block was significant in each analysis which examined the amount of variance accounted for by the addition of the anticipating consequences, recognizing circumstances, and considering others reasoning strategies.

The number of issues identified was significantly related to recognizing circumstances (β = .515; p <.05). The anticipating consequences and considering others strategies, with regard to the number of issues identified only approached significance (β = .171; β = .169; p <.10). The only strategy variable significantly related to the number of issue types identified was considering others (β = .286; p <.05). Lastly, information integration was significantly related to recognizing circumstances and considering others (β = .558; β = .213; p <.05), while anticipating consequences was approaching significance (β = .155; p <.10). Thus hypothesis 2 was partially supported; the use of reasoning strategies identified in previous research is related to higher quality sensemaking. Figures 3 , ​ ,4, 4 , and ​ and5 5 offer a pictorial representation of these relationships.

Summary of multiple regression findings predicting sensemaking from anticipating consequences.

Summary of multiple regression findings predicting sensemaking from recognizing circumstances.

Summary of multiple regression findings predicting sensemaking from considering others.

In order to test the third hypothesis regarding the effect of the manipulations on the cognitive reasoning strategies, a multivariate analysis of covariance (MANCOVA) was used. Need for cognition was retained as a covariate in this analysis. This analysis was used to see how manipulating the framing of the causes and consequences influenced the use of the cognitive reasoning strategies. The omnibus results reveal a significant main effect for the framing of consequence variable ( F cons (3, 152) = 3.16; p <.05; η 2 = .06), but no other significant main effects or interactions ( F caus (3, 152) = 2.05; p <ns; F cons*caus (3, 152) = 1.70; p <ns). Thus hypotheses 3a was not supported, framing the individual as being responsible for causing an ethical event does not appear to have an effect on strategy use.

An ANOVA was used to examine the framing of consequences manipulation further. The results of this analysis showed that this manipulation was significantly related to recognizing circumstances ( F (1, 154) = 9.49; p <.01; η 2 = .06), anticipating consequences ( F (1, 154) = 7.70; p <.01; η 2 =.05), and considering others ( F (1, 154) = 2.44; p <.01; η 2 = .05). Figure 6 presents a graphical representation of the means for these relationships, revealing that in each case when the outcomes were framed as being organizational rather than personal higher levels of strategy use were observed. This finding supports hypothesis 3b, framing the outcomes of an ethical situation in terms of being organizationally relevant, individuals engage in more effective strategy use.

Graphical display of mean differences in strategy used for the Framing of Outcome manipulation.

Note: Personal condition significantly lower at the p<.01 level for all strategies as rated on a 5-point scale.

Before discussing the implications of these findings, a few limitations should be noted. First, the use of an undergraduate sample could limit the generalizability of these findings. The majority of the individuals participating in this study were Midwestern students between the ages of 18–21. It is possible, indeed likely, that samples that included older individuals might respond differently to these experimental manipulations. Given that people have an increasing level of experience with ethical dilemmas as they grow older, it would not be surprising that they would approach ethical dilemmas from a different perspective. It is important to note that it is unclear whether or not older participants will be any better at making ethical decisions. It could be that experience teaches people how to better handle ethical problems. Alternatively, people who have experienced the ‘losing end’ of ethical dilemmas may become jaded leading them to make worse decisions. Further research in this area is warranted.

On another note, this study did not focus on variables such as self-image or other social domains which are known to vary with age and instead focuses on the cognition individuals engage in when presented with an ethical problem. Given this fact, the effect of using an undergraduate sample should provide some degree of generalizability and still function to further our knowledge of the cognition involved in making ethical decisions ( Wintre, North, & Sugar, 2001 ). Additionally, while generalizability is of some concern, the best way to establish boundaries on generality is via replication. Thus, the concern of internal validity was of tantamount importance in this study as compared with the effects on external validity which will be better addressed with replications of this study with older participants.

A second limitation to consider when interpreting these findings is that there was a lack of power in some analyses. This can be seen in the small eta squared values in the MANCOVA analysis as well as the marginally significant findings in the regression analyses. We interpret these findings with caution given the limited power afforded by the sample size and the use of a low fidelity simulation task. It is likely that these effects stemming from the manipulations might demonstrate much larger effects in real world settings, due to the fact that a low fidelity simulation can only approximate the impact the potential outcomes would have on an individual’s emotional and cognitive processes.

One final limitation that should be mentioned is the potential for method bias. All the variables reported in this study were collected using expert raters judgments on materials from a low-fidelity simulation. The participants responded to three different questions for each scenario during the course of the experiment. The first two responses were content coded for information bearing on sensemaking and strategy use. Ethicality was coded for using the third response participants gave for each scenario. Because these data were collected using the same method it is likely that some of the variance they share is due to the method rather than the constructs themselves being interrelated. Thus the findings should be interpreted accordingly.

Bearing these limitations in mind, we feel that certain implications are warranted. On the broadest level, taken together these findings suggest that taking a broad perspective from the earliest stages of encountering an ethical situation can help individuals make more ethical decisions. Sensemaking involves the recognition and diagnosis of a novel, unusual, or ambiguous situation. Sensemaking begins when a person recognizes that a strange situation is unfolding and ends when a person has established some understanding of the circumstances in their mind, thus preparing the way for action. Given that ethical dilemmas are often novel, unusual, or ambiguous situations sensemaking is particularly relevant. Additionally, it is often the case that the initial understanding of an ethically ambiguous situation coupled with the first few actions one takes creates a trajectory for the unfolding situation. Thus, knowing how to encourage people to be effective at creating an understanding of an ethical situation can go a long way towards creating the type of momentum that will facilitate the most ethical resolution

The evidence supporting this assertion stems from the three findings from the current study. First, is the finding that considering a variety of issues and integrating disparate information were related to higher ethical decision-making. Second, is the fact that the strategies of recognizing circumstances and considering others had a notable impact on sensemaking. Third, and finally, that framing issues in terms of organizational outcomes rather than personal outcomes caused the strategies of recognizing circumstances and considering others to be used more effectively. We will discuss each of these points in turn.

The first set of findings supporting the assertion that taking a broad perspective promotes ethical decision-making is the pattern of results bearing on the relationship between sensemaking and ethical decision-making. Interestingly, the number of different issues the individual considered was not significantly related to ethicality. However, the variety of issues considered approached significance and the integration of information was significantly related to ethical decision-making. Thus it appears that it is not enough merely to engage in active cognition about the problem at hand; if that were the case merely considering a large number of issues would be related to ethical decision-making. Rather, how one goes about actively thinking about the problem matters. Considering a variety of issues and integrating that information into a coherent, understandable interpretation of the given situation is important for coming to an ethical decision. While promoting active cognition in a hectic environment is a challenge in and of itself, it is important that the active cognition individuals engage in takes into account multiple perspectives and a variety of issues.

The second line of evidence for the above assertion comes from the findings regarding the relationship between cognitive reasoning strategies and sensemaking. The strategies that were examined were originally developed by Mumford and colleagues and have been shown to be related to ethical decision-making in a variety of studies, using a variety of techniques ( Antes, et al., 2007 ). What has been unclear up to this point is why these strategies have been associated with higher levels of ethical decision-making. Some evidence to clarify this issue was found in the current study. Recognizing circumstances and considering others were the two strategies that demonstrated the clearest relationship with sensemaking in this study.

Both, recognizing circumstances and considering others were significantly related to information integration, additionally, considering others was significantly related to the variety of issues identified by participants. Recognizing circumstances and considering others are two strategies that encourage individuals to consider multiple lines of information and think about a problem from multiple perspectives. Given that the variety of issues considered and the act of integrating information were significantly related to ethical decision-making (albeit marginally in the case of the variety of issues identified) this adds credence to the assertion that broader perspective-taking is important at the beginning stages of ethical cognition.

The final line of evidence that lends itself to the assertion that taking broader perspectives facilitates ethical decision-making comes from the effects of manipulating environmental conditions on the strategies used by the participants. Interestingly, whether the cause of the event was construed as something that the participant initiated or as something thrust upon them by the circumstances had no discernable effect on strategy use. Rather, what seemed to influence how effectively participants used the reasoning strategies was how the outcomes were framed with regard to their level of impact (e.g., personal vs. organizational).

A strong argument could be made that when outcomes are framed as being very personally relevant it is likely that the individual will engage in more active cognition and develop a more effective solution to a given problem. However, as stated earlier, it is not merely the act of engaging in active cognition that is important. It is how the individual thinks about the problem while they are engaging in active cognition that matters. In this case, when the outcomes were framed as being organizationally relevant, participants more effectively utilized the anticipating consequences, recognizing circumstances, and considering others reasoning strategies.

This finding suggests that emphasizing the personal relevance of outcomes for individuals rather than emphasizing the broader impact those outcomes may backfire, at least with regard to ethical decision-making. It is conceivable that making a situation personally relevant will encourage an individual to pay more attention or use a larger portion of their available cognitive resources on a given problem. However, people tend to be notoriously self-focused and facilitating this personal focus may only exacerbate the tunnel vision people are already likely to demonstrate. This finding suggests that helping others see the larger, organizational impact of a given set of circumstances and their role within that situation, is likely to be a more effective strategy than merely helping the individual see the personally relevant outcomes inherent in a given situation.

Interestingly, it is often the case that an organizational leader will work to help people see the personal relevance of a given situation rather than the organizational relevance. Managers often try to help people see the good or bad outcomes that may result if a given situation isn’t handled effectively in order to increase their motivation to make an effective decision. However, highlighting the potential individual level rewards or consequences someone might face could lead them to make a less ethical decision. This study suggests that organizational leaders should emphasize broader outcomes rather than more personal outcomes when ethical decisions are being made.

Taken together these findings help clarify conditions in which individuals are likely to use pro-ethical cognitive reasoning strategies. Specifically, organizational leaders are encouraged to frame outcomes in terms of the organization rather than emphasizing personally relevant outcomes It appears that one mechanism by which these pro-ethical cognitive reasoning strategies have their effect on ethical decision-making is through their effect on sensemaking. Sensemaking is an important cognitive process that facilitates the individual’s ability to understand a situation, make decisions, and take action. As such it is likely that it determines the trajectory a problem takes and the solution that is likely to arise. Individuals should be encouraged to consider a broad array of issues and perspectives from the very beginning of addressing an ethically sensitive issue and to form a coherent, unified understanding of that situation such that the concerns and goals of multiple parties can be attended to when resolving issues of this type.

Contributor Information

Jay J. Caughron, Radford University.

Alison L. Antes, Northern Kentucky University.

Cheryl K. Stenmark, San Angelo State University.

Chaise E. Thiel, The University of Oklahoma.

Xiaoqian Wang, The University of Oklahoma.

Michael D. Mumford, The University of Oklahoma.

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Organizational sensemaking.

• Ravi S. Kudesia Ravi S. Kudesia Washington University in Saint Louis
• https://doi.org/10.1093/acrefore/9780190236557.013.78
• Published online: 26 April 2017

Since the 1980s, the management and organizations literature has grown substantially, turning over the years toward cognitive, discursive, and phenomenological perspectives. At the heart of this continued growth and its many turns is the matter of sensemaking. Construed narrowly, sensemaking describes the process whereby people notice and interpret equivocal events and coordinate a response to clarify what such events mean. More broadly, sensemaking offers a unique perspective on organizations. This perspective calls attention to how members of organizations reach understandings of their environment through verbal and embodied behaviors, how these understandings both enable and constrain their subsequent behavior, and how this subsequent behavior changes the environment in ways that necessitate new understandings.

Whereas organizational psychology constructs typically fit most comfortably into a linear “boxes and arrows” paradigm, sensemaking highlights a recursive and ongoing process. Sense is never made in a lasting way: It is always subject to disruption and therefore must be continually re-accomplished. As a result, sensemaking is especially evident when equivocal events cause breakdowns in meaning. Such breakdowns render organizations incapable of answering two key questions: “What’s going on here?” and “What should we do about it?” Not coincidentally, such events—including crisis situations, strategic change episodes, firm formations and dissolutions, and new member socialization—are among the most pivotal events that occur in organizations. Sensemaking is therefore strongly implicated in organizational change, learning, and identity.

Sensemaking can appear impenetrable to newcomers for precisely the same reason that it enables remarkably incisive analyses: the sensemaking perspective helps disrupt limiting rationality assumptions that are so often embedded in organizational theories. As such, sensemaking sensitizes scholars to counterintuitive aspects of organizational life. These aspects include how action in organizations often precedes understanding rather than following from it, how organizations are beset by a surplus of possible meanings rather than a scarcity of information, how retrospective thought processes often trump future-oriented ones, and how organizations help create the environments to which they must react. Nonetheless, despite these advances and insights, much remains to be learned about sensemaking as it relates to emotion and embodiment; as it occurs across individual, group, organizational, and institutional levels of analysis; and as it both shapes and is shaped by new technologies.

• sensemaking
• organizational cognition
• social construction
• interpretation

Introduction to Sensemaking

Sensemaking in action: the chicago board options exchange.

It is often easiest to understand sensemaking with an example of how it unfolds in action. For this reason, we begin with an example (see MacKenzie & Millo, 2003 ):

There are few organizational environments as tumultuous as that of financial derivatives trading. In 1973 , the economists Black and Scholes tried to understand how options were priced in the newly instituted Chicago Board Options Exchange. They developed a formula that priced options (i.e., the ability to purchase a particular commodity at a specific price) based on a handful of parameters like its spot price or time to maturity. In the first months of trading, the formula failed to predict option prices, with typical deviations as large as 30% to 40%. Yet, over time, the formula became accurate to within the low single digits, leading its creators to receive Nobel Prizes. Interestingly enough, however, the formula became accurate only because market participants acted as if it was accurate. Traders used values derived from the formula to inform their bids, the formula became integrated into trading regulations, and assumed in technological infrastructure. The Black–Scholes formula began by modeling options prices. It ended up modeling options prices in response to its modeling of option prices.

This case has much to tell us about sensemaking. In fact, it describes three pertinent features of the sensemaking perspective: (1) organizations operate in environments characterized by chaos and flux, (2) people develop plausible and tentative interpretations of their environments by noticing and bracketing out certain pieces of information, and (3) by acting on the basis of these interpretations, people actually make their environments more orderly and better understood (Weick, Sutcliffe, & Obstfeld, 2005 ). As our story indicates, from the initial chaos of prices and bids in options trading, Black and Scholes extracted certain plausible parameters and compiled them into a tentative formula, broader social utilization of this formula changed prices and bidding behavior, and the world of options trading became more orderly as a result.

Sensemaking: A Perspective and a Process

Perhaps the critical point underlying sensemaking research is that action and knowledge are recursively entangled. It is difficult to act without knowing the context in which action is to occur. And yet, it is difficult to know much about any context without first acting on it. This insight is not always obvious because when the events we face are familiar—as they typically are—our existing knowledge structures most often prove adequate. We can form a reasonable interpretation of what an event means and how we should respond to it. At times, however, we are faced with equivocal events. Equivocal events contain elements of shock and surprise: They defy straightforward applications of our existing knowledge structures.

In order to understand what such events mean and how we should respond to them, we engage in a process of sensemaking . Sensemaking models how people adjust their knowledge structures as they notice and interpret an equivocal event and coordinate a response to clarify what the event means (Holt & Cornelissen, 2014 ; Maitlis & Christianson, 2014 ; Sandberg & Tsoukas, 2015 ; Weick et al., 2005 ). It is for this reason that sensemaking is so important to organizational scholarship: If “organization” is a pattern of interlocking group behaviors, then sensemaking is the pattern formation process (Weick, 1979 ). And if equivocal events disrupt these patterns of interlocking group behaviors, then organization can only be restored through sensemaking. If sensemaking fails, organized groups can rapidly disintegrate into disorganized individuals who lack the capacity for action (Weick, 1993b ). In this view, organization only navigates the chaotic flux of perpetual change by the sense that it makes (Tsoukas & Chia, 2002 ). Thus, sensemaking functions not only as a process that organizational members engage in when responding to equivocal events, but also as a perspective on what organization is and how it is accomplished. Stated differently, sensemaking is both a method and object of inquiry in organizational contexts (Blatt, Christianson, Sutcliffe, & Rosenthal, 2006 ).

Article Overview

The purpose of this article is thus to provide an accessible introduction to sensemaking as it relates to organizations. It begins by contextualizing the sensemaking perspective within its historical milieu and delimiting its boundaries relative to rational decision-making and cognitive psychology perspectives. It then describes the sensemaking process by reviewing the types of events that trigger sensemaking, how actors notice and bracket information from such events, the means by which they interpret that information, the immediate products resulting from this interpretive process, the proximal and distal consequences of enacting interpretations, and the contextual factors that influence this overall sensemaking process. Taken as a whole, this article should provide a solid conceptual foundation for readers interested in the role that sensemaking plays in the field of organizational psychology.

Sensemaking as Perspective

Reductionist approach to organizations.

How is it that individuals organize and act collectively? It is worthwhile to begin by considering two very different schools of thought, both of which purport to answer this question. In reviewing the behavioral strategy literature, Powell, Lovallo, and Fox ( 2011 ) distinguish between the “reductionist” school on one end and the “contextualist” school on the other. The reductionist approach “relies on positivist, realist, and objectivist philosophies of science and favors quantitative hypothesis testing using methods such as mathematical modelling, simulation, and laboratory decision experiments” (Powell et al., 2011 , p. 1371).

This approach models individuals as decision-makers who face an objectively defined reality, are given ex ante options for how they can act, and choose actions based on a forward-looking process that maximizes their expected value (e.g., Edwards, 1954 ; Von Neumann & Morgenstern, 1947 ). It thus best describes an idealized hypothetical individual who makes decisions about a world that he or she knows with clarity. When work in this reductionist paradigm embraces more realistic psychological assumptions, it focuses on how actual human rationality is “bounded” in its ability to process all the available information and calculate the objectively best course of action. It therefore highlights the resulting “cognitive biases” that inhibit rational decision-making (Kahneman, 2003 ). It also prescribes ways for managers to improve their rationality while capitalizing on the irrationality of others (cf. Raiffa, 1982 ).

Contextualist Approach to Organizations

In contrast, the contextualist school—in which the sensemaking perspective plays a central role—describes a world in which “subjective beliefs, shared ideologies, and cognitive frames matter more than explicit ex ante decisions, which seldom correspond with what people or firms actually do” (Powell et al., 2011 , p. 1373). In doing so, this school suggests that organizational environments are not objectively defined, but are socially constructed. People are not passively given all available options for how they can act, but must actively search for or even improvise them. And when searching for options, people draw on past memories more than they imagine future outcomes. Through these memories, they generate plausible interpretations that enable tentative action rather than pursuing maximal accuracy and absolute certainty.

This approach clearly appreciates a central insight offered by cognitive psychology: people use their beliefs to interpret their circumstances (Fiske & Taylor, 1991 ). Yet, it also goes beyond this work in several ways (cf. Gilliland & Day, 1999 ). The first way is by noting the key role that people play in sculpting the very circumstances they later need to interpret. The second way is modeling how order at the higher level of a group can emerge even when the individual members that constitute the group lack a full mental understanding of it. The third way is how people’s sense of identity is tightly interwoven into the emergence and continuation of this higher-level order. And the fourth way is that plausibility rather than accuracy is the criterion. Indeed, if we undertake action to express our identity and sculpt our circumstances, we do not need perfectly accurate knowledge about our current situation before we can act meaningfully or produce order at the higher level.

As a result, sensemaking is not about cognitive biases (the extent to which a person’s knowledge reflects “reality”) or even shared cognition (the extent to which multiple people’s knowledge structures align or not). It is about collective cognition : how people come to think together, forming higher order patterns of interpretation and action that are fundamentally irreducible down to any one individual (Daft & Weick, 1984 ; Elsbach, Barr, & Hargadon, 2005 ; Sandelands & Stablein, 1987 ; Weick & Roberts, 1993 ). Given these defining characteristics, researchers within the contextualist school typically “conduct empirical work ‘in context,’ favoring qualitative and interpretive methods such as ethnography and textual analysis and rejecting positivism and quantitative hypothesis testing” (Powell et al., 2011 , p. 1373).

Building Blocks of the Sensemaking Perspective

Historically, the reductionist and decision-making approach ascended to prominence in organizational scholarship before the contextualist and sensemaking approach did. Throughout the 1950s and into the early 1960s, rationality assumptions were dominant in the field. By the late 1960s, however, there was both an appetite for a new way of thinking about human behavior and the conceptual building blocks to construct such a way of thinking. This appetite existed due to growing dissatisfaction with stimulus–response models of human behavior and increasing interest in existentialism. It called for greater emphasis on the active role that people play in sculpting their lives. The building blocks of the sensemaking perspective were also developed by this time and ready for assembly into a coherent perspective. These building blocks include the following:

As groups of people act, they take their collective patterns of action and interpretation for granted, seeing these patterns as part of a reality that exists independently of them and which would continue even without their participation in it (Berger & Luckmann, 1966 ).

People apply these taken-for-granted understandings to navigate their everyday activities. In doing so, people ask others to account for behavior that challenges their taken-for-granted understandings (Garfinkel, 1967 ).

Producing such “accounts” of behavior requires people to step outside the flow of their ongoing experience and retrospectively interpret their past behavior (Schutz, 1967 ).

People form these interpretations by rationalizing their past behavior, such that they reduce the unpleasant experience of cognitive dissonance (Festinger, 1957 ).

Formative Historical Context

These disparate ideas were first assembled into a coherent sensemaking perspective on organizations by Karl Weick in his 1969 text, The Social Psychology of Organizing . Weick ( 2003 ) later described the historical context in the late 1960s where these ideas, so radically distinct from the dominant rational decision perspectives, first emerged:

These ideas coincided with a growing societal realization that administrators in Washington were trying to justify committing more resources to a war in Vietnam that the United States was clearly losing. One could not escape the feeling that rationality had a demonstrable retrospective core, that people looked forward with anxiety and put the best face on it after the fact, and that the vaunted prospective skills of McNamara’s “whiz kids” in the Pentagon were a chimera. It was easy to put words to this mess. People create their own fate. Organizations enact their own environments. The point seemed obvious. (p. 186)

Here we can see that, far from being some arcane academic exercise, sensemaking came about precisely to explain messy and complicated real-world phenomena. Yet, at the time, these insights remained somewhat underappreciated. Rather, the 1970s saw the rise in three highly influential theories of organization: transaction cost economics (Williamson, 1975 ), population ecology (Hannan & Freeman, 1977 ), and resource dependence (Pfeffer & Salancik, 1978 ). As Kaplan ( 2011 ) rightfully noted, each of these theories “privileged position and situation” in objective terms—bypassing insights that managers interpret their positions and enact situations (p. 667). It was only when Weick substantially extended The Social Psychology of Organizing into what became the seminal second edition in 1979 that scholars took note. And these ideas, derived from Vietnam-era concerns, remain relevant even today. Indeed, The Social Psychology of Organizing continues to be well-cited for precisely the idea that sensemaking is a retrospective process by which organizations help create the very environments that perplex them (Anderson, 2006 ). These ideas have also influenced practitioners through their integral role in Peters and Waterman’s ( 1982 ) bestselling management book, In Search of Excellence (see Colville, Waterman, & Weick, 1999 ).

Consequences of Taking a Sensemaking Perspective

To understand the continued interest in the sensemaking perspective, we must consider the consequences for scholars who embrace it. When compared to its “reductionist” counterpart, the sensemaking perspective performs an important rhetorical function: it induces a figure-ground reversal between rationality and human behavior. Decision-making theories begin with rationality as their theoretical grounding, and thus assess human behavior in light of it. From this perspective, they find human behavior to be boundedly rational. The sensemaking perspective, however, begins with, and is grounded in, lived human behavior. By examining rationality in light of human behavior, it finds that rationality lacks explanatory power. In other words, while decision-making characterizes humans as “boundedly rational,” sensemaking characterizes rationality as a “boundedly relevant” way to explain human behavior. As such, sensemaking offers a rather distinct perspective on what organizations are, how they function, and how they should be studied.

This perspective has generative potential for organizational scholars precisely because it differs from commonly shared assumptions in the field. Namely, we typically theorize about “entities” connected by box and arrow diagrams (Whetten, 1989 ). The sensemaking perspective emphasizes how theoretical entities—like people, identities, interpretations, and environments—are not distinct from each other. Rather, they can only be defined in relation to each other. This emphasizes how processes unfold over time and encourages us to study relations rather than entities (cf. Bradbury & Lichtenstein, 2000 ; Langley, 2007 ). The sensemaking perspective thus “acts as a lens that . . . focuses our attention on agency because action is viewed as part of people’s efforts to make sense; on equivocality because sensemaking is triggered by people’s need to understand an equivocal flow of experience; and on relationships as sensemaking is social” (Blatt et al., 2006 , p. 898). Indeed, in his analysis of a friendly fire accident in Iraq, Snook ( 2000 ) compares a sensemaking perspective to that of decision-making. Adopting a sensemaking perspective, he suggests, enables scholars to maintain a richer understanding of how events come to be, portraying even terrible tragedies “as ‘good people struggling to make sense,’ rather than as ‘bad ones making poor decisions’” (pp. 206–207).

A Sensemaking Perspective on Environments

To understand the sensemaking perspective on organizations, we must first consider the nature of the environment in which organizations exist. We can begin with a helpful distinction between certainty, risk, and uncertainty. Certainty describes environments where actions “lead invariably to a specific outcome,” risk describes when actions lead to many possible outcomes that occur “with a known probability,” and uncertainty describes when these possible outcomes occur with “completely unknown” probabilities (Luce & Raiffa, 1957 , p. 13). To clarify, if you see that it is clearly raining outside, you are operating under conditions of certainty. This makes your decision to carry an umbrella unproblematic. More interesting is when you see looming clouds and must weigh the convenience of avoiding wet clothes in the case of rain with the inconvenience of needlessly holding an umbrella in the case of no rain. Here, the best advice is to move from a situation of uncertainty to one of risk: instead of estimating a subjective probability of rain, you can consult the official weather forecast to find the objective probability.

These environmental conditions of certainty, risk, and uncertainty are mutually exclusive. However, they are not exhaustive because of their implicit assumptions. Namely, they assume that more information inherently brings more clarity to environments. To make good decisions, organizations should therefore gather enough information to derive probabilities for outcomes that are as objective as possible (Galbraith, 1973 ; Thompson, 1967 ). To unsettle these implicit assumptions, sensemaking scholars make further distinctions about environmental conditions that foreground different properties of information. Namely, they distinguish ambiguity and equivocality from uncertainty (Weick, 1995 , pp. 91–100). Whereas uncertainty can be remedied by more information, ambiguity and equivocality cannot. The latter two conditions capture how any one piece of information can support many possible meanings. As such, gathering more information can actually make action harder rather than easier, as the number of possible meanings multiply as the amount of information increases. People must therefore reduce the possible meanings of existing information instead of adding more information (Weick, 1979 ).

Ambiguity and equivocality differ in one important regard. Ambiguity implies that there is some “true” state of the environment out there to be discovered. Equivocality suggests instead that the state of the environment must be invented: by focusing on certain pieces of information and acting on the basis of certain interpretations of that information, organizations help invent their environments. Now, organizations certainly vary in the degree to which their members treat environments as ambiguous or equivocal (Daft & Weick, 1984 ; Weick & Daft, 1983 ). But in either case, their members reduce the number of possible meanings through interpretation: the information they receive does not inherently bring clarity. And objective probabilities seldom exist because organizations influence the outcomes they seek to predict. Sensemaking scholars have elaborated the nature of uncertainty, ambiguity, and equivocality in several helpful ways (see Milliken, 1987 ; Smircich & Stubbart, 1985 ; Sutcliffe, 2001 ). For example, these distinctions shed light on important questions including how to balance knowledge and action (Colville, Brown, & Pye, 2012 ), respond to ethical issues (Sonenshein, 2007 ; Thiel, Bagdasarov, Harkrider, Johnson, & Mumford, 2012 ), and routinize processes (Becker & Knudsen, 2005 ).

A Sensemaking Perspective on Organizations

As sensemaking scholars emphasize how people interpret and sculpt their environments, some describe sensemaking as supporting an “interpretive” view of organizations rather than a “computational” view that is predicated on gathering and processing more information (Lant & Shapira, 2000 ). This brings us from a view of the environment to a view of the organization. Within a sensemaking perspective, it is often less helpful to talk about “organizations” than it is to talk about “organizing” (Gioia, 2006 ; Weick, 1969 ). Talking about an organization as though it was a single actor (e.g., “the company launched a new product” or “the government went to war”) is misleading. It minimizes the presence of multiple conflicting rationalities that exist among different groups within a single organization. It also gives the illusion of stability to what is actually an ongoing process that is always subject to disruption and therefore always in need of re-accomplishment. An organization is merely a snapshot at a single point in time of the consequences of an ongoing organizing process. Thus, taking organizing as the focus of research helps sensitize scholars to group-level processes that enable people to coordinate patterns of interlocking behaviors and respond to other groups with alternate rationalities.

Figure 1. Organizing as Enactment-Selection-Retention.

The organizing process occurs when changes in their environment prompt groups of organizational members to enter cycles of enactment, selection, and retention (Weick, 1979 ; Weick et al., 2005 ). This enactment-selection-retention process explicitly adopts the variation-selection-retention model described in natural selection and evolution, but as applied to social systems like organizations (Campbell, 1970 ). Because enactment-selection-retention collectively define what “organizing” means and how “organization” comes about, each of the three stages merits attention and explanation (see Figure 1 ):

Enactment refers to the ways in which organizational members do not merely re act to their environments, but help to en act it much in the same way that legislators enact laws. Enactment occurs both through perception and behavior. Perceptually, organizational members notice only limited portions of their environment. They notice information that is puzzling or problematic and then bracket that information so they can interpret what it means. Once they have a plausible interpretation at hand, enactment occurs again through behavior. Through behaviors taken through speech or with the body, organizational members act out their interpretations, thereby embedding their interpretations into their environment. Enactment thus reminds us how organizations “construct, rearrange, single out, and demolish many of the ‘objective’ features of their surroundings” as they engage with it through perception and behavior (Weick, 1979 , p. 164).

Selection refers to the interpretive process, in which organizational members work to determine what the bracketed information means. It is an equivocality reduction process: reducing the number of possible meanings allowed by the information until it becomes actionable. In essence, selection imposes knowledge structures that configure the bracketed pieces of information in different arrangements: making some variables more or less central and relating these variables in new ways. Selection is thus “both a cognitive process and a political process as organizational members struggle with the definition of the situation and the resulting choices consistent with the definition” (Ocasio, 2000 , p. 51). As we will see, various cognitive, discursive, and embodied means of interpretation can be used for selection. Either way, selection produces a plausible understanding of what the environment means that can serve as a guide for enactment.

Retention is a social process by which the selected interpretations become integrated into the group’s identity, interwoven into its narrative of the environment, and used as a reference to guide subsequent enactment and selection. Whereas selection describes how interpretations influence the information currently being processed, retention describes how current interpretations influence subsequent behavior. Importantly, for organizing to work, retained understandings must be not only believed, but also doubted (signified respectively by the + and – feedback loops in Figure 1 ). Belief means that we notice the same type of information we previously noted, interpret that information in similar ways, and enact that interpretation in similar ways. With unmitigated belief, however, the accumulation of retained experience will pose increasing constraints on organizing. Thus, we must infuse doubt into organizing by noticing new information, questioning our entrenched interpretations, or acting out our old interpretations in new ways.

Taken together, organizing can thus be seen as a process whereby groups of individuals reduce the equivocality in their environment through a series of interlocking behaviors; through these behaviors, the group notices and brackets information from the environment, applies knowledge structures to interpret that information, and then acts out their interpretations in ways that develop the group’s knowledge and bring order to its environment (Weick, 1979 ).

Although early work emphasized organizing and characterized “organization” as a term that adds more confusion than it does clarity (Weick, 1969 ), more recent perspectives see room for both terms—if properly understood. Namely, when people organize, they enact their retained interpretations to navigate moment-to-moment changes in their environment. As such, we can think of “organization” in two ways (see Tsoukas & Chia, 2002 ). Organization can refer to the retained interpretations that characterize groups of people: their beliefs, identities, etc. It can also refer to the moment-to-moment patterns of actions that apply these interpretations to the current environment. In neither case is the organization a place where people work. It is a result of people’s attempts to stabilize understandings of their environment (see Walsh & Ungson, 1991 ). In Wiley’s ( 1988 ) terminology, this directs us to the intersubjective and generically subjective levels of analysis. As opposed to the private interpretations of single individuals (intrasubjective level) or interpretations that exist with little need for personal involvement like mathematics (extrasubjective level), the intersubjective and generically subjective levels are socially negotiated. At the intersubjective level, fragile interpretations emerge from social interactions among specific people. At the generically subjective level, interpretations remain stable regardless of the people involved. The organizational form is unique in that it foregrounds the “bridging operations” between these two levels (Weick, 1995 , p. 75). Organizations must enable the innovation and vivid understandings that occur within groups at the intersubjective level. But they must also value the managerial control and ability to hire and fire people without losing substantial operational knowledge that is entailed by generic subjectivity.

As such, this sensemaking perspective on organizations is uniquely versatile. It can describe small groups, large corporations, or even entire options trading markets. Although some find the idea of producing objective knowledge about subjectivity to be “paradoxical” (Allard-Poesi, 2005 ) or guilty of “ontological oscillation” (Burrell & Morgan, 1979 , p. 266), such challenges may be less incisive than they seem at first. As Weick ( 1995 ) noted, “People who study sensemaking oscillate ontologically because that is what helps them understand the actions of people in everyday life who could care less about ontology” (p. 35). People who interact within organizational forms necessarily treat the generically subjective as objective—until challenges, contradictions, and breakdowns in meaning require innovative new intersubjective understandings. This oscillation is not a flaw of sensemaking research, but a crucial feature.

Sensemaking as Process

Properties of the sensemaking process.

As organizational scholarship grew increasingly interested in cognition throughout the 1980s and early 1990s (Kaplan, 2011 ; Walsh, 1995 ), a body of work began exploring the sensemaking process in organizational contexts (e.g., Louis, 1980 ; Porac, Thomas, & Baden-Fuller, 1989 ; Starbuck & Milliken, 1988 ; Thomas, Clark, & Gioia, 1993 ). And as the 1990s progressed, scholars—especially in Europe—turned toward language and discourse (see Alvesson & Karreman, 2000 ; Czarniawska, 1998 ). As Maitlis and Christianson suggest ( 2014 ), an impetus behind the growth of sensemaking research in the 1990s was Weick’s ( 1995 ) Sensemaking in Organizations . This book documented the state of the research at this important transitional time, while also providing some structure and direction (see Manning, 1997 ).

In it, Weick identified seven properties of the sensemaking process. Although the literature proliferated and the theorizing has matured since this text, these seven properties remain influential in guiding how scholars understand the sensemaking process (cf. Helms Mills, Thurlow, & Mills, 2010 ). For this reason, we consider each of the seven sensemaking properties briefly as an informative introduction to the sensemaking process. After, we will delve deeper into the nuances of sensemaking as they exist in the current literature.

Sensemaking is:

Grounded in identity construction because in responding to equivocal events, individuals and groups must determine who they are now in relation to a suddenly strange environment and who they will become as they start trying to change the environment.

Retrospective in nature because disruptions prompt individuals to turn their attention to information from the past in order to interpret how the current disruption came about.

Based on enacting sensible environments because a key output of sensemaking is an enacted environment that is more orderly than the equivocal environment that triggered sensemaking in the first place.

Social in that interpretations are negotiated and enacted through social interactions.

An ongoing process because sense is never made in perpetuity, but is always subject to disruption and therefore in need of re-accomplishment.

Focused on cues extracted from the environment because informational cues containing equivocality provide the raw material for interpretation.

Driven by plausibility rather than accuracy because sensemaking helps people reach only enough clarity to coordinate action, not to maximize expected outcomes with certainty.

Sensemaking Within the Context of Organizing

These seven properties can help guide our growing understanding of the sensemaking process. As a first step, we can relate sensemaking back to the overall project of organizing. Sensemaking captures the way organizing proceeds when the environment suddenly becomes more equivocal or ambiguous. Thus, whereas organizing is a process that is always operative, sensemaking is typically described in terms of particular episodes triggered by unexpected events that infuse equivocality into the environment (Sandberg & Tsoukas, 2015 ).

Without denying that the many small, routine events that demand little attention and characterize much of organizational life also require interpretation and influence action (Gioia & Mehra, 1996 ), sensemaking typically describes the process by which interpretation and action are shaped by rarer and more unexpected events. As such, we can consider sensemaking to be a special case of organizing: organizing in response to the unexpected. The value in making this distinction is that organizing operates in a somewhat different manner when members are faced with unexpected events and equivocal inputs. The stakes of organizing in these cases are also certainly higher, lending a certain salience and intrigue to sensemaking episodes.

Thus, it is valuable to note how sensemaking occurs through same enactment-selection-retention process as does organizing more generally. We can therefore link the organizing process with each of the aforementioned seven sensemaking principles from (1) to (7).

Enactment describes the perceptual process of noticing and bracketing information from the environment (6: focused on cues) and the behavioral process by which acting on the basis of interpretations helps shape the world and bring order to it (3: enacting sensible environments).

Selection describes how people draw on the past to interpret bracketed information (2: retrospective in nature) and seek to find workable interpretations rather than completely accurate ones (7: driven by plausibility).

Retention describes how the outputs of interpretation are stored in ways that affect individual and collective identities (1: grounded in identity), and how these outputs are negotiated through interactions with others (4: social) as they are applied continuously to the environment as its equivocality levels constantly fluctuate (5: ongoing).

Many have characterized this relationship between sensemaking and organizing in similar ways. For example, Sandberg and Tsoukas ( 2015 ) explain:

organizing is a process in which individuals interactively undertake action (enactment), the results of which they subsequently confront as their “environment,” which they then seek to make sense of by retrospectively chopping their lived experiences into meaningful chunks, labeling them, and connecting them (i.e., selection). This sense made is retained in their minds in the form of cognitive ‘cause maps,’ indicating what is crucial for carrying out their tasks and how they are interconnected (retention). Through sustained interaction, individuals interlock their behaviors over time, and, in so doing, they deal with residual equivocality, which they seek to remove through negotiating a consensus about their common task and how it ought to be handled. Thus, a group of individuals become organized when their cause maps converge (Weick, 1979 ). In other words, sensemaking is homologous to organizing: The latter is achieved to the extent that the former is accomplished. (p. 8)

Similarly, others suggest that “people organize to make sense of equivocal inputs and enact this sense back into the world to make that world more orderly” (Weick et al., 2005 , p. 410). Alternatively, “to make sense is to organize, and sensemaking refers to processes of organizing using the technology of language . . . to identify, regularize and routinize memories into plausible explanations” (Brown, Stacey, & Nandhakumar, 2008 , p. 1055). With this key relation in place, we can turn to sensemaking in greater detail by reviewing its components.

Triggers of Sensemaking

In differentiating sensemaking from organizing in general, the initial question arises: What types of events prompt sensemaking in the first place? By definition, these are events that contain higher degrees of equivocality. However, it is also worthwhile to consider broader taxonomies of such events that occur in organizations (e.g., Maitlis & Christianson, 2014 ; Sandberg & Tsoukas, 2015 ). Triggering events can vary in how equivocal and thus disruptive they are (mild to extreme), where they originate (inside or outside the organization), and the degree to which they are planned or unexpected. For example, events can be completely unexpected and occur for reasons largely external to the organization, as when the roof of a railroad museum collapsed due to inclement weather, prompting the museum to reassess its strategy (Christianson, Farkas, Sutcliffe, & Weick, 2009 ). In other cases, triggering events stem from broader social phenomena, such as when university administrators faced demographic trends of diminishing of 18–22 year olds enrolling in college (Milliken, 1990 ).

At other times, triggering events can stem more directly from feedback to organizational behavior. This occurs when employees of a retail store notice disappointed or apathetic customer reactions to their change initiative (Sonenshein, 2009 ) or when customers adapt an organization’s technology product in new and unexpected ways (Griffith, 1999 ). Sensemaking is also triggered by the collapse of organizations, and plays an important role in guiding how employees respond to the news (Walsh & Bartunek, 2011 ). Indeed, sensemaking can be triggered by events entirely due to past organizational behaviors and interpretations. For example, a group of firefighters assumed they were facing a routine fire that would be contained by 10 o’clock the following morning, only to face an entirely different type of fire (Weick, 1993b ). For them, the triggering event came entirely from their expectations, not the fire itself, which could have been handled if they faced it with a different set of interpretations.

Sensemaking can also be triggered more intentionally in organizations (see Barnett & Pratt, 2000 ). For instance, art initiatives in organizations can be intentionally utilized to this end (Barry & Meisiek, 2010 ). Perhaps the most common intentional sensemaking trigger stems from the actions of other people. For example, Pratt ( 2000b ) described how, in the process of being socialized into a multilevel marketing organization, individuals underwent a “sensebreaking” process that disrupted how they understood their current identity. This, in turn, allowed for the organization to more deeply influence individuals as they subsequently made sense through socialization into the organization. Similarly, a large body of work focuses on the process of sensegiving (e.g., Fiss & Zajac, 2006 ; Gioia & Chittipeddi, 1991 ; Hill & Levenhagen, 1995 ; Maitlis & Lawrence, 2007 ). Sensegiving describes “the process of attempting to influence the sensemaking and meaning construction of others toward a preferred redefinition of organizational reality” (Gioia & Chittipeddi, 1991 , p. 442). Leaders often engage in sensegiving during periods of strategic planned change or crises to ensure that organizing does not unravel, but instead progresses toward their envisioned outcome (e.g., Christianson et al., 2009 ).

Noticing and Bracketing

When existing patterns of organizing adequately control the equivocality levels in the environment, individuals find themselves immersed in a flowing stream of experience (Schutz, 1967 ). Sensemaking begins when information cues from the environment disrupt this flow, requiring individuals to interpret the nature and meaning of this information. When sensemaking scholars talk about “noticing and bracketing,” they are describing how information can prompt individuals to step out of the ongoing flow. Foundational research identified characteristics of informational cues that are more likely be noticed within the flow of experience. For example, as social psychology research has noted, information is more likely to be noticed when it is novel, intense, and unpleasant (Fiske & Taylor, 1991 ). It is important to note, however, that not all information that is novel, intense, or unpleasant is also relevant to organizing. Information gains relevance to organizing based on how it compares against expectations (Weick, 1995 ).

Organizing implies certain retained expectations about the environment, and information that confronts those expectations is most likely to be bracketed for interpretive work. For this reason, noticed information becomes bracketed based on its relevance to organizing. Information is bracketed for interpretation when it makes the meaning of their environment unclear, indicates that current actions are failing, or enables no obvious set of behavioral responses (e.g., Ashforth & Fried, 1988 ; Kiesler & Sproull, 1982 ; Starbuck & Milliken, 1988 ; Weiss & Ilgen, 1986 ). These ideas about bracketing align with managerial problem-solving approaches that see problems not as defined inherently, but as constructed based on context and expectations (Smith, 1988 ). Therefore, while novel and intense information is especially likely to be noticed, novel and intense information that makes the environment more equivocal will not only be noticed, but will also be bracketed for interpretive work—thus sparking the sensemaking process.

Interpretive Processes: Cognitive Means

Once information is noticed and bracketed for interpretation, how precisely does the interpretive process occur? As the sensemaking literature has developed, scholarly interest has shifted sequentially from cognitive to discursive to embodied means of interpretation. Early work in sensemaking focused on cognitive means of sensemaking such as frames, cause maps, and schemata (e.g., Bartunek, 1984 ; Bougon, Weick, & Binkhorst, 1977 ; Starbuck & Milliken, 1988 ; Weick, 1969 ). These generally synonymous terms refer to relatively stable knowledge structures that individuals can apply to interpret ongoing events (Walsh, 1995 ). Doing so imposes “a structure of assumptions, rules, and boundaries that guide sensemaking and over time become embedded and taken-for-granted” (Lüscher & Lewis, 2008 , p. 222).

This work focused on how informational cues that individuals notice and bracket from the environment and cognitive frames retained in their memory jointly enable interpretation. As Weick ( 1995 ) describes, “Frames tend to be past moments of socialization and cues tend to be present moments of experience. If a person can construct a relation between these two moments, meaning is created. This means that the content of sensemaking is to be found in the frames and categories that summarize past experience, in the cues and labels that snare specifics of present experience, and in the ways these two settings of experience are connected” (p. 111). A similar, but distinct concept to frames is the script, which is a “cognitive structure that specifies a typical sequence of occurrences in a given situation, such as an employment interview or formal meeting” (Ashforth & Fried, 1988 , p. 306). Unlike frames, which clarify the present moment of experience, scripts also set expectations that guide how actions should proceed over time (Gioia, 1986 ; Weber & Glynn, 2006 ).

Although these knowledge structures can be shared across organizational members to varying degrees, their character is mostly cognitive and individual. They primarily reside passively in the mind of individuals, rather than actively in socially vocalized speech or physical body movements (for a review of frames both within and beyond sensemaking, see Cornelissen & Werner, 2014 ). It is certainly true that cognitive means of sensemaking have become somewhat less prevalent than the subsequent discursive and embodied approaches, which we will soon explore. Nonetheless, cognitive means of interpretation should not be discounted. For instance, the data-frame theory of sensemaking describes expert behavior with remarkable precision (Klein, Moon, & Hoffman, 2006a , 2006b ; Klein, Phillips, Rall, & Peluso, 2007 ; Klein, Wiggins, & Dominguez, 2010 ). Contrary to decision-making models, this naturalistic approach notes how experts do not typically evaluate multiple options. Instead, experts draw on diagnostic cues in the environment to invoke a single best-fitting frame, which they then mentally simulate in order to assess fit between the cues and frame. This process describes, for instance, how fireground commanders quickly recognize the appropriate routines to handle a particular fire. In this way, cognitive means of sensemaking can still be profitably investigated within particular contexts.

Although not discussed as frequently in the sensemaking literature, another such context concerns formulas like the Black–Scholes formula mentioned earlier (MacKenzie & Millo, 2003 ). Formulas, algorithms, and formal models are also tools of interpretation (cf. Boland, 1984 ; Gephart, 1997 ). After all, formulas specify what variables we should extract from the environment, how these variables relate to each other, and what outcomes are meaningful. Such formulas are likely to become especially important as they underlie “the Internet of things,” whereby aspects of our everyday life are increasingly quantified, networked, and optimized. Formulas also matter because of what they exclude. For example, in advocating for considering stakeholders in addition to shareholders, Freeman ( 1994 ) argued that most formulas quantifying organizational success exclude ethical consequences on third parties.

Interpretive Processes: Discursive Means

The discussion of shareholders and stakeholders attunes us to another important means of interpretation: language. Indeed, sensemaking research in the 1990s and beyond increasingly shifted away from cognition and toward language. Weick ( 1995 ) provided an early example of the power of language and verbal labels in interpretation: battered child syndrome. Pediatricians had long puzzled over children whose observed injuries were discrepant from their reported medical histories. Only after creating the label “battered child syndrome” could they interpret this odd phenomenon: The observed injuries differed from reported medical histories precisely because the parents of the children were both inflicting the injuries and misreporting their medical histories (Westrum, 1982 ). In this way, verbal labels provide an important means of interpretation (Weick et al., 2005 ). Similarly, it becomes easier to advocate for strategies that benefit “stakeholders” rather than mere “shareholders” once we have a label to describe the otherwise diffuse grouping of people impacted by organizational practices.

In addition to labels, a good deal of discursive interpretive work also occurs through metaphors (e.g., Cornelissen, 2005 ; Cornelissen, Holt, & Zundel, 2011 ; Gioia, 1986 ; Patriotta & Brown, 2011 ). Perhaps one reason metaphors are especially useful for sensemaking is that they are “incomplete statements of one thing—in terms of another” (Hill & Levenhagen, 1995 , p. 1062). As a result, they help reduce equivocality to a limited degree, but still allow for paradox, incongruity, and contradiction. This makes them especially useful as means to foreground some pieces of information over others and connect novel aspects of ongoing events to existing knowledge structures in creative ways. Yet, metaphors do not merely serve to link ongoing events to knowledge structures. They also serve to socially justify certain actions over others (Cornelissen, 2012 ). For example, by invoking the metaphor of a “learning curve,” a hospital interpreted their poor performance in ways that legitimized it (Weick & Sutcliffe, 2003 ).

Building from the simpler discursive tools of labels and metaphors, interpretation also occurs through more complex accounts and narratives. Accounts and narratives differ from cognitive means of interpretation (frames, schemata, cause maps) most crucially in that they are fundamentally discursive: based in language (e.g., labels, metaphors). Therefore, by focusing on accounts and narratives, we are made to notice how events are translated into language, how that language selection is socially negotiated, and how organization is thus talked into existence. Instead of residing passively in mental knowledge structures, accounts and narratives live socially in acts of speech. This has the added benefit of making them more observable, including through secondary sources. In contrast, cognitive means of interpretation are most reliably elicited through interviews (e.g., Bougon et al., 1977 ; Weber & Manning, 2001 ).

An account is a “situation that is comprehended explicitly in words and serves as a springboard to action” (Taylor & Van Every, 2000 , p. 40). As “discursive constructions of reality,” accounts “provide members with ordered representations of previously unordered external cues . . . [that] describe or explain the world and thus make it meaningful” (Maitlis, 2005 , p. 23). The underlying idea of “accounting for” behavior stems from ethnomethodology (Garfinkel, 1967 ; Heritage, 1984 ). On the other hand, narratives stem from a wide multiplicity of scholarly traditions (e.g., Bakhtin, 1981 ; Ricœur, 1984 ). Narratives are “a discursive construction that actors use as a tool to shape their own understanding (sensemaking), as a tool to influence others’ understandings (sensegiving), and as an outcome of the collective construction of meaning” (Sonenshein, 2010 , p. 480). The crucial distinction between narratives and accounts are that narratives are explicitly temporal: Narratives make sense by describing how events have proceeded over time to produce the triggering event. Narratives thus have at least three components: “an original state of affairs, an action or an event, and the consequent state of affairs” (Czarniawska, 1998 , p. 2). Given this temporal component, narratives can serve to support both stability and change in organizations (Vaara, Sonenshein, & Boje, 2016 ).

From postructuralist and postmodernist perspectives, narratives differ from stories in a key way. Narratives need not have “coherent storylines, shared meaning, and common values” but can entertain multiple, even contradictory meanings (Cunliffe, Luhman, & Boje, 2004 , p. 264). Further, some of these meanings are favored while others are suppressed. This brings questions of power and privilege into the picture. Indeed, asking how some meanings gain favor over others can help link sensemaking with more critical approaches (Brown, 2000 ; Helms Mills et al., 2010 ; Mumby, 1987 ). It is evident that powerful individuals play an important role in shaping narratives. This role is well exemplified in how a central bank’s chairman listened to several competing narratives about economic recovery in a meeting and decided which of these narratives would actually be enacted publicly (Abolafia, 2010 ). It is also evident in the way that leaders narrate their own personal stories in ways intended to grant them social legitimacy (Maclean, Harvey, & Chia, 2012 ). Similarly, but at a higher level of analysis, organizations that foster a strong “official narrative” invariably relegate other narratives to a secondary position—as exemplified by analyses of the Walt Disney company (e.g., Boje, 1995 ; Van Maanen, 1991 ).

Taken as a whole, discursive means of interpretation therefore add important value beyond cognitive means (Cooren, 2000 ; Taylor & Van Every, 2000 ). Discursive interpretations are neither an intrasubjective matter of an individual’s cognition, nor are they necessarily an extrasubjective matter of taken-for-granted reality. Rather, they highlight how groups first establish their intersubjective understandings and then make these understandings generic over time through discourse (e.g., Schall, 1983 ). Sensemaking research has accordingly explored these discursive processes in a variety of contexts: fire fighters, intensive care units, blue collar work, hostage situations, and monetary policy groups (e.g., Abolafia, 2010 ; Albolino, Cook, & O’Connor, 2007 ; Baran & Scott, 2010 ; Mills, 2002 ; Quinn & Worline, 2008 ).

This raises a further question: How should discourse best proceed if it is to facilitate sensemaking? Some highlight specific techniques, which balance members voicing their ideas, needs, and attitudes as well as respectfully questioning the ideas of others (Blatt et al., 2006 ; Wright & Manning, 2004 ; Wright, Manning, Farmer, & Gilbreath, 2000 ). As we will shortly see, although not focused entirely on discourse, the ideas of mindfulness and “heedful interrelating” (Weick & Roberts, 1993 ) also provide valuable insights into how groups form intersubjectivity (e.g., Bijlsma-Frankema, de Jong, & van de Bunt, 2008 ; Jordan & Daniel, 2010 ; Stephens & Lyddy, 2016 ). Others highlight the types of situations under which discursive means of interpretation are most likely to flourish (Browning & Boudès, 2005 ; Rouleau & Balogun, 2011 ). Nonetheless, we should not assume that any single technique or situation will fit all possible sensemaking contexts. For instance, depending on the context, there is certainly room for strategically ambiguous communications, using language that is intentionally decoupled from actual business practices, using language to revise understandings of prior events rather than changing practices, or formalizing language into written contracts and rules (Eisenberg, 1984 ; Fiss & Zajac, 2006 ; Gioia, Corley, & Fabbri, 2002 ; Vlaar, Van den Bosch, & Volberda, 2006 ).

From a discursive perspective, organizations can thus be seen as a “storytelling system” (see Boje, 1991 ) or even a “text” (Westwood & Linstead, 2001 ). Yet, such perspectives are open to a critique, namely, that “organizations may emerge through conversation, but they do not emerge for the sake of conversation” (Engeström, 1999 , p. 170). Sensemaking intends to answer the latter question: modeling not just how organizations emerge, but also why. Thus, while some describe sensemaking and discourse as equal members of interpretive organizational research (Morgan, Frost, & Pondy, 1983 ), sensemaking scholars typically disagree. They note how sensemaking differs from interpretation: Sensemaking emphasizes how organizations do not merely interpret their environments, but instead help create them through perception and action (Sutcliffe, 2014 ; Weick, 1995 ). Thus, discourse has a subsidiary place within sensemaking: It is an important means of interpretation, but cannot be considered aside from the information that people notice and the actions they take on their environment (cf. Taylor & Robichaud, 2004 ). And it is only in understanding how environments are enacted that we can start to understand why organizations emerge.

Interpretive Processes: Embodied Means

Thus, even this discursive perspective has its limits. Through it, we may inadvertently start to “portray sensemaking as more cerebral, more passive, more abstract than it typically is . . . [because sensemaking] starts with immediate actions, local context, and concrete cues” (Weick et al., 2005 , p. 412). Indeed, there can be a tendency to forget immediate actions in favor of the more cerebral use of language over time. And, as noted, discourse research has not always made the connection between language, action, and environments (Taylor & Robichaud, 2004 ). For instance, although the technique is not frequently used in sensemaking research, agent-based models can serve as a helpful reminder about the power of action (cf. Gavetti & Levinthal, 2000 ; Gavetti & Warglien, 2015 ; Rudolph, Morrison, & Carroll, 2009 ; Rudolph & Repenning, 2002 ). In such models, we see that organization can emerge from individuals with remarkably simple interpretations (see Nowak & Vallacher, 2007 ). Physical action can be a means of interpretation given the central importance of retrospect: We make sense by looking back on what we have done. It is through physical action that we often identify our interpretations. And, as institutional theory perspectives highlight (Weber & Glynn, 2006 ), even seemingly unthinking and mundane actions also carry interpretive weight. Our rooting in a larger “lifeworld” provides such everyday actions with taken-for-granted meanings (Wright & Manning, 2004 ).

An important aspect of physical action is the role of the body. Indeed, the literature on sensemaking is puzzlingly rather mute on the role of the physical senses as an interpretive tool (Maitlis & Sonenshein, 2010 ; Sandberg & Tsoukas, 2015 ). This trend is reversing, as work continues to explore how sensemaking occurs in and through the body (Cornelissen, Mantere, & Vaara, 2014 ; Cunliffe & Coupland, 2012 ; Harquail & King, 2010 ). For example, physiological states, gestures, and body posture are inextricably interwoven into the verbal narratives that individuals use to interpret events (Cunliffe & Coupland, 2012 ). Our interpretations are encoded in our movements—and shared with others through our embodied actions (Whiteman & Cooper, 2011 ). What is less fully appreciated is that embodied emotional states are not merely influences on interpretation, but are interpretations themselves (see Myers, 2007 ). An embodied state contains a map of what the event is, who the relevant social actors are, and what generally might be done to address the event (see Averill, 1983 ). In this way, embodied action and emotion not only influence interpretation, but are a means by which interpretation occurs. Furthermore, an embodied approach dovetails nicely with phenomenological approaches to sensemaking and organizing (Chia & Holt, 2006 ; Guiette & Vandenbempt, 2016 ; Yanow & Tsoukas, 2009 ). Such approaches, which draw most heavily from Heidegger (Dreyfus, 1991 ), help us attend to the actual firsthand embodied experience of breakdowns in meaning and recovery from them.

Immediate Products of Interpretation

What exactly is produced by interpretation? We may use frames, narratives, or gestures during the selection process, but what exactly do they produce? As Elsbach, Barr, and Hargadon ( 2005 ) argued, scholars have historically given inadequate attention to the temporary outcomes of the interpretive process. Instead, much of the scholarly attention highlights the more stable knowledge structures that are retained as the result of enactment. Yet, the momentary and fleeting understandings that stem from selection are the key drivers of enactment. Weick and colleagues ( 2005 ) describe these interpretive products as answers to two questions: “What’s the story here?” and “Now what should I do?” (p. 410). In other words, individuals and groups first draw on retrospective processes to understand what the disruptive event means and then use this understanding to turn an eye more prospectively toward action in the present moment.

Similarly, Elsbach and colleagues ( 2005 ) delimited four products of the interpretive process as identified in the literature. Considering these interpretive products is valuable because they provide some structure to the transient but influential products that come from selection and guide enactment. These four interpretive products describe the following:

Our understanding of the problem at hand . This interpretive product answers the question, “what’s the story here?” whereas the remaining three answer “now what should I do?” Problem understandings concern the variables of interest that we bracket from ongoing events and the relations we propose among them. For example, a workgroup geographically dispersed into two separate locations formed two separate understandings of their joint task, thereby forestalling their ability to decide on an action moving forward (Cramton, 2001 ). Until a group comes to a plausible shared understanding of “the story,” the second question of “what to do next” becomes fundamentally unworkable.

The attractiveness of various options . Option attractiveness refers to the value people associate with a particular path forward. For example, two sets of research teams developing cochlear ear implants responded to different cues from institutional forces like government agencies and business partners. As a result, they differentially valued the attractiveness of starting with a simpler initial model and adding complexity relative to beginning with a more complex initial model (Garud & Rappa, 1994 ). The cues available to them and the way those cues were interpreted guided the attractiveness of various options for enactment.

The features about ourselves we perceive as being distinctive . These perceptions capture the elements of our identity such as our skills, traits, or expertise that are especially salient in the current situation. For example, in a crucial moment after a fire grew uncontainable, several firefighters failed to drop their heavy tools and run from the fire (Weick, 1993b ). Within the context of their interpreted environment, it was unfathomable to drop the very tools that constituted their distinct identity as firefighters. To drop their tools would be to enact a rather different identity. This shows how interpretations influence identity, and thus the kinds of behaviors that are available for enactment.

Our receptivity to a collectivist mindset . The interpretive process can open individuals up to greater degrees of a collectivist mindset in which knowledge is pooled and integrated. For example, in trying to interpret what a particular business opportunity means, some firms generate cultures that increase a collectivist mindset. This mindset taps more broadly into organizational memory to ultimately influence the way these business opportunities are interpreted (Sutton & Hargadon, 1996 ). Based on how we interpret our situations, we may be more or less open to these collective modes of thinking.

Proximal Consequences of Enactment

Together, these four interpretive products set the stage for enactment. In enactment, interpretation leads to some type of action, be it verbal or embodied. For this reason, we must “designate binding action as the object of sensemaking” (Weick, 1993a , p. 17). In other words, cognitive, discursive, or embodied interpretations only shape reality through action that binds. By enacting an interpretation, we produce two important and related consequences. First, we grow more committed to the interpretations we enact. And, second, we produce new changes in the environment that either bring the environment closer in line with the interpretation or violate the expectations embedded in our interpretations, and thus trigger a new round of sensemaking.

In regard to the first consequence, enacted interpretations are especially commitment producing when they are the result of a volitional choice that is public and hard to reverse (Salancik, 1977 ; Weick, 1993a ). In regard to the second consequence, a key question becomes: How will groups respond to violations of their enacted interpretations? At times, groups are able to “query an initial frame and . . . mobilize instead an alternative frame from background knowledge or make novel associations as a way of structuring expectations and make inferences” (Cornelissen et al., 2014 , p. 703). This phenomenon is sometimes referred to as “adaptive sensemaking” (Cornelissen et al., 2014 ; Strike & Rerup, 2015 ; Weick et al., 2005 ). Adaptive sensemaking shows how violations produced by enactment can prompt groups to rethink their commitment to an interpretation. Adaptive sensemaking is evident in the example of a railroad museum facing a roof collapse (Christianson et al., 2009 ). After the roof collapsed, the museum changed how they framed their organization from one of a historical repository to an attraction, thereby enabling new strategic initiatives and greater involvement from the community.

In many cases, however, commitment to continued enactment outweighs considerations of alternatives. Such “staunch commitment to a particular set of meanings” can be especially problematic in crisis situations because it “creates substantial blind spots that impede adaptation” (Maitlis & Sonenshein, 2010 , p. 562). We can see this effect in firefighters who perished after enacting a “10 o’clock fire” label—that the fire will be contained by 10 a.m. the following morning—despite evidence to the contrary (Weick, 1993b ). Similarly, with high shared levels of nervousness and fear, an anti-terrorist police force committed to enact their interpretation of an innocent citizen as a terrorist (Cornelissen et al., 2014 ). Although there were opportunities for doubt and reflection, their strong commitment led them to ultimately kill the wrong person.

These groups remained committed to enacting their interpretation despite violations of their interpretations. In some cases, however, the violations of expectations may arrive too late to reverse the course of enactment. For example, consider the fatal actions of NASA in launching the Challenger and Columbia shuttles. Employees frequently used labels like “acceptable risk” or metaphors like “in-family” to “waive” security concerns (Dunbar & Garud, 2009 ; Vaughan, 1996 ). In the process, employees grew committed to the environment as they enacted it. Only in retrospect—after these shuttles exploded in a harsh violation of their enacted environment—were these cues interpreted in alternative ways.

In sum, we can see that there is “a delicate tradeoff between dangerous action which produces understanding and safe inaction which produces confusion” (Weick, 1988 , p. 305). The tradeoff is that enactment helps us understand past events, but also creates new future events. Thus, there is value in considering what enables some groups to skillfully notice, bracket, and interpret information, and to manage the delicate tradeoff as they enact their interpretations.

Mindful Organizing as Effective Sensemaking

Indeed, as these examples reveal, not all groups are equally effective at sensemaking (e.g., Blatt et al., 2006 ; Winch & Maytorena, 2009 ). Some of the most important differences can be captured through the idea of mindfulness : the ability of groups to sustain attention toward and interpret ongoing events in a manner that captures enough discriminatory detail to act with speed and flexibility (Levinthal & Rerup, 2006 ; Weick & Sutcliffe, 2006 , 2015 ; Weick, Sutcliffe, & Obstfeld, 1999 ). Mindfulness has an important basis in the attentional and interpretive capabilities of individual members, including those described by meditative practices (Kudesia & Nyima, 2015 ; Reb & Atkins, 2015 ; Weick & Putnam, 2006 ). However, it can also be nurtured by more enduring mechanisms such as HR policies, leader modeling, and organizational climate (Ray, Baker, & Plowman, 2011 ; Vogus & Sutcliffe, 2012 ; Vogus & Welbourne, 2003 ). As shown in Figure 2 , mindful organizing describes five processes that make sensemaking more adaptive.

Figure 2. Processes of Mindful Organizing.

Groups that organize mindfully do a better job of anticipating potential disruptive events. This is because they constantly update their shared understanding of real-time events by pooling information and expertise across all members instead of relying on assumptions derived from past experience. As such, they are better able to notice and respond to minor disruptions in their environment before these disruptions cascade into full-blown crises. In this way, mindfulness captures a “quality of organizational attention” that prevents the normalizing away of important information and thereby enables more effective sensemaking (Weick & Sutcliffe, 2006 ). Mindfulness also shapes how groups respond to violations of their expectations (Levinthal & Rerup, 2006 ; Rerup & Levinthal, 2013 ; Weick & Sutcliffe, 2015 ). Groups that organize mindfully respond to these violations by adopting a resilient mindset that flexibly adapts past knowledge and by empowering the group members who have relevant expertise, rather than members with the most formal power. Mindfulness can therefore hedge the tendency to enact interpretations in an unthinking manner (Fiol & O’Connor, 2003 ). It helps groups anticipate triggering events and contain these events when they arise through skillful enactment. This is one reason why mindful organizing is especially prevalent in high-reliability organizations, where sensemaking failures can prompt disasters (Weick & Sutcliffe, 2015 ). Nonetheless, mindful organizing is relevant across a number of organizational contexts, including investment banks (Michel, 2007 ), management and design consultants (Hargadon & Bechky, 2006 ), information technology (Swanson & Ramiller, 2004 ), and others (see Sutcliffe, Vogus, & Dane, 2016 ).

Distal Consequences of Enactment

Why might an organization care to support mindful organizing? What are the big picture benefits of sensemaking? The answer is that enactment produces important distal consequences in terms of how an organization changes, learns, and negotiates its identity (for a comprehensive review, see Maitlis & Christianson, 2014 ). This is evident in how a railroad museum not only responded to the crisis of its roof collapsing, but turned this roof collapse into an opportunity to develop a new and more effective business strategy (Christianson et al., 2009 ). It is evident in how Air Force pilots who faced and effectively made sense of dangerous circumstances shared their stories with other pilots, prompting vicarious learning throughout the organization (Catino & Patriotta, 2013 ). It is evident in how sensemaking after an organization collapsed prompted members to pool their resources and found a new organization (Walsh & Bartunek, 2011 ). And a number of quantitative studies have also explored how various aspects of the sensemaking process influence overall firm financial performance (e.g., Daft, Sormunen, & Parks, 1988 ; Osborne, Stubbart, & Ramaprasad, 2001 ; Thomas et al., 1993 ).

Enactment, however, does not simply influence single organizations, but entire industries. For example, consider Porac, Thomas, and Baden-Fuller’s ( 1989 ) seminal analysis of the Scottish knitwear industry. They found that managers across knitwear firms had shared cognitive frames in how they categorized various subsets of textiles. Given these shared frames, most managers interpreted only other Scottish knitwear firms as their real competitors. They enacted these interpretations through the strategies and pricing, their supplier and distributor relationships, and so forth. Despite other companies producing similar goods at similar prices, these managers essentially enacted a “cognitive oligopoly” that shaped the behavior of an entire market. In this way, enactment completes the sensemaking process. Enactment produces an environment that may appear objectively true, and existing independent of our own actions (see Kaplan, 2011 ). However, our environment is really constructed through our actions and the actions of those with whom we are interlocked.

Contextual Influences on Sensemaking

As the Scottish knitwear example illustrates, the sensemaking process links individuals, groups, organizations, and broader institutional forces into interlocking patterns of mutual action and understanding. Sensemaking simultaneously operates at the level of ongoing environmental changes, social interactions within an organization, and the broader institutional context (e.g., Jensen, Kjærgaard, & Svejvig, 2009 ; Jeong & Brower, 2008 ). It also draws on cultural values that help define what properties make accounts sensible and to whom one should be accountable (e.g., Malsch, Tremblay, & Gendron, 2012 ; O’Leary & Chia, 2007 ). Nonetheless, much of the relevant research focuses on single levels without exploring cross-level processes (for a notable exception, see Stigliani & Ravasi, 2012 ). It is therefore helpful to outline some of the contextual, multiparty, and cross-level phenomena that affect sensemaking. For instance, individuals certainly have strong moral intuitions—but collective factors like social pressures and interaction partners shape how individuals define, communicate, and act upon ethical issues (Sonenshein, 2007 ). In this way, collectives can influence how individuals make sense of their environments in a top-down manner. The reverse process also occurs. For example, Strike and Rerup ( 2015 ) found that trusted outside advisors can attenuate the tendency toward commitment by inserting doubt into the sensemaking process. Such doubt is crucial for adaptive sensemaking. This shows how individuals can also influence how collectives make sense. In considering the degree to which individuals can influence collective sensemaking, we must note how social positions grant various forms of economic, social, and cultural capital that shape how people construct issues (Ibarra & Andrews, 1993 ; Lockett, Currie, Finn, Martin, & Waring, 2014 ; Westley, 1990 ).

When we consider multiple parties, this inevitably brings up questions of power, politics, and influence. For instance, Maitlis ( 2005 ) explored how sensemaking occurs across multiple parties in more complex organizational settings. She found that leaders influenced the degree to which sensemaking was controlled. Leaders controlled sensemaking processes by scheduling meetings, forming committees, and planning events. Importantly, they also controlled which organizational members had access to these sensemaking channels. In this way, leaders had greater ability to engage in sensegiving and they typically did so in less public forums. On the other hand, stakeholders influenced the degree to which sensemaking was animated. Active and involved stakeholders induced more sharing of information, reporting to board members, and so on. This prompted greater and more constant communication of issues and interpretations within the organization. In this way, multiple parties within organizations influence sensemaking (Fiss & Zajac, 2006 ). They can also prevent effective sensemaking. For instance, Dunbar and Garud ( 2009 ) found that two groups at NASA developed different patterns of interpretation, focusing respectively on safety issues and scheduling issues. As a result, they were unable to effectively interpret and act on a cue of foam shedding on the Columbia space shuttle, resulting in disaster as it re-entered Earth’s atmosphere. This case clearly highlights how the transition from group-level intersubjectivity to organization-level generic subjectivity is fraught with danger.

Similarly, there is also value in considering second-order sensemaking (Sandberg & Tsoukas, 2015 ). Second-order sensemaking refers to how outside groups subsequently make sense of another group’s sensemaking process. A famous case is the Rogers Commission, which investigated the sensemaking process that led to the Challenger launch decision (cf. Vaughan, 1996 ). Such public inquiries extract cues from the original sensemaking episode and often seek to find fault or blame. For example, if an individual reports being emotional during sensemaking in crisis, this can later be interpreted during second-order sensemaking as a lack of professionalism (Gephart, 1993 ). It is important to remember that this second-order sensemaking process is not simply “finding facts” and “uncovering the truth.” Second-order sensemaking often occurs as part of a broader strategy of “depoliticizing disaster events,” “legitimating the actions and interests of dominant groups,” and reducing post-disaster anxiety “by elaborating fantasies of omnipotence and control” (Brown, 2000 , p. 45; see also Boudès & Laroche, 2009 ; Brown, 2004 ). However, it is certainly possible that sensemaking about crises can foster greater preparedness for similar future crises as well (Nathan, 2004 ).

Finally, we can also consider how sensemaking at the group and organizational level connect with the broader social forces described by institutional theory (see Jennings & Greenwood, 2003 ; Weick, 2003 ). On one hand, the interpretive tools most readily available to organizational members necessarily reflect the institutions in which they participate (Weber & Glynn, 2006 ). This means that individuals in organizations draw on institutional resources like identities (e.g., union member), expectations (e.g., loyalty), and frames (e.g., going on strike) in making sense. In fact, given how thoroughly we internalize our institutional realities, it may even be hard to make sense outside of the resources they offer. At the very least, local context can cognitively prime individuals to draw on institutional resources and socially prompt others to push behavior in line with these resources. On the other hand, sensemaking processes can also help us understand these forces as well (Ocasio, Loewenstein, & Nigam, 2015 ; Porac, Ventresca, & Mishina, 2005 ). For instance, sensemaking analyses have shed light on institutions as diverse as religion (Pratt, 2000a ), globalization (Fiss & Hirsch, 2005 ), policing (Maguire & Katz, 2002 ), markets (Abolafia & Kilduff, 1988 ), and health care (Nigam & Ocasio, 2010 ). In this way, a wealth of contextual factors across multiple types of social agents and levels of analysis influence the sensemaking process.

Conclusions and Future Directions

In sum, we can see that sensemaking offers both a general perspective on organizations and describes a specific process by which people collectively organize their world. It challenges us to complicate ourselves such that we are comfortable with retrospect, enactment, feedback loops, human agency, multiple conflicting narratives, plausible interpretations, emergent order, and process. Nonetheless, we still have much to learn (cf. Miner, 2005 ). Some avenues include developing richer conceptualizations of how embodiment and emotions sculpt the sensemaking process and how sensemaking occurs across individual, group, organization, and institutional levels. Questions of how much sensemaking operates through prospective, future-oriented thinking in addition to the established retrospective processes also remain open and worth exploring (Gephart, Topal, & Zhang, 2010 ; Gioia, Thomas, Clark, & Chittipeddi, 1994 ; MacKay, 2009 ; Stigliani & Ravasi, 2012 ; Wright, 2005 ). New technologies including social media likely change how sensemaking occurs by distributing interpretations across wider groups (Boland, Tenkasi, & Te’eni, 1994 ). They also offer opportunities to collect valuable data on discursive means of sensemaking (Gephart, 2004 ; Herrmann, 2007 ). To this end, there is also value in expanding methodological and research design considerations. For instance, alternate methodological approaches such as action research (Allard-Poesi, 2005 ; Lüscher & Lewis, 2008 ) and agent-based modeling (Gavetti & Warglien, 2015 ; Rudolph et al., 2009 ) may prove valuable. We can also better tie sensemaking to the actual practice of social science and management. In this way, sensemaking can become not only a topic to research, but more closely integrated into the process of social science research itself (Cornelissen, 2006 ; Weick, 1993c , 2007 ). Managers can similarly understand and apply a sensemaking perspective to their own work in fruitful ways (Parry, 2003 ).

Finally, although sensemaking may help us understand how humans do behave descriptively, it cannot make claims regarding how humans prescriptively should behave. A sensemaking perspective reminds us that it is not merely the facts of history that matter, but how those facts are interpreted. The “logical conclusion” of this reminder is potentially discouraging: that “the organizational world will tend ever more relentlessly toward a postmodern world where image dominates substance—and in particular a world in which images of change supplant substantive change” (Gioia et al., 2002 , p. 632). Therefore, we must realize that the descriptive validity of sensemaking as a perspective does not mean it cannot have a potentially harmful influence in practice. This calls our attention to the need to participate responsibly in the sensemaking process. To make sense with and through organizations, we must be willing to not only reinterpret the past, but also help author the future through mindful action. Such mindful action requires not only commitment to our values and beliefs, but also the courage to doubt.

Acknowledgements

This research was supported by the Future Resilient Systems project at the Singapore-ETH Centre, which is funded by the National Research Foundation of Singapore under its Campus for Research Excellence and Technological Enterprise program (FI 370074011).

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Sensemaking

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Sensemaking is typically understood as the ongoing production of images, labels, stories or symbols in order to stabilize the streaming of experience. People make sense by focusing on a limited set of cues and elaborating those few cues into a plausible, momentarily useful, guide for action. And actions themselves partially define the guide that they follow. Sensemaking is better thought of as a process of bringing about reality rather than discovering it. The sensemaking perspective, with its emphasis on the social construction of reality, provides an important contrast to traditional organization theory, which emphasizes information processing and decision-making under uncertainty.

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Sutcliffe, K.M. (2018). Sensemaking. In: Augier, M., Teece, D.J. (eds) The Palgrave Encyclopedia of Strategic Management. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-00772-8_371

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There are a variety of frameworks and methodologies that provide distinct sense-making methods. Without adopting a particular process, the following chart shows some easy steps to begin sense-making. Note that the process is non-linear, meaning that the steps take place concurrently. Sense-making, like the world makes sense of, is an emergent process.

Principles of Sense-making

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Development of the Sci-math Sensemaking Framework: categorizing sensemaking of mathematical equations in science

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• Anita Schuchardt   ORCID: orcid.org/0000-0001-6978-6484 2  

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Scientific ideas are often expressed as mathematical equations. Understanding the ideas contained within these equations requires making sense of both the embedded mathematics knowledge and scientific knowledge. Students who can engage in this type of blended sensemaking are more successful at solving novel or more complex problems with these equations. However, students often tend to rely on algorithmic/procedural approaches and struggle to make sense of the underlying science. This deficit may partly be the fault of instruction that focuses on superficial connections with the science and mathematics knowledge such as defining variables in the equation and demonstrating step-by-step procedures for solving problems. Research into the types of sensemaking of mathematical equations in science contexts is hindered by the absence of a shared framework. Therefore, a review of the literature was completed to identify themes addressing sensemaking of mathematical equations in science. These themes were compiled into nine categories, four in the science sensemaking dimension and five in the mathematics sensemaking dimension. This framework will allow for comparison across studies on the teaching and learning of mathematical equations in science and thus help to advance our understanding of how students engage in sensemaking when solving quantitative problems as well as how instruction influences this sensemaking.

Introduction

Mathematical equations are used to represent scientific phenomenon and communicate scientific ideas (Bialek & Botstein, 2004 ; Brush, 2015 ; Gingras, 2001 ; Lazenby & Becker, 2019 ; Steen, 2005 ). Students are expected to be able to engage in sensemaking with these equations to interpret the mathematical and scientific meaning represented by the equation (Bialek & Botstein, 2004 ; Heisterkamp & Talanquer, 2015 ; Kuo, Hull, Gupta, & Elby, 2013 ; Sevian & Talanquer, 2014 ). However, studies on students solving quantitative problems show that they often solve problems by relying on algorithmic procedures without making connections between the mathematical equation and the scientific phenomenon (Bing & Redish, 2009 ; Stewart, 1983 ; Taasoobshirazi & Glynn, 2009 ; Tuminaro & Redish, 2007 ). This tendency to solve problems algorithmically has been associated with a failure to transfer problem-solving techniques to novel contexts or more complex problems (Becker & Towns, 2012 ; Nakhleh, 1993 ; Ralph & Lewis, 2018 ; Schuchardt & Schunn, 2016 ; Stamovlasis, Tsaparlis, Kamilatos, Papaoikonomou, & Zarotiadou, 2005 ). The reliance on algorithmic problem-solving strategies has been attributed to the different opportunities provided for sensemaking of mathematical equations in science during instruction (Bing & Redish, 2008 ; Lythcott, 1990 ; Schuchardt & Schunn, 2016 ). To successfully develop and understand the impact of providing different sensemaking opportunities, it is first necessary to understand the types of sensemaking that can occur. However, a consistent and coherent framework of sensemaking of mathematical equations in science has not yet been developed. This paper presents such a framework based on a review of the literature on instruction of mathematical equations in science and on students’ problem-solving using mathematical equations in a science context.

Scientists use mathematical knowledge to represent ideas about scientific phenomenon

Canonical mathematical equations in science have been developed based on understanding of both scientific phenomena and mathematical concepts represented in the equations (Ghosh, 2009 ; Quale, 2011 ). Scientists use mathematical equations to formulate theories deduced from observations or experimentation or to represent patterns they observe (Brush, 2015 ; de Ataíde & Greca, 2013 ; De Berg, 1992 ; Ghosh, 2009 ; Pospiech, 2019 ; Steen, 2005 ; Wigner, 1960 ). For example, Newton’s second law of motion is often represented as \( {\overrightarrow{F}}_{net}=m\overrightarrow{a} \) . The development of \( {\overrightarrow{F}}_{net}=m\overrightarrow{a} \) was based on observations from multiple scientists, as well as many experiments in making sense of the scientific phenomenon (Ghosh, 2009 ). The equation represents a “central principle of classical mechanics” (Gierer, 2004 ), a theory that has been used to explain the motions of objects. In biology, the mathematical expression for the population growth of bacteria in optimal growth conditions N t  =  N 0 2 x is based on scientists’ biological knowledge of the relationship among the initial population size ( N 0 ), the number of generations ( x ), and the final population size ( N t ) after a specific time period. This biological understanding was combined with knowledge of how to arrange the mathematical variables and mathematical operations (i.e., multiplication, exponents) to match the quantitative relationships to the patterns found in the phenomenon. Whether equations are developed to represent a theory or a pattern, both scientific knowledge and mathematical knowledge are embedded in these equations.

Mathematical equations are often referred to as models of scientific phenomena, “a representation of structure in a physical system or process” (Hestenes, 2010 , p. 18). The development of mathematical models of scientific phenomena as engaged in by scientists is a multi-faceted, multi-step process known as the modeling cycle (Diaz Eaton et al., 2019 ; Gouvea & Passmore, 2017 ; Halloun, 2007 ; Hestenes, 2010 ). The steps in the modeling cycle include identifying the task or relations to be represented, mathematizing the physical entities, structuring the equation to express the pattern, interpreting the equation with reference to the scientific process, and validating the equation (Borromeo Ferri, 2006 ; Dukerich, 2015 ; Gouvea & Passmore, 2017 ; Redish, 2017 ). If the mathematical model is validated in one situation, the process is repeated in other situations. If the model is not found to match the data, either the idea is rejected or the model is modified (Halloun, 2007 ; Hestenes, 2010 ). The modeling process as engaged in by scientists provides multiple opportunities for making sense of the connections between the mathematical equation, the phenomenon, and the mathematical ideas.

Three theoretical perspectives have been widely used when investigating students’ ability to solve quantitative problems in science; the resources framework (diSessa, 1993 ; Hammer, 2000 ; Redish & Kuo, 2015 ; Rodriguez, Bain, Hux, & Towns, 2019 ), epistemological framing (Bing & Redish, 2012 ; Chen, Irving, & Sayre, 2013 ; Hammer, Elby, Scherr, & Redish, 2005 ; Hutchison & Hammer, 2009 ; Redish, 2004 ; Tuminaro & Redish, 2007 ), and sensemaking (Becker & Towns, 2012 ; Bing & Redish, 2007 ; Dreyfus, Elby, Gupta, & Sohr, 2017 ; Eichenlaub & Redish, 2019 ; Kuo et al., 2013 ; Sherin, 2001 ). The resource perspective examines how and what cognitive resources are activated in the problem-solving process (Hammer, 2000 ; Redish & Kuo, 2015 ). Studies guided by epistemological framing focus on how students perceive mathematics in science and solving quantitative problems in science classrooms as well as how students’ framing affects problem-solving (Chen et al., 2013 ; Tuminaro & Redish, 2007 ). Sensemaking is broadly defined as using prior resources and knowledge to understand new concepts or representations or to solve problems (Kapon, 2016 ; Martin & Kasmer, 2009 ; Schoenfeld, 1992 ). With respect to mathematical equations in science, students need to make sense of both the mathematical structure of the equation (Sherin, 2001 ) and the connections to the scientific phenomenon (Redish, 2017 ; Schuchardt & Schunn, 2016 ). These three perspectives are not mutually exclusive. The resources that a student activates are likely influenced by their epistemological framing (Hammer et al., 2005 ; Redish & Kuo, 2015 ). The resources that are activated are likely to affect sensemaking (Dreyfus et al., 2017 ).

Blended sensemaking as a lens for investigating students’ quantitative problem-solving

Blended sensemaking is described as the process of combining separate cognitive resources to generate a new merged, blended understanding (Fauconnier & Turner, 1998 ). Scientific knowledge and mathematical knowledge are two cognitive resources that can be activated during sensemaking of mathematical equations that describe scientific phenomena. The sensemaking of these equations can occur with respect to only scientific ideas or only mathematical ideas if only one of these resources is activated, or sensemaking can be blended, making use of both cognitive resources (Bain, Rodriguez, Moon, & Towns, 2019 ; Eichenlaub & Redish, 2019 ). For example, in the sensemaking of \( {\overrightarrow{F}}_{net}=m\overrightarrow{a} \) , the resources from mathematics (e.g., procedures of doing calculation, knowledge of multiplication, knowledge of the mathematics notation) can be blended with resources from science (e.g. the net force causes the acceleration) and together form a blended mental space that enables proper interpretation and application of this equation. Experts’ understanding of physics equations includes the blending of mathematical forms and physical intuition, but novices tend to treat mathematical equations as a calculation tool without connection to the physics knowledge (Eichenlaub & Redish, 2019 ). In the above equation, the vector notation above the “ F ” and the “ a ” has mathematical meaning indicating that those quantities have direction. This mathematical understanding can be combined with the physics knowledge that acceleration will occur in the same direction as the net force. After receiving instruction in Newton’s second law, undergraduate physics students were asked to solve two problems related to this topic. Many students failed to access their scientific and mathematics resources and showed difficulty in understanding the directionality embedded in the equation (Flores-García, Terrazas, González-Quezada, Pierce, & Soto, 2008 ). Students often fail to access both of these resources and thus often do not pay attention to the directionality embedded in the equation (Flores-García et al., 2008 ).

Categorization of blended sensemaking has been used to describe students’ quantitative problem-solving (Bain, Rodriguez, Moon, & Towns, 2018 ; Bain, Rodriguez, Moon, & Towns, 2019 ; Bing & Redish, 2007 , 2009 ; Brahmia, Boudreaux, & Kanim, 2016 ; Greca & Moreira, 2002 ; Hu & Rebello, 2013 ; Kuo et al., 2013 ; Tuminaro & Redish, 2007 ). Bing and Redish ( 2007 ) identified two types of blending in students’ problem-solving processes, single-scope blend (a one-way mapping) and double-scope blend (a back-and-forth integration). The difference between these two types of blending lies in whether the sensemaking uses only one cognitive resource or moves back and forth between the science and mathematics cognitive resources. In one study (Bing & Redish, 2007 ), when students were reasoning about the direction of air drag for falling objects using the equation F v  =  −  bv , they started their sensemaking of the equation in the physics space, mapping the variables onto aspects of the scientific phenomenon, and then used their physics knowledge to reason that friction ( F v ) operates in the opposite direction of velocity ( v ). They then discussed the mathematical rule that multiplying two negatives together yields a positive outcome. Finally, they showed a blending of the knowledge from physics and mathematics when they reasoned that positive is up, and therefore, the direction of friction for falling objects is up (Bing & Redish, 2007 ). In chemistry, students were found to make sense of equations starting from either a chemistry or mathematics space and then pull in concepts from the other discipline to complete the problem-solving process (Bain, Rodriguez, Moon, & Towns, 2019 ). These authors proposed that the quality of blended sensemaking was dependent on whether students applied a superficial or sophisticated conceptual understanding in chemistry space. However, details were not provided on what kind of conceptual understanding should be considered as superficial or sophisticated, or on the difference between low and high quality blended sensemaking. This study aims to provide a less-evaluative framework for examining sensemaking that provides rich descriptions of the types of sensemaking that are occurring in both the mathematics and science dimensions. The definition and examples used to explain each type of sensemaking will enable consistent descriptions for sensemaking that can then be evaluated based on additional criteria.

Students’ difficulties with quantitative problem-solving have been attributed to difficulties with making sense of the conceptual knowledge embedded in the mathematical equations (Bing & Redish, 2007 , 2009 ; Schuchardt & Schunn, 2016 ; Tuminaro & Redish, 2007 ). In physics, chemistry, and biology, students struggle to apply mathematical equations they have learned in class to novel or more complex scenarios (Becker & Towns, 2012 ; Nakhleh, 1993 ; Ralph & Lewis, 2018 ; Schuchardt & Schunn, 2016 ; Stamovlasis et al., 2005 ). This difficulty has been attributed to students’ tendency to apply memorized algorithmic procedures instead of making sense of connections between the mathematical equations and the modeled scientific phenomenon (Bing & Redish, 2009 ; Stewart, 1983 ; Taasoobshirazi & Glynn, 2009 ; Tuminaro & Redish, 2007 ). When students spontaneously apply blended sensemaking, they are able to overcome being stuck and solve more complex problems (Bing & Redish, 2007 ; Kuo et al., 2013 ). One instructional approach that provides opportunities for students to connect the scientific phenomenon to the mathematics is model-based instruction (Blum & Borromeo, 2009 ). If instruction encourages engagement in mathematical modeling and sensemaking, students show improved quantitative problem-solving for novel and more complex problems (Becker, Rupp, & Brandriet, 2017 ; Lazenby & Becker, 2019 ; Schuchardt & Schunn, 2016 ).

Viewing instruction of mathematics in science through the lens of blended sensemaking

Sensemaking opportunities provided by the instructors in the classroom serve a critical role in students’ learning (Koretsky, Keeler, Ivanovitch, & Cao, 2018 ; Li & Schoenfeld, 2019 ; Lo, Marton, Pang, & Pong, 2004 ; Marton, Runesson, & Tsui, 2004 ). What the instructor does and says create the opportunities or the environment for students to make sense of something, i.e., the conditions for students to learn specific content or skills (Marton et al., 2004 ). Mathematics instruction in the USA has been criticized as “broad and shallow” (Polikoff, 2012 ), focusing on procedures without connection to mathematical concepts (Litke, 2020 ). Teachers in countries whose students do well on mathematical assessments tend to focus more on conceptual understanding than procedures (Hill, Rowan, & Ball, 2005 ). Science instruction that focuses on high-level thinking (e.g., doing scientific inquiry) as compared to low-level thinking (e.g., rote memorization) is also associated with better learning outcomes for students (Tekkumru-Kisa, Stein, & Schunn, 2015 ). Besides the effect on students’ learning outcomes, instruction also affects students’ perception of what is valued in science learning and their understanding of the nature of scientific knowledge (Bing & Redish, 2009 ; Eichenlaub & Redish, 2019 ; Hutchison & Hammer, 2009 ; Kang, Windschitl, Stroupe, & Thompson, 2016 ; Russ, 2018 ; Russ, Coffey, Hammer, & Hutchison, 2009 ; Tuminaro & Redish, 2007 ). For example, evaluation in introductory physics courses tends to focus on students’ correct calculation rather than their understanding of the meaning of the equations (Eichenlaub & Redish, 2019 ). In this instructional environment, students can develop the belief that mathematics is merely a tool to do calculation in physics, and they may devalue conceptual understanding of mathematical equations.

Instructional opportunities for student sensemaking of equations are not necessarily synonymous with specific teaching practices. For example, although making sense of mathematical rules often occurs through instructors delivering explanations via lecture (Njini, 2012 ), Baig and Halai ( 2006 ) presented a student-centered learning activity to make sense of four rules for working with fractions. Marton et al. ( 2004 ) argued that the understanding of what learners are expected to learn needs to occur before an effective teaching method can be identified. However, attention is rarely paid to the sensemaking opportunities created by what the instructor is doing or saying.

Many frameworks or protocols have been proposed to describe or measure instructional practice or discourse in mathematics or science classrooms, e.g., Classroom Observation Protocol for Undergraduate STEM (Smith, Jones, Gilbert, & Wieman, 2013 ), Classroom Discourse Observation Protocol (Kranzfelder et al., 2019 ), Instructional Quality Assessment (Boston, 2012 ), Mathematical Quality of Instruction (Learning Mathematics for Teaching Project, 2011 ), and Reformed-Oriented Teaching Observation Protocol (Sawada et al., 2002 ). Very few frameworks specifically discuss instruction of mathematics in science classrooms. One framework that has been developed is the Mathematics Integrated into Science: Classroom Observation Protocol, MISCOP (Judson, 2013 ). This framework seeks to characterize the extent of integration of mathematics and science and the overall quality of instruction to evaluate the quality of integration of mathematics in science. The opportunities for student sensemaking of mathematical equations in science provided by instructors are not addressed.

This paper establishes the Sci-Math Sensemaking Framework for categorizing sensemaking of mathematical equations in science on the science and mathematics dimensions. Categories within the framework are identified based on a literature review using manuscripts from both the science education and mathematics education communities. This framework supplies researchers with a common language for discussing opportunities instructors provide for sensemaking of mathematical equations in science as well as student use of sensemaking when working with these equations.

The objective for this literature review is to identify ideas expressed in the literature about the different types of mathematics sensemaking and science sensemaking of mathematical equations in science. A snowballing approach that began with recent reviews of the literature was used (Wohlin, 2014 ). The procedure is shown in Fig. 1 and includes identification of an initial set of manuscripts, a backward screening on the reference lists of the starting set of manuscripts, a forward screening on the publications citing the starting set of manuscripts, and iteration of backward and forward screening on the included publications (Wohlin, 2014 ).

Summary of the snowballing approach

Identifying the starting set of manuscripts

Guided by the theory of blended sensemaking of mathematical equations in science, five topics from the field of education research were chosen to search for the starting set of manuscripts (Table 1 ). Three of the topics covered mathematics in three science fields commonly taught in schools: chemistry, biology, and physics. Mathematics sensemaking and science sensemaking were the other two topics to provide publications that discussed sensemaking in each discipline. Within each topic, the starting set of manuscripts was identified using a key word search in Google Scholar or based on recommendations from experts. When more than one reference was identified through these methods, the publications that contained the greatest number of citations were retained to provide breadth and depth of literature in the initial backward snowballing. Out of these, the most recent manuscript was chosen in each topic area to uncover the most recent publications in the field. Five manuscripts published during 2015–2019 were identified that cover the following topics: (a) mathematics in physics, (b) mathematics in chemistry, (c) mathematics in biology, (d) science sensemaking, and (e) mathematics sensemaking (Table 1 ).

Iteration 1

Backward snowballing.

For each of the five starting manuscripts, the publications listed in the references were screened based on the following inclusion and exclusion criteria. The inclusion criteria were: published between 1986 and 2019; written in English; included components of mathematical knowledge of equations or scientific knowledge with respect to equations; and published papers, conference proceedings, book chapters, or dissertations. The exclusion criteria were: published earlier than 1986, a whole book, written in a non-English language, not about mathematical equations, provided only a broad description of teaching strategies or student problem-solving abilities or student epistemologies, not focused on mathematical equations, and from the same research group referring to the same theories and findings as other references. The foundational research symposium on mathematical sensemaking (Hiebert & Lefevre, 1986 ) was published in 1986, and research in mathematics in science emerged after research in mathematics education. Therefore, the criterion for publication year was set as no earlier than 1986. References were initially screened by title and then abstract. References which remained were read in their entirety to reach the final decision about inclusion. Forty-six references were identified through backwards snowballing.

Forward snowballing

Forward snowballing was performed on the five manuscripts in the starting set. Citations of each of these manuscripts were located through the “cite” function under Google Scholar. Google Scholar was chosen because of the power of the forward cite function, even though the algorithms for this search tool are not publicized. The multiple iterations of backwards snowballing (screening references listed in identified papers), as well as the use of recent reviews of the literature in five fields to initiate the literature review, mitigate against the danger that some articles may not be included in the review due to bias in the database. The inclusion criteria for forward snowballing were the same as for backward snowballing. Of the five starting manuscripts, the book chapter for mathematics sensemaking (Rittle-Johnson & Schneider, 2015 ) has been cited 222 times, the paper of science sensemaking (Odden & Russ, 2019 ) has been cited 20 times, the dissertation of mathematics in biology (Schuchardt, 2016 ) has been cited once, while the book chapter for mathematics in physics (Pospiech, 2019 ) and the paper of math in chemistry (Bain, Rodriguez, & Towns, 2019a ) have not been cited yet. Because of the recent publication dates for the starting manuscripts, the forward snowballing was not expected to produce many citations. The book chapter for mathematics sensemaking was the most cited but most of the citations are not about knowledge of mathematical equations and thus only four publications were retained from these citations. A total of fifty manuscripts were retained from iteration 1 snowballing (46 from backward snowballing and 4 from forward snowballing).

Iteration 2

A second iteration of backward and forward snowballing was performed on all 50 publications retained from iteration 1 (see Table S 1 ).

The inclusion criteria for backward snowballing during iteration 2 was the same as during iteration 1 but an additional criterion was added: references had to use a new theoretical framework about mathematical equations to guide their research or analysis. Six publications were identified from the backward snowballing.

Publications that cited any of the 50 manuscripts retained from iteration 1 were identified using Google Scholar. If the citation list for the publication identified in the first iteration contained fewer than 30 cites, all publications were screened by title and abstract and then by reading the whole reference. If the citation list contained more than 30 cites, an initial filtering step was performed using the key words “equation” or “sensemaking,” or “blend”, or “model.” If references contained these keywords, they were then screened one by one using the same criteria as for the backward snowballing. Two additional references were identified from the forward snowballing in iteration 2.

Criteria for saturation

One criterion for determining when a literature review is complete is whether new meaningful information arises by including more references (vom Brocke et al., 2015 ). Saturation was considered to be reached if the ratio between the total number of included references to the total number of references examined is very low in iteration 2 compared to that of iteration 1. In iteration 1, fifty references were included after screening 813 references. In iteration 2, eight references were included after screening 11,118 references. Because the ratio fell from 6.2 to 0.07% and no new themes on sensemaking of equations arose in retained publications from iteration 2, we concluded that the review identified most of the articles related to the field of sensemaking of mathematics in science and therefore was saturated.

Themes developed from the retained publications

The retained references were read and themes on sensemaking of mathematical equations in science were noted as they arose, and short descriptions were generated. These themes and descriptions were presented to and discussed over several iterations with members of an educational research group resulting in the retention of nine themes. These themes formed the nine categories of the Sci-Math Sensemaking Framework. Distinctions between categories are provided in the results section where descriptions and examples are used to specify the similarities and differences between categories.

Mathematical equations in science contain conceptual knowledge about mathematics based on the arrangement of the symbols and the operations contained within the equations. These equations also contain connections to a scientific phenomenon. To enable characterization of the sensemaking that is occurring in a science classroom along both of these dimensions, the nine themes identified from the literature review were classified as either science sensemaking (referring to making sense of connections to the scientific phenomenon) or mathematics sensemaking (referring to making sense of mathematics). The science sensemaking dimension of the Sci-Math Sensemaking Framework contains four categories while the mathematics sensemaking dimension contains five categories (Table 2 ).

Categories within the science sensemaking dimension

There is a rich history of studying how students make sense of science (Berland et al., 2016 ; diSessa, 1993 ; Ford, 2012 ; Kapon, 2016 ; Odden & Russ, 2019 ; Russ, Scherr, Hammer, & Mikeska, 2008 ). Science sensemaking is defined as “the process of building an explanation to resolve a perceived gap or conflict in knowledge” (Odden & Russ, 2019 , p. 187). Therefore, science sensemaking of mathematical equations used in science aims at understanding the scientific knowledge represented by the equations. Sensemaking of mathematical equations in science can occur during classroom instruction of equations or during the process of students interpreting or developing or applying equations (Etkina, Warren, & Gentile, 2006 ; Hestenes, 2010 ; Lazenby & Becker, 2019 ; Redish & Kuo, 2015 ; Schuchardt & Schunn, 2016 ). The four categories describing science sensemaking through mathematical equations in science are Sci-Label, Sci-Description, Sci-Pattern, and Sci-Mechanism (Table 2 ). The four categories are ordered theoretically to represent an increasingly sophisticated understanding of the scientific phenomenon.

Sci-Label sensemaking

Sci-Label sensemaking refers to connecting variables in mathematical equations with characteristics of objects or processes within the scientific phenomena. These characteristics could refer to quantifiable aspects of specific objects in the phenomenon (e.g., number of sperm types, mass) or they could refer to a quantity that characterizes a process within the phenomenon (e.g., time, temperature, force) (Becker et al., 2017 ; Becker & Towns, 2012 ; Bing & Redish, 2007 ; Geyer & Kuske-Janßen, 2019 ; Hansson, Hansson, Juter, & Redfors, 2015 ; Hu & Rebello, 2013 ; Izsák, 2004 ; Kuo et al., 2013 ; Lehavi et al., 2017 ; Pietrocola, 2009 ; Quale, 2011 ; Redish, 2017 ; Redish & Kuo, 2015 ; Rodriguez et al., 2019 ; Schuchardt, 2016 ; Schuchardt & Schunn, 2016 ; Svoboda & Passmore, 2013 ; Tuminaro & Redish, 2007 ; Wink & Ryan, 2019 ). For example, in the equation  \( \mathrm{F} net=\mathrm{ma} \) , the variables F , m , and a are defined as the net force applied to an object, the mass of the object, and the net acceleration of the object, respectively. It should be noted that operators (e.g., division or addition symbols) could also receive labels. For example, the division symbol in a  =  F / m could be labeled as “distributed over” or simply as “divided by” (Redish, 2017 ). However, explicit labeling of operators in a manner parallel to variables was not present as a sensemaking process used by students or instructors in any of the papers reviewed.

The first step in students’ interpretation of equations or their application of equations to solve problems is often labeling the variables. For example, when students were asked how they would explain the equation  v  =  v 0  +  at to their friends, one student answered that “I think the first thing you’d need to go over would be the definitions of each variable and what each one means” (Kuo et al., 2013 , p. 46). Labeling variables in the mathematical equation has been identified in multiple studies on students’ interpretation of equations when solving quantitative problems in science (Becker & Towns, 2012 ; Bing & Redish, 2007 ; Hu & Rebello, 2013 ; Redish & Gupta, 2010 ; Rodriguez et al., 2019 ; Tuminaro & Redish, 2007 ). Literature on students’ development of mathematical equations also shows that one of the first steps is selecting and quantifying characteristics of the scientific phenomenon as variables (Izsák, 2004 ; Quale, 2011 ; Schuchardt & Schunn, 2016 ; Svoboda & Passmore, 2013 ).

Similarly, during instruction of mathematical equations in science, the first step is often defining the variables in the equation (Hansson et al., 2015 ; Lehavi et al., 2017 ; Schuchardt, 2016 ; Schuchardt & Schunn, 2016 ). In their study on the role of mathematics in physics lessons in upper-secondary school, Hansson et al. ( 2015 ) presented a description of a lecture on electric fields. The physics teacher introduced the equation F  =  EQ with “ F is the force that the electron senses in the electric field” before proceeding to manipulate the equation (Hansson et al., 2015 , p. 628). It has been proposed that this mapping of variables in the equation to objects in the phenomenon sets the foundation for problem-solving (Kuo et al., 2013 ; Redish & Kuo, 2015 ; Rodriguez et al., 2019 ; Schuchardt, 2016 ). Because defining important variables relevant to the scientific phenomena is an essential step during the sensemaking of the mathematical equations, but does not go beyond making label connections, this category is placed on the first level of sensemaking in the Sci-Math Sensemaking Framework.

Sci-Description sensemaking

The Sci-Description sensemaking category captures the use of mathematics to provide a measure of properties of physical objects, or of scientific phenomena or systems (Bain, Rodriguez, & Towns, 2019b ; Brahmia et al., 2016 ; Geyer & Kuske-Janßen, 2019 ; Lehavi et al., 2017 ; Lehrer & Schauble, 2010 ; Pospiech, 2019 ; Wink & Ryan, 2019 ). For example, density is a measure of the property of a substance. The density equation ρ  =  m / V is an invented quantifiable characteristic derived from the two direct measures, mass and volume (Pospiech, 2019 ). This category differs from Sci-Label because in Sci-Label the focus is on establishing only the connection between a variable and the name of a characteristic of a scientific phenomenon, e.g., ρ is density, m is mass, and V is volume, while in Sci-Description the entire equation describes how a measure such as density is calculated. Descriptive equations are found across scientific disciplines. In biology, the Shannon index equation is a description of the biodiversity of a biological system. In chemistry, the equation for the equilibrium constant is a measure of the state of a reaction at equilibrium (Bain, Rodriguez, & Towns, 2019b ). Many statistical equations are descriptions of features of a system, e.g., mean, standard deviation (Lehrer & Schauble, 2010 ).

Few studies address Sci-Description sensemaking (Brahmia et al., 2016 ; Lehavi et al., 2017 ; Lehrer & Schauble, 2010 ). Lehavi et al. ( 2017 ) describe a class discussion on the definition of speed, “the change in distance versus time” (p. 99), where the instructor tried to focus students’ attention on the definition of speed as a derived measure. However, students had difficulty with the idea that speed is a variable described by the equation while time and distance are direct measurements. Several other studies discussed instances where students derived descriptive equations from data. In biology, Lehrer & Schauble ( 2010 ) provide examples of students inventing mathematical equations to describe characteristics of a population of organisms (e.g., variation, average growth, measure of a healthy aquatic system). In physics, Brahmia et al. ( 2016 ) listed several examples of students’ inventing equations to describe the features of the motion of cars, such as how fast cars move and how rapidly cars speed up.

Sci-Pattern sensemaking

The category of Sci-Pattern sensemaking emerged from multiple studies suggesting mathematical equations in science represent patterns in scientific phenomena (Bain, Rodriguez, & Towns, 2019b ; Baxter et al., 2014 ; Becker et al., 2017 ; Becker & Towns, 2012 ; Etkina et al., 2006 ; Geyer & Kuske-Janßen, 2019 ; Gupta & Elby, 2011 ; Hestenes, 2010 ; Hu & Rebello, 2013 ; Karam & Krey, 2015 ; Kuo et al., 2013 ; Michelsen, 2015 ; Pospiech, 2019 ; Quale, 2011 ; Redish, 2017 ; Rodriguez et al., 2019 ; Sherin, 2006 ; Svoboda & Passmore, 2013 ). The Sci-Pattern category captures sensemaking of the trend or pattern among variables in the mathematical expression, how properties of a system vary with respect to one another. For example, possible sensemaking opportunities for science patterns in the equation ρ  =  m / V are (1) for the same substance, the larger its volume, the larger its mass or (2) for objects with the same volume, the larger its density, the larger its mass. This type of sensemaking differs from Sci-Description because the emphasis is on understanding the relationships among variables in the equation as opposed to understanding that the equation is providing a descriptive measurement of a specific characteristic of an object or system.

The idea of sensemaking of scientific patterns embodied within a mathematical expression in science is presented in both theoretical and empirical literature about mathematics in science. For example, in chemistry, mathematical equations are often used to describe features of stable and dynamic chemical systems (Bain, Rodriguez, Moon, & Towns, 2019 ; Bain, Rodriguez, & Towns, 2019b ; Rodriguez et al., 2019 ). The Gibbs free energy equation, ∆ G  = ∆ H  −  T ∆ S , describes the relationship among change in entropy, change in enthalpy, and the change in free energy in a chemical reaction. One student referred to how the change in enthalpy (Δ H ) and the change in entropy (∆ S ) lead to a negative change in the Gibbs free energy (∆ G ) when explaining the formation of lipid membranes (Redish, 2017 ). Sci-Pattern sensemaking is a common focus in learning activities designed by teachers to integrate mathematics into science (Baxter et al., 2014 ; Michelsen, 2015 ). For example, one group developed a learning module for students to investigate the relationship between the coefficient of friction and braking distance for cars (Michelsen, 2015 ). In this curriculum, the learning objective was for students to develop an understanding of the pattern that for cars with the same initial speed, the wetter the road, the longer the breaking distance.

Sci-Mechanism sensemaking

Mathematical equations can be used to describe a causal relationship among objects within the phenomenon (Etkina et al., 2006 ; Hestenes, 2010 ; Lazenby, Rupp, Brandriet, Mauger-Sonnek, & Becker, 2019 ; Redish, 2017 ; Redish & Kuo, 2015 ; Schuchardt, 2016 ; Schuchardt & Schunn, 2016 ). The causal relationship that can be described by an equation is the scientific mechanism that explains how or why a scientific phenomenon occurs (Machamer, Darden, & Craver, 2000 ). A pattern only provides information on which scientific entities are related, but a mechanism shows why the relationship among entities behaves in that way. A single equation can be interpreted or taught as describing a causal mechanism and/or describing a pattern. For example, the equation for Ohm’s law I = U / R , can be interpreted or taught by an individual using Sci-Mechanism sensemaking as describing the causal mechanism for the current: the current ( I ) in a conductor is caused by (represented by the equals sign) the voltage difference between two points ( U ) applied across (represented by the division symbol) the resistance ( R ) (Sci-Mechanism). Alternatively, individuals engaged in Sci-Pattern sensemaking of this equation would focus on the relationship among current, voltage, and resistance (e.g., as resistance increases, current decreases) without describing the cause for this pattern. Often in scientific research, the pattern in a phenomenon is discovered and studied before the mechanism responsible for the pattern. Therefore, Sci-Mechanism is placed at the fourth level of the Science Sensemaking dimension.

Discussion of Sci-Mechanism sensemaking is not common in the literature (Etkina et al., 2006 ; Hestenes, 2010 ; Redish, 2017 ; Schuchardt, 2016 ; Schuchardt & Schunn, 2016 ). Causal relationships among variables in equations is often not explicit in canonical forms because the form of the equation hides the causal relationship. For example, Newton’s second law is often structured as \( {F}_{\mathrm{net}}=\mathrm{ma} \) , and interpreted as a pattern, the total force on a system in a specific direction is proportional to the acceleration in that direction. If, however, the equation is structured as \( \overrightarrow{a}={\overrightarrow{F}}_{net}/m \) , the arrangement of the variables fosters a mechanistic interpretation, the net force distributed over the mass of an object results in acceleration of the object, while the vector indicates the direction of the net force (Redish, 2017 ). In biology, one curriculum restructured a mathematical equation used to predict offspring outcomes from an expression of probability rules to “number of different offspring outcomes = (number of egg types) * (number of sperm types)” (Schuchardt & Schunn, 2016 ). This restructuring shifts the sensemaking focus to the mechanism for inheritance: any egg type can randomly join with any sperm type to produce offspring. Students who were instructed in this curriculum showed improved quantitative skills and conceptual understanding compared to students who were instructed in the use of mathematical algorithms or rules (Schuchardt & Schunn, 2016 ).

Categories within the mathematics sensemaking dimension

Mathematical equations in science do not just contain scientific meaning, they also contain mathematical meaning that can be accessed independently (Bain, Rodriguez, Moon, & Towns, 2019 ; Kuo et al., 2013 ; Sherin, 2001 ). Therefore, the Sci-Math Sensemaking Framework includes a separate mathematics sensemaking dimension. Categories of sensemaking of mathematics were derived from literature on mathematics education and the use of mathematics in science. Five categories emerged from the literature review that captured opportunities for sensemaking of mathematical knowledge. The definitions of these categories are provided in Table 2 , and each category will be illustrated below.

Math-Procedure sensemaking

The Math-Procedure category captures sensemaking that focuses on the procedural knowledge or algorithms for using mathematical equations to solve problems (Bain, Rodriguez, & Towns, 2019b ; Baroody, Feil, & Johnson, 2007 ; Becker et al., 2017 ; Bing & Redish, 2007 ; Case & Gunstone, 2003 ; Fan & Bokhove, 2014 ; Gupta & Elby, 2011 ; Haapasalo & Kadijevich, 2000 ; Hiebert & Lefevre, 1986 ; Hu & Rebello, 2013 ; Jacobs, Franke, Carpenter, Levi, & Battey, 2007 ; Karam, 2014 ; Kuo et al., 2013 ; Lehavi et al., 2017 ; Peled & Segalis, 2005 ; Pietrocola, 2009 ; Pospiech, 2019 ; Radmehr & Drake, 2019 ; Redish, 2017 ; Redish & Kuo, 2015 ; Rittle-Johnson & Schneider, 2015 ; Schuchardt, 2016 ; Schuchardt & Schunn, 2016 ; Star, 2005 ; Tsaparlis, 2007 ; Tuminaro & Redish, 2007 ; Uhden, Karam, Pietrocola, & Pospiech, 2012 ; Wells, Hestenes, & Swackhamer, 1995 ; Wink & Ryan, 2019 ). Procedural knowledge was first defined by Hiebert and Lefevre ( 1986 ) and has come to mean knowing the sequential steps in solving problems without having conceptual understanding (Baroody et al., 2007 ; Haapasalo & Kadijevich, 2000 ; Jacobs et al., 2007 ; Star, 2005 ). Peled and Segalis ( 2005 ) presented the subtraction procedure that students can engage in when solving the equation \( 310-164=\boxed{?} \) in a mathematics classroom. These steps included (1) borrowing from the tens column, (2) subtracting 4 from the ones column, (3) borrowing from the hundreds column, (4) taking away 6 from the tens column, and (5) taking away 1 from the hundreds column. The focus on procedural knowledge in mathematics classes has been criticized as one reason for students’ difficulty in understanding the meaning of equations or adopting efficient problem-solving strategies for new or complex problems (Cañadas, Molina, & del Río, 2018 ; Jacobs et al., 2007 ; Peled & Segalis, 2005 ). Similarly, in science classrooms, researchers have found that students tend to rely on algorithms without conceptual understanding of the science in solving problems and teachers tend to focus instruction on using mathematical procedures to do calculation (Bain, Rodriguez, & Towns, 2019b ; Bing & Redish, 2007 ; Hansson et al., 2015 ; Hu & Rebello, 2013 ; Kuo et al., 2013 ; Lehavi et al., 2017 ; Redish, 2017 ; Redish & Gupta, 2010 ; Redish & Kuo, 2015 ; Schuchardt, 2016 ; Schuchardt & Schunn, 2016 ; Tuminaro & Redish, 2007 ; Wink & Ryan, 2019 ). One description captured a high school physics instructor teaching the equation  F  =  EQ . After linking the variables in the equation to science entities, the teacher presented the steps of how to solve the problem mathematically and asked students to work on similar textbook problems (Hansson et al., 2015 ). The focus on mathematical procedures during instruction might be one reason why many students do not engage in sensemaking of mathematical concepts or make connections to the scientific phenomenon when problem-solving. This category has been placed at the first level in the math sensemaking dimension.

Math-Rule sensemaking

The Math-Rule sensemaking category identifies sensemaking of generalizable statements derived from fundamental mathematics principles which are used to guide calculation or decision-making (Baroody et al., 2007 ; Bing & Redish, 2007 ; Dixon, Deets, & Bangert, 2001 ; Haapasalo & Kadijevich, 2000 ; Hansson et al., 2015 ; Hiebert & Lefevre, 1986 ; Moss & Case, 1999 ; Njini, 2012 ; Radmehr & Drake, 2019 ). For example, the knowledge of divisibility rules such as a dividend is divisible by 5 if the last digit is 0 or 5 enables quick decision-making of whether a dividend is divisible (Potgieter & Blignaut, 2017 ). Rules can be used to guide the step-by-step calculation; however, compared to procedural knowledge, mathematical rules are more generalizable. For example, the rule pertaining to the order of mathematical operations applies to all types of calculation in all problem-solving processes, while the step-by-step procedure for different problems may vary depending on problem type. Because mathematical rules have greater generalizability than mathematical procedures but can still be employed without understanding the other levels, they are placed at the second level of the mathematics sensemaking dimension.

In the literature, references to using mathematical rules to make sense of mathematical equations in science is often found in descriptions of students’ problem-solving process rather than during instruction (Bing & Redish, 2007 ; Hansson et al., 2015 ; Hu & Rebello, 2013 ; Schuchardt & Schunn, 2016 ). When trying to understand the relation between the direction of velocity and the viscous force represented by the equation F v = − bv , students referred to the mathematical rule that “two negatives cancel out” (Bing & Redish, 2007 ). Similarly, Hansson et al. ( 2015 ) described how one student manipulated the equation ( \( mgh=\frac{m{v}^2}{2} \) ) by using the rule of division of fractions. In science classrooms, Schuchardt and Schunn ( 2016 ) describe an often-used approach to teaching inheritance where the instructor presents the probability rule that “If both events are required then multiply the probability of the two events together” to help students make sense of calculating the probability of producing offspring with specific combinations of genes.

Math-Structure sensemaking

In the Math-Structure sensemaking category, the focus is on understanding the arrangement of variables (symbols) and operations of the mathematical equations (Bain, Rodriguez, & Towns, 2019a , 2019b ; Becker & Towns, 2012 ; Bing & Redish, 2007 ; Brahmia et al., 2016 ; Cañadas et al., 2018 ; Hestenes, 2010 ; J. Hiebert & Lefevre, 1986 ; Hu & Rebello, 2013 ; Izsák, 2004 ; Jacobs et al., 2007 ; Karam, 2014 ; Karam & Krey, 2015 ; Kirshner, 1989 ; Kuo et al., 2013 ; Moss & Case, 1999 ; Pospiech, 2019 ; Redish, 2017 ; Redish & Kuo, 2015 ; Rodriguez et al., 2019 ; Rodriguez et al., 2018 ; Schuchardt & Schunn, 2016 ; Sherin, 2001 ). The idea of mathematical structure has often been discussed in science education literature from the perspective of symbolic forms, which was proposed by Sherin ( 2001 ) as “the particular arrangement of symbols in an equation [that] expresses a meaning that can be understood” (p. 480). Because interpretation of the mathematical structure depends on knowing the symbolic arrangements in particular contexts, greater sensemaking is required than when applying mathematical rules. Therefore, Math-Structure sensemaking is placed at the third level in the Mathematics sensemaking dimension.

Math-Structure sensemaking emphasizes the number and location of the variables and operations in the equation. Compared to the typical addition structure, 3 + 4 + 5 + 3 = _, mathematics students are more likely to offer an incorrect answer when the equation is structured as 3 + 4 + 5 = 3 + _ (McNeil & Alibali, 2004 ). In the development of mathematical representations of scientific phenomena, knowledge of mathematical structures provides resources for scientists to organize mathematical symbols and operations to represent the target relationship in the phenomenon (Pospiech, 2019 ; Redish & Kuo, 2015 ). Sherin ( 2001 ) proposed that students use “symbolic forms” to make sense of physics equations. Equations in the symbolic form of □+□ have a structure of two components adding together. Sherin ( 2001 ) provides an example of how students use their knowledge of the mathematical structure of equations to express an idea from their observations of a physical phenomenon that friction consists of two components.

Students tend to memorize the structure of canonical equations without conceptual understanding of the mathematics which leads to their difficulty in choosing or developing a meaningful equation for the target scientific phenomenon (Bain, Rodriguez, Moon, & Towns, 2019 ; Becker & Towns, 2012 ; Redish, 2017 ; Rodriguez et al., 2018 ; Sherin, 2001 ). For example, Bain, Rodriguez, Moon, and Towns ( 2019 ) show that students tend to conflate the ideas of rate constant and equilibrium constant because of similarities in the structure of the equations. One student expresses the difficulty as “It just seems that everything is the same almost, and it’s hard to distinguish each equation and each principle” (Bain, Rodriguez, Moon, & Towns, 2019 , p. 1573). This quote suggests that the student recognizes that the equations look the same (have the same structure) but realizes that they represent different concepts (principles).

Math-Relation sensemaking

Math-Relation sensemaking refers to understanding the quantitative relationships expressed in the equation (Bain, Rodriguez, & Towns, 2019a ; Baroody et al., 2007 ; Becker et al., 2017 ; Becker & Towns, 2012 ; Cañadas et al., 2018 ; Carlson et al., 2002 ; Dixon et al., 2001 ; Hestenes, 2010 ; Izsák, 2004 ; Izsák & Jacobson, 2017 ; Jacobs et al., 2007 ; Karam, 2014 ; Kuo et al., 2013 ; Lazenby & Becker, 2019 ; Lehavi et al., 2017 ; Levy & Wilensky, 2009 ; Moss & Case, 1999 ; Pietrocola, 2009 ; Pospiech, 2019 ; Redish, 2017 ; Redish & Kuo, 2015 ; Rodriguez et al., 2018 ; Rodriguez et al., 2019 ; Schuchardt, 2016 ; Sherin, 2001 ; Smidt & Weiser, 1995 ; Thompson & Carlson, 2017 ; Tuminaro & Redish, 2007 ; Uhden et al., 2012 ; Von Korff & Sanjay Rebello, 2014 ; Wink & Ryan, 2019 ). Carlson et al. ( 2002 ) defined covariational reasoning as attending to the way in which two variables change with respect to one another. For example, in the equation y = 2 x , the math-relation embedded in the equation is that y increases 2-fold for every unit increase in x . An understanding of quantitative relationships is often built on an understanding of the mathematical structure of the equation (Bassok, Chase, & Martin, 1998 ). However, the mathematical structure is often not referred to during sensemaking of quantitative relationships, perhaps because the knowledge of mathematical structure is intuitive and not explicitly available to students. Because Math-Relation sensemaking of an equation is built on understanding the mathematical structure, Math-Relation is placed on the fourth level in the mathematics sensemaking dimension.

Math-Relation sensemaking (coordination of relationship between quantities) has been conflated in the literature with Sci-Pattern sensemaking (coordination of the relationship between properties of a scientific phenomenon) (e.g., Carlson et al., 2002 ). We have distinguished them in this framework because as in the example of the equation for a line, y  = 2 x , quantitative coordination can occur separately from any knowledge of connection to real-world measures. Moreover, when discussing sensemaking of mathematical equations in science classrooms, it has been observed that students tend to limit their sensemaking to the Math-Relation sensemaking space (Becker & Towns, 2012 ; Izsák, 2004 ; Lehavi et al., 2017 ; Svoboda & Passmore, 2013 ; Wink & Ryan, 2019 ). In Izsák’s study, two students developed a mathematical equation to represent the relationship of turns of a crank handle to the distance a weight is moved, and they discuss the phenomenon entirely in terms of quantitative relationships “Zero inches moves 4 point 5 inches per crank. And the weight starting at 14 inches only moves 3 inches.” (Izsák, 2004 , p. 494) There is no discussion of the physics of the phenomenon. In another study (Lehavi et al., 2017 ), the teacher expressed concern that “for students who hold the mathematical conceptualization, time, speed and distance are merely three quantities related by an equation” even when the teacher tried to use a teaching strategy to move students into physical understanding of the equation. In this case, students were limiting their sensemaking to the quantitative relationships between time, speed, and distance. They were not connecting to the Sci-Pattern sensemaking that speed, time, and distance are physical properties of a scientific phenomenon that have a logical relationship to one another: if speed increases, the distance traveled in a specific time period will also increase.

Math-Concept sensemaking

Math-Concept sensemaking focuses on a network of knowledge that enables explanation of the what, how, and why of a mathematical idea, referred to as conceptual knowledge (Baroody et al., 2007 ; Even, 1990 ; Fan & Bokhove, 2014 ; Fuson et al., 1997 ; Haapasalo & Kadijevich, 2000 ; Hiebert & Lefevre, 1986 ; Hu & Rebello, 2013 ; Jacobs et al., 2007 ; Moss & Case, 1999 ; Peled & Segalis, 2005 ; Radmehr & Drake, 2019 ; Star, 2005 ; Thompson & Carlson, 2017 ). For example, a conceptual understanding of probability for two independent events A and B cooccurring includes understanding what probability means, why the individual probabilities for the two events are multiplied, and when to perform this calculation and why. Sensemaking of mathematical concepts is the prerequisite for reasoning and justification in mathematics problem-solving (Peled & Segalis, 2005 ).

Students struggle with conceptual understanding of various mathematical ideas in mathematics classrooms (Even, 1990 ; Jacobs et al., 2007 ; Moss & Case, 1999 ). Incorrect or incomplete understanding of the concepts can lead to adoption of incorrect procedures and rules, or difficulty in solving novel problems (Jacobs et al., 2007 ). For example, students sometimes provide 93 as the answer for \( 57+36=\boxed{?}+34 \) , instead of 59. This error indicates that they are treating the equals sign as a signal to carry out the calculation that precedes it instead of treating it as an indicator of a relationship between the two sides of the equation (Jacobs et al., 2007 ). Concept-based reasoning in mathematics can lead to more efficient problem-solving. Peled and Segalis ( 2005 ) investigated students’ problem-solving with subtraction. When students were asked to solve a word problem for the time difference between “one week, 5 days, and 18 hours” and “2 weeks, 3 days and 4 hours,” students who applied a more conceptual strategy were more successful than those who applied a rules-based approach that dictated that all units of time needed to be converted to the same unit. The difficulty that students have with conceptual understanding in mathematics has been attributed to a focus in instruction on procedures over concepts (Hill et al., 2005 ).

Math-Concept sensemaking in science classrooms is relatively underexplored. One biology curriculum developed by Schuchardt and Schunn ( 2016 ) seeks to have students understand the concept of probability in the context of inheritance as the number of desired events out of all possible events. Students who participated in this curriculum showed improved ability to solve novel and complex probability problems situated in inheritance compared to students who were not exposed to this curriculum. Reflecting the mathematics education literature, Math-Concept is placed at the highest level of the mathematics sensemaking dimension.

The Sci-math sensemaking framework is informed by research across multiple fields

The categories of sensemaking identified in this paper are drawn from literature from several fields including studies of mathematics in science education, and mathematics education. Research from physics, chemistry, and biology was synthesized to identify the four categories in the science sensemaking dimension of the Sci-Math Sensemaking Framework. Therefore, the types of science sensemaking that have been identified are expected to apply across different disciplines. Additionally, evidence from both mathematics education and science education was used to generate each category in the mathematics sensemaking dimension of the framework. These categories represent a synthesis of ideas from both fields. Therefore, the Sci-Math Sensemaking Framework is expected to provide a common structure for education studies on mathematics in science contexts. The availability of a common structure will enable descriptions of instruction in sensemaking and students’ sensemaking of equations during problem-solving to be compared across disciplines. Comparative studies will permit the abstraction of common principles that aid in sensemaking as well as the development of testable models of how students engage in sensemaking and how sensemaking of equations affects students’ problem-solving.

Sensemaking opportunities on the science dimension

Categorization of the science sensemaking opportunities when working with mathematical equations in science offers a framework for exploring the different types of sensemaking that occurs in science classrooms. The four sensemaking categories on the science dimension emerged from literature on the process of mathematical modeling in science (Etkina et al., 2006 ; Hestenes, 2010 ; Izsák, 2004 ; Lazenby & Becker, 2019 ; Lehrer & Schauble, 2010 ; Levy & Wilensky, 2009 ; Michelsen, 2015 ; Redish, 2005 , 2017 ; Redish & Gupta, 2009 ; Redish & Kuo, 2015 ; Uhden et al., 2012 ), descriptions of students’ sensemaking of mathematics in science during problem-solving (Hu et al., 2013 ; Rodriguez et al., 2018 ; Sherin, 2001 ), and descriptions of instructional approaches to teaching mathematics in science (Lehrer & Schauble, 2010 ; Michelsen, 2015 ; Schuchardt & Schunn, 2016 ).

Sensemaking opportunities on the mathematics dimension

The categories identified in the mathematics sensemaking dimension of the Sci-Math Sensemaking Framework provide a nuanced description of the multiple types of mathematics sensemaking that can occur when working with mathematical equations in science. The teaching and learning of mathematical procedures, rules, structure, relation, and concepts have been widely discussed in mathematics education studies (Baroody, 2003 ; Cañadas et al., 2018 ; Hiebert & Lefevre, 1986 ; Moss & Case, 1999 ; Peled & Segalis, 2005 ; Star, 2005 ). The use of mathematics in science offers the opportunity for students to practice mathematics as mathematicians, thus developing their mathematical thinking (Schoenfeld, 1992 ). In science, development of canonical mathematical expressions in science involves selection of a mathematical structure, including the specific variables and their arrangement, that best represents a specific scientific idea (Borromeo Ferri, 2006 ; Dukerich, 2015 ; Diaz Eaton et al., 2019 ; Gouvea & Passmore, 2017 ; Halloun, 2007 ; Hestenes, 2010 ). However, during instruction of mathematical equations in science, the rich knowledge in mathematics is often neglected (Hansson et al., 2015 ; Lazenby & Becker, 2019 ; Redish & Kuo, 2015 ; Svoboda & Passmore, 2013 ). By including a separate mathematics sensemaking dimension, the Sci-Math Sensemaking Framework emphasizes the importance of mathematics sensemaking as a means for students to grapple with the represented science concepts (Bain, Rodriguez, Moon, & Towns, 2019 ; Brahmia et al., 2016 ; Sherin, 2001 ).

Relationship among different categories of sensemaking

The categories within the dimensions of the Sci-Math Sensemaking Framework have been organized to represent increasingly sophisticated levels of sensemaking from Sci-Label to Sci-Mechanism in the science sensemaking dimension and from Math-Procedure to Math-Concept in the mathematics sensemaking dimension. These levels have been theorized based on the referenced literature and on logic. For example, in the literature of science education, mechanistic reasoning is thought to reflect a deeper understanding of the scientific phenomenon than sensemaking of the labels of the entities or of the pattern in the phenomenon. (Machamer et al., 2000 ; Illari & Williamson, 2012 ; Russ et al., 2008 ). Logically, understanding Sci-Mechanism requires identifying associations between variables in the mathematical equation and properties of the scientific phenomenon. However, the placement of some of these levels (e.g., Sci-Description below Sci-Pattern) needs to be assessed by additional research.

During the interpretation or instruction of one equation, multiple types of sensemaking may occur simultaneously. With reference to the density equation, Sci-Description sensemaking can only occur after the referents of the variables have been understood (Sci-Label sensemaking), and Sci-Description sensemaking can occur together with understanding the patterns represented in the equation (Sci-Pattern). Similarly, in the mathematics dimension, Math-Concept is the most advanced type of sensemaking, but Math-Concept sensemaking may occur in conjunction with application of procedures and rules and sensemaking of mathematical structures.

In science classrooms, little priority is placed on Sci-Mechanism or Math-Concept sensemaking of mathematical equations (Bing & Redish, 2009 ; Schuchardt & Schunn, 2016 ; Stamovlasis et al., 2005 ). Sensemaking at these higher levels has shown promise with elevating students’ understanding of science concepts and their ability to solve quantitative problems (Mestre, Docktor, Strand, & Ross, 2011 ; Schuchardt & Schunn, 2016 ; Taasoobshirazi & Glynn, 2009 ). However, this does not mean that instruction of mathematical equations needs to always occur at these higher levels. For example, if the goal is to rapidly develop students’ ability to quickly solve problems of the same type, then Sci-Label and Math-Procedure may be most efficient. Additionally, some equations can only enable a Sci-Description or Sci-Pattern sensemaking (e.g., density equation or diversity index).

Sci-math Sensemaking framework to identify opportunities for blended sensemaking

Specifying the sensemaking occurring in each of the two dimensions of the Sci-Math Sensemaking Framework will permit identification and description of opportunities provided for blended sensemaking (Fauconnier & Turner, 1998 ). For example, students who rely on algorithms without connection to the scientific knowledge embodied in the equations (Becker & Towns, 2012 ; Bing & Redish, 2009 ; Case & Gunstone, 2003 ; Kuo et al., 2013 ; Stewart, 1983 ) are using Sci-Label sensemaking to identify the variables combined with Math-Procedure to solve the problem following a prescribed step-by-step process. These two types of sensemaking provide little opportunity for blended sensemaking, and students using these two types of sensemaking have difficulty applying the mathematical equation to different contexts (Redish, 2017 ; Stewart, 1983 ; Taasoobshirazi & Glynn, 2009 ). On the other hand, when students combine sensemaking of mathematical structures of an equation with sensemaking of mechanisms responsible for the scientific phenomenon, they are blending two types of sensemaking (Math-Structure and Sci-Mechanism) (Sherin, 2001 ). Students may start sensemaking of mathematical equations from either the mathematics sensemaking dimension or the science sensemaking dimension. In chemistry, accessing sensemaking of an equation from either dimension could result in students moving to the other dimension for a richer understanding of the equation (Bain et al., 2018 ). Instruction that provides opportunities for blended sensemaking has been shown to improve students’ understanding of the scientific phenomenon and their ability to solve complex and novel quantitative problems (Schuchardt & Schunn, 2016 ). Additionally, those with more experience in a field are more likely to apply blended sensemaking to and be more successful at solving quantitative problems, than those with less experience (Redish, 2017 ).

Relationship between sensemaking opportunities and pedagogical strategies

The types of sensemaking opportunities of mathematical equations in science are often related to the pedagogical strategies that are used. Evidence from innovative instructional approaches synthesized in this review shows that instruction which has students develop mathematical equations to model scientific phenomena can create opportunities for students to engage in higher levels of sensemaking, including Sci-Description (Lehrer & Schauble, 2010 ), Sci-Pattern (Baxter et al., 2014 ), Sci-Mechanism (Schuchardt & Schunn, 2016 ), Math-Structure (Izsák, 2004 ), and Math-Concept (Schuchardt & Schunn, 2016 ). However, this relationship is not absolute. Sensemaking can occur in classes taught by different methods. Students can spontaneously engage in Sci-Mechanism and Math-Structure sensemaking after having equations provided to them during instruction (Mestre et al., 2011 ; Redish, 2017 ; Stewart, 1983 ; Taasoobshirazi & Glynn, 2009 ). By separating sensemaking from pedagogical strategies, it is possible to investigate whether different teaching methods can promote or limit the type of sensemaking opportunities that occur during students’ quantitative problem-solving.

Limitations

The categories of sensemaking of equations in science that are presented in the Sci-Math Sensemaking Framework are drawn from published literature. Moreover, the scientific disciplines that were included were only biology, physics, and chemistry, excluding disciplines such as geology. It is possible that other sensemaking opportunities will be discovered during analysis of instruction in different contexts or in investigation of scientists’ use of mathematics in their work. The framework is intended to be modifiable to allow addition of new categories.

Conclusions

This Sci-Math Sensemaking Framework is generated from a systematic literature review that combines theoretical and empirical evidence on the teaching and learning of equations in mathematics and science. The categories developed in this study capture sensemaking opportunities of equations in science that has rarely been studied. This framework can provide a consistent way for researchers to compare sensemaking of mathematical equations in science across studies. The framework is intended to be used by researchers to examine students’ interpretation and application of mathematical equations as well as the sensemaking opportunities created during class by instructors. This framework may also be used by instructors to reflect on their own teaching, to examine whether the sensemaking opportunities provided in class align with their learning objectives.

Availability of data and materials

Not applicable.

Abbreviations

Mathematics Integrated into Science: Classroom Observation Protocol

Science, Technology, Engineering and Mathematics

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Acknowledgements

We would like to thank the Schuchardt/Warfa research group members for providing constructive feedback on the work. We also appreciated Professor Gillian Roehrig’s feedback on this manuscript .

This study was funded by start-up funds awarded to the AS by the University of Minnesota. The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript should be declared.

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Zhao, F., Schuchardt, A. Development of the Sci-math Sensemaking Framework: categorizing sensemaking of mathematical equations in science. IJ STEM Ed 8 , 10 (2021). https://doi.org/10.1186/s40594-020-00264-x

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Sensemaking

At NSTA, our mission is to transform science education to benefit all through professional learning, partnerships and advocacy.

When students-as-scientists have authentic, relevant opportunities to actively make sense of the world and beyond - what we call sensemaking -  science learning becomes engaging, accessible and important to ALL students.

What are the attributes of sensemaking?

NSTA describes four critical attributes of sensemaking: phenomena, science and engineering practices, student ideas and science ideas (grade-appropriate disciplinary core ideas). The highest-quality lesson plans exhibit all four of these attributes. Lesson plans that leverage one or more of these attributes are considered quality works-in-progress. Below you’ll find curated collections of resources for each of the four attributes of sensemaking. Whether you are a novice or expert in sensemaking , we believe you’ll find what you need to support you in implementing or revising existing lesson plans and designing your own lessons. We also suggest using the NSTA  Sensemaking Tool for Equity to vet lessons, provide colleagues with feedback or suggestions for improving lessons, and/or guide lesson revisions and design.

Sensemaking Tool

Curated Resources

Leveraging Assets to Build on Student Experiences: A Focus on Curiosity

Local Phenomena

Phenomena Drive Instruction: How to Choose and Us

Using phenomena to promote equity in science instruction

Talking as Scientists: Engaging Students in Science and Engineering Practices

Who Is the “Knower” in the Classroom? Learning as Sensemaking in a Globally Connected World

Teaching and Learning Through Storylines

Student Ideas

Let Me Tell You My Idea About That! Empowering Students to Make Their Thinking Visible

How to Get Your Students to Start Talking! Supporting Equitable Participation in the Science Classroom

Making Student Thinking Visible

Science Ideas

What is sensemaking.

Sensemaking is actively trying to figure out how the world works (science) or how to design solutions to problems (engineering). Students do science and engineering through the science and engineering practices. Engaging in these practices necessitates students be part of a learning community to be able to share ideas, evaluate competing ideas, give and receive critique, and reach consensus. Whether this community of learners is made up of classmates or family members, students and adults build and refine science and engineering knowledge together.

Natural phenomena are observable events that occur in the universe and that we can use our science knowledge to explain and predict. The goal of building knowledge in science is to develop general ideas, based on evidence, that can explain and predict phenomena. ( STEM Teaching Tool #42: Using Phenomenon in NGSS-Designed Lessons and Units )

In sensemaking lessons, students experience a phenomenon together (firsthand or through video, images, graphs, maps, etc.) and share their observations and wonderings with the class. The focus of the lesson is pursuing an answer to a question students shared; the answer to which requires students to develop a targeted science idea needed to explain how or why the phenomenon occurred. 

Science and Engineering Practices

Engaging in the practices of science helps students understand how scientific knowledge develops; such direct involvement gives them an appreciation of the wide range of approaches that used to investigate, model and explain the world. Engaging in the practice of engineering likewise helps students understand the work of engineers…The actual doing of science or engineering can pique students’ curiosity, capture their interest, and motivate their continued study . ( A Framework for K-12 Science Education , pp 42-43)

Sensemaking lessons require students to engage in elements of science and engineering practices (discrete pieces of knowledge and skills that make up the practice and are grade-band specific) to make sense of the science ideas needed to explain the how or why of the phenomenon.

Science is social! Students can’t fully engage in the science and engineering practices to build the science ideas needed to explain how or why a phenomenon occurs without opportunities to share ideas, build on each other’s ideas, provide each other feedback, and change their minds. 

Additionally, students’ ideas – conveying their prior knowledge, experience as members of a family and other communities, interests and curiosities – can be leveraged to move the whole class’ learning forward. Students are aware their ideas are valued and important.

Science ideas (elements of disciplinary core ideas) are fundamental ideas that are necessary for understanding a given science discipline. The core ideas all have broad importance within or across science and engineering disciplines, provide a key tool for understanding or investigating complex ideas and solving problems, relate to societal or personal concerns, and can be taught over multiple grade levels at progressive levels of depth and complexity. ( https://www.nextgenscience.org/glossary/disciplinary-core-idea-dci ) 

Pursuing a question students raise about a phenomenon they have experienced together; students engage in science and engineering practices to make sense of targeted science ideas they need to explain how or why the phenomenon occurs.

Quality Examples

Nsta instructional materials.

Visit the NSTA Instructional Materials page for high quality sensemaking examples.

Making sense of sensemaking: using the sensemaking epistemic game to investigate student discourse during a collaborative gas law activity

First published on 8th December 2020

Beyond students’ ability to manipulate variables and solve problems, chemistry instructors are also interested in students developing a deeper conceptual understanding of chemistry, that is, engaging in the process of sensemaking. The concept of sensemaking transcends problem-solving and focuses on students recognizing a gap in knowledge and working to construct an explanation that resolves this gap, leading them to “make sense” of a concept. Here, we focus on adapting and applying sensemaking as a framework to analyze three groups of students working through a collaborative gas law activity. The activity was designed around the learning cycle to aid students in constructing the ideal gas law using an interactive simulation. For this analysis, we characterized student discourse using the structural components of the sensemaking epistemic game using a deductive coding scheme. Next, we further analyzed students’ epistemic form by assessing features of the activity and student discourse related to sensemaking: whether the question was framed in a real-world context, the extent of student engagement in robust explanation building, and analysis of written scientific explanations. Our work provides further insight regarding the application and use of the sensemaking framework for analyzing students’ problem solving by providing a framework for inferring the depth with which students engage in the process of sensemaking.

Introduction

Since the process of sensemaking involves an iterative building of ideas, a collaborative learning environment serves as a well-suited context for investigating students’ sensemaking. Active learning pedagogies that utilize a collaborative learning environment have been widely incorporated into undergraduate chemistry courses due to their immense benefit to student learning ( Freeman et al. , 2014 ; Theobald et al. , 2020 ). One common active learning pedagogy applied in the chemistry community is Process Oriented Guided Inquiry Learning (POGIL). Recently, our research group synthesized the published research surrounding POGIL, noting the overwhelming use of pre/post assessments to measure the effects of POGIL but a limited amount of research into the specific features of POGIL activities that help facilitate the observed positive learning gains ( Rodriguez et al. , 2020b ). Our review showcases the need for qualitative work that can elucidate the specific features of active learning pedagogies that promote student learning. More than recognizing that these interventions improve student learning, further research is needed to address the ways in which active learning approaches influence student learning and support students in doing more than simply providing an answer.

Although the existing literature on student engagement in active learning pedagogies has provided a wealth of insight for instructors, analyzing student discourse using sensemaking will augment the existing literature by providing a new lens to uncover the role of collaborative activities in promoting student learning. We are interested in the extent to which collaborative learning promotes student movement beyond answer-making and towards the process of sensemaking. Therefore, we are interested in addressing the following research question: In what ways do students engage in the process of sensemaking during a collaborative gas law activity?

Review of relevant literature

Process oriented guided inquiry learning.

Our recent literature review analyzed the extant research surrounding POGIL, pointing to the predominant use of pre/post assessments to gauge the effect of POGIL interventions, which indicates improvement in student learning across different dimensions ( Rodriguez et al. , 2020b ). Aside from the pre/post assessments, there has been less research focused on the features of a POGIL classroom such as student roles, process skills, and the role of the facilitator in promoting student learning. Outside research using pre/post assessments, the existing qualitative studies have commonly analyzed student discourse using Toulmin's Argumentation Pattern (TAP) model to make inferences about learning from students’ argumentation ( Daubenmire and Bunce, 2008 ; Becker et al. , 2013, 2015 ; Kulatunga and Lewis, 2013 ; Kulatunga et al. , 2014 ; Daubenmire et al. , 2015 ; Moon et al. , 2016, 2017b, 2017a ; Stanford et al. , 2016, 2018 ). With regards to the structure of POGIL activities, there have been a few studies that analyzed the ways in which the learning cycle influenced arguments generated by students ( Kulatunga et al. , 2014 ), promoted use of causal reasoning ( Moon et al. , 2016, 2017a ), and the ways question language guided coordination of students’ responses across the chemistry triplet ( Stanford et al. , 2018 ). The work by Kulatunga et al. (2014) showed that the learning cycle scaffolds students in constructing arguments especially for the application phase, and that argumentation was limited during the exploration phase, due to the large use of directed questions. Other research has shown that the students are limited in the causal reasoning they employ and that there is a clear link between the question prompt and depth of explanations provided by students ( Moon et al. , 2016, 2017a ; Stanford et al. , 2018 ).

The ideal gas law

In the existing literature related to gas laws, research has commonly focused on students’ conceptual understanding of specific gas law problems and general problem solving. For instance, Nurrenbern and Pickering's (1987) foundational work showed that general chemistry students do not perform as well when problems gas law problems are framed conceptually rather than algorithmically, with follow-up studies by other researchers making supporting these claims ( Pickering, 1990 ; Sawrey, 1990 ; Nakhleh, 1993 ; Nakhleh and Mitchell, 1993 ; Zoller et al. , 1995 ; Nakhleh et al. , 1996 ; Stamovlasis et al. , 2005 ; Cracolice et al. , 2008 ; Sanger et al. , 2013 ). In a similar vein, Madden et al. (2011) noted that students will also solve problems algebraically without displaying underlying conceptual understanding. Additionally, Matijaevi et al. (2016) pointed to students’ difficulty in translating gas law equations to pictorial representations which further highlights their limited conceptual understanding of the laws. Other research focused on students’ conceptions of gas laws has uncovered a general lack of deep conceptual reasoning behind the behavior of gases, more specifically with regards to the particulate nature of gases and how to relate the behavior of gases to physical situations ( De Berg, 1995 ; Lin et al. , 2000 ; Kautz et al. , 2005a, 2005b ; Robertson and Shaffer, 2013, 2016 ). Moreover, Schuttlefield et al. (2012) , detailed the specific features of problems that led students to incorrect answers, finding that number format ( i.e. , scientific, general, decimal) and unit conversions (for variables like volume and temperature) were particularly challenging for students. In an effort to understand student's problem-solving approaches when working with gas laws, Tang and Pienta (2012) characterized the process of students’ problem solving into three stages: problem reading, problem planning, and calculation. They found that unsuccessful students spent larger amounts of time in the planning phase, while both successful and unsuccessful students spent similar amounts of time in the reading phase; suggesting students need scaffolding to move them past the planning stage and into the calculation phase ( Tang and Pienta, 2012 ). Chen et al. (2019) also analyzed students problem-solving through the lens of systems thinking, showing that students struggle to incorporate higher-order systems thinking ability and fail to retrieve essential conceptual material related to gas law problems.

In summary, the research into students’ reasoning with ideal gas law problems suggests students need more scaffolded tasks that engage them in deeper conceptual understanding. A common method used in chemistry contexts that reinforces conceptual knowledge is the use of dynamic, particulate-level simulations that students can use to actively investigate molecular interactions. Studies have shown that dynamic, particle-level representations improve students’ conceptual knowledge ( Williamson and Abraham, 1995 ; Sanger et al. , 2000 ; Tasker and Dalton, 2006 ; Akaygun and Jones, 2013a, 2013b ). Given this research, our activity was designed to focus on a conceptual understanding by having students use a dynamic simulation to construct the ideal gas law and investigate limitations of the model.

Theoretical perspectives

Sensemaking.

In this study we adopt the definition Odden and Russ (2019a) proposed for sensemaking, “a dynamic process of building or revising an explanation in order to ‘figure something out’—to ascertain the mechanism underlying a phenomenon in order to resolve a gap or inconsistency in one's understanding.” Their definition combined the three literature strands discussed in this section by acknowledging that sensemaking entails integration of new knowledge into old (cognitive) and the dynamic process of building an explanation (discursive) with the ultimate goal of “figuring something out” (framing). This definition also aligns with a further development in defining the sensemaking process where Odden and Russ (2018) framed sensemaking as an epistemic game. Epistemic games were initially proposed by Collins and Ferguson (1993) as a way of describing the ways in which inquiry unfolds ( i.e. , the generation of new knowledge), where game is a reference to the coherent set of “rules” an individual utilizes in a particular context and epistemic is a reference to the type of ideas and assumptions that influence and mediate the construction of knowledge. According to Tuminaro and Redish (2007) , epistemic games can be identified and distinguished from one another based on the presence of ontological and structural components. The ontological components involve (1) a knowledge base, which reflects the cognitive resources relevant for the game ( e.g. , mathematical reasoning, physics or chemistry principles, etc. ) and (2) an epistemic form, which can be described as the target structure used to guide inquiry, that is, the desired product or outcome that motivates continued engagement in the epistemic game. The structural components of an epistemic game involve the specific conditions that begin and end an epistemic game (entry condition and ending condition) and the actions taken within a particular game (moves).

The epistemic games framework has previously been applied to chemistry ( Sevian and Couture, 2018 ; Rodriguez et al. , 2020a ) and physics ( Tuminaro and Redish, 2007 ; Chen et al. , 2013 ) contexts. Types of epistemic games proposed by Tuminaro and Redish (2007) in a physics problem solving context include: mapping meaning to mathematics, recursive plug and chug, and the physical mechanism game. For example, a game named “pictorial analysis” has a target epistemic form that involves a knowledge base rooted in physics principles and an epistemic form centered on the generation of a representation that identifies spatial relationships within a problem statement such as a free-body diagram, circuit diagram, or schematic of a physical situation. Specific moves that occur in this game involve identifying a target concept, choosing an external representation, telling a conceptual story based on relations among objects, and filling the gaps of the representation ( Tuminaro and Redish, 2007 ). Relevant to this work, another epistemic game that has been described is the “answer-making” game, in which the moves of the epistemic game involve students either (1) trying to remember the answer to a problem and subsequently justify this answer using mathematics and conceptual reasoning or (2) using mathematics and conceptual reasoning to reach an answer ( Chen et al. , 2013 ). This epistemic game is in contrast to the “sensemaking epistemic game”, with both games provided in Fig. 1 ( Chen et al. , 2013 ; Odden and Russ, 2018 ). For clarity, in Fig. 1 we have depicted a simplified version of the answer-making game—illustrating the scenario where students do not remember the answer—in order to highlight similarities and differences with the sensemaking epistemic game. The sensemaking epistemic game begins by students (0) gathering initial knowledge before (1) noticing a gap in understanding followed by (2) iteratively building an explanation that ultimately results in (3) resolving the gap in understanding. Importantly, the answer-making epistemic game is different from the sensemaking epistemic in terms of the framing of the task—for the answer-making epistemic game, rather than focusing on understanding the problem, there is an attempt to remember something they already know ( i.e. , brainstorming frame) and to provide a sufficient solution to complete the task ( i.e. , oral examination frame) ( Russ et al. , 2012 ; Odden and Russ, 2019b ). Using the language of epistemic games, one of the key differences between sensemaking and answer-making is the target epistemic form, in which the goal of sensemaking is understanding a problem and the goal of answer-making is completing a problem. In the next section we detail how this body of literature played a role in adapting the sensemaking framework as an analytical lens.

Research setting

Activity development.

Previously published gas law POGIL activities involved presenting students with the ideal gas law and prompting them to investigate the different relationships among variables in the expression ( e.g. , P , V , n , T ) ( Hanson, 2011 ; Moog and Farrell, 2017 ). Instead, for this study, our activity relied on students using an interactive simulation from the Concord Consortium that allowed them to investigate the relationship between variables included in the ideal gas law ( e.g. , temperature and pressure) as well as those included in the van der Waals equation ( e.g. , particle size and particle attraction) ( The Concord Consortium, 2020 ). A reproduction of this simulation is provided below in Fig. 2 . The students used this simulation to gather data to draw inferences about the influence of various variables ( i.e. , temperature, pressure, number of particles, particle size, particle attraction, and particle mass) on the volume of a gas within each of the pistons.

The overarching question guiding students in the first learning cycle is a scenario based on the National Football League's 2014 American Football Conference (AFC) Championship Game scandal known commonly as “Deflate-gate.” This game was widely covered on the news because of reports that a team had purposefully deflated game footballs, while others argued that due to the extremely cold temperatures the footballs deflated without external influence. In the trial that followed, data about pressure and temperature were provided, making this scenario an attractive topic for an application question investigating a real-world scenario. In the exploration phase of the activity, students use the simulation to explore the effect of the number of particles, temperature, and pressure on the volume of an ideal gas. In the concept invention phase, students construct the ideal gas law and determine the value of the gas constant R . Finally, in the application phase students use the newly constructed equation to draw conclusions regarding whether the data support the idea that the New England Patriots cheated by intentionally underinflating footballs.

The second learning cycle of the activity focuses on the idea that models may be changed or different models may be necessary by having students examine the assumptions of the ideal gas law. In the exploration phase, students consider the effect of variables not included in the ideal gas law: particle mass, particle size, and particle attraction. Then, during the concept invention phase, the students predict the effect of these variables under certain conditions and complete a table that emphasizes the influence the variables may have on the ideal gas law equation under different conditions. In the final application phase, students are prompted to reason about the relationship between a real and an ideal gas.

Data collection

Data analysis, developing a sensemaking coding scheme: identifying the structural components of the sensemaking game.

As part of this process, we developed code definitions related to determining what constituted as an entry condition in the context of our activity. Odden and Russ (2018) described the entry condition as a point in the dialogue where a student notices an inconsistency between old and new knowledge, which transitions the students into a sensemaking game with the ultimate goal being to resolve the inconsistency. For our study context, we initially made the assumption that the questions of the activity serve as the gap in understanding students were trying to resolve; therefore, students vocalizing these questions served as a sufficient entry condition. Moreover, because we considered restating the question to serve as the entry condition, we did not require building knowledge framework to be a necessary precursor to sensemaking and this element was not included in our analysis. This was done because we found that students did not gather initial knowledge before beginning a question, but rather the question served as the entry condition, initiating the sensemaking process. The extent to which these assumptions are valid were subsequently addressed as part of the second layer of analysis, discussed in the next section.

The final code definitions are presented in Table 2 and once the definitions were refined; they were used as a deductive coding scheme by the primary researcher to identify potential instances of sensemaking across each group. This analysis was done on a question-by-question basis, with each question serving as the unit of analysis ( Campbell et al. , 2013 ). For the purposes of this study, we considered a potential episode of sensemaking to be when students engaged in the following steps (in order): entry condition, explanation building, and resolution.

Analytic memoing: identifying the epistemic form of the sensemaking game

In order to characterize the quality of students’ explanations (3), we used the framework of scientific explanations developed and outlined in McNeill et al. (2006) . This aligned well with the sensemaking epistemic game which is focused on students’ understanding phenomena because, as outlined by McNeill et al. (2006) , scientific explanations deal with students working to explain how or why a phenomena occurs. Within this model, an explanation contains: a claim ( i.e. , conclusion), which is then supported by evidence to support the claim, and finally reasoning to connect the claim to the evidence. Within our dataset we required that in order to code a passage for reasoning the response must have already been coded with claim and evidence . The written responses analyzed were those collected specifically from the students who served as the Recorder, whose role was to document student group responses as they worked through the activity.

As discussed by Birks et al. (2008) , engaging in the process of memoing is a useful analytic strategy for extracting meaning from qualitative data. We found the memoing process to be more productive and generative (as opposed to coding) because of the small sample size and the idiosyncratic nature of group discussions. Moreover, the level of detail provided by memoing in contrast to coding, afforded rich descriptions of students’ nuanced discussions. For our dataset, two researchers first used a line-by-line analysis for a subset of questions, involving discussing, annotating, and recording memos related to students’ discourse. In particular, we focused on the specific features related to sensemaking such as real-world context, robust explanation building, and written explanations as discussed above. Following this, the primary researcher expanded this analysis to encompass more questions from the dataset in order to provide examples and contrasting cases for each feature. To reiterate, the more of these features that are exhibited in students’ discussions, the stronger the claims we can make that students are engaging in sensemaking, and subsequently, the more convincing the claims made that the activity questions are sufficient entry conditions to initiate student engagement in sensemaking.

Interrater reliability and agreement

Overview of student engagement in the structural components of sensemaking, trends among group engagement in the structural components of the sensemaking process.

It is also important to discuss the structure of a POGIL activity—which was used in the design of the activity—and how this potentially influenced the data seen in Fig. 3 . As mentioned previously, the learning cycle within a POGIL activity is divided into three phases: exploration, concept invention, and application, which are labled in Fig. 3 . These phases have different goals: recognizing trends in data during exploration, developing a definition during concept invention, and applying what they have learned during application. First, we see that students working through the exploration phases did not involve all the structural components of the sensemaking game and there was generally a lack of explanation building for the exploration phases of the learning cycles, which often involved students gathering data using the interactive simulation. Once students had become familiar with the simulation, they were able to target their data collection and simply note their observations as answers to the question without engaging in a collaborative period of explanation building. Contrastingly, we see that Questions 7 and 12 in the concept invention phase and Question 8 of the application phase involved the structural components of sensemaking, which is fitting because this is where students construct and apply new concepts. This trend of limited engagement in sensemaking for the exploration phase and more potential for engagement during the concept invention and application phases, supports evidence in previous work that involved analyzing the effect of question prompt on the depth of students’ generated arguments. Kulatunga et al. (2014) noted that the directed questions in the exploration phase did not elicit strong argumentation building by students, but rather served the role of supporting students in scaffolding arguments in subsequent questions. Similar to Kulatunga et al. (2014) , we assert that although the exploration phase tended to not engage students in sensemaking, these initial questions provide students the necessary information needed to engage in explanation building in later questions, thus, supporting future sensemaking.

In addition, in Fig. 3 it is notable that Questions 13 and 14 did not have any codes applied. For context, the question preceding this, Question 12, prompted students to predict the effect of variables not included in the ideal gas law (particle attraction, particle size). In Questions 13 and 14, students were then asked to make similar claims as they did in Question 12. Due to the similar material, students often quickly voiced an answer that was agreed upon by the entire group without a period of explanation building.

Building a case for engagement in sensemaking: evidence for students’ epistemic form

For analysis of robust explanation building, consider the dialogue presented below in Fig. 6 . This contains pieces from the explanation building of Group 3 completing Question 12 (far left), Group 1 completing Question 8 (middle), and Group 2 completing Question 8 (far right). For these, all groups engaged in the structural components of the sensemaking game for their respective questions. Beginning on the far left with Group 3, the group engaged in robust explanation building that leveraged each group member's ideas as they constructed an explanation for the question. This is exhibited in the collaborative nature of their dialogue, with each group member voicing their thoughts and building off of each other's contributions. Additionally, we see the use of critique from the group such as Cassie saying “I’m thinking more mass for volume” which led the group to their ultimate resolution in the final line. Similarly, the dialogue of Group 1 in the middle of Fig. 6 highlights productive construction of an argument. Michael began with an explanation that connects many of the final elements of their explanation, but the entire group did not fully contribute to the explanation building until Chris voiced the question “So do you guys think it was intentional or not?” This question acted much like a vexing question that connected back to the question prompt and also served as an implicit critique to Michael, acknowledging that the group had not fully answered the question. Chris’ question also shifted the group into a period of construction, where they collaborated to gather correct data to construct an explanation to the question. These two examples showcase instances of groups engaging in productive discourse that included constructing and critiquing in order to reach a consensus (resolution). As a disclaimer, with the example of Group 3 completing Question 12, we would like to acknowledge that this dialogue reflects non-normative scientific reasoning. Although working to construct an explanation for the question, the group ultimately reached an incorrect conclusion. This represents a limitation of the sensemaking framework in that it focuses more on the process of explanation building leading to a resolution, rather than emphasizing the ultimate product of sensemaking.

For contrast, while the first two episodes of dialogue in Fig. 6 represent productive explanation building, the final piece of dialogue from Group 2 highlights an unproductive instance of explanation building where the group is not engaged in construction and critique. The group began by attending to useful data related to the temperature, but the group did not allow for potential critiques, such as comments from Chloe (“wait, cause” and “wait, but are we looking at-”), limiting the overall depth of their explanation building. Additionally, unlike the first example from Group 3, the dialogue of Group 2 included short snippets of dialogue, with minimal reasoning behind their contributions. Whereas Melissa of Group 3 used a concrete hypothetical example of molecules in a box getting larger which would cause an increase of pressure, the dialogue of members in Group 2 was much shorter chunks of dialogue and pertained more to specific pieces of data that was not further justified with reasoning.

Lastly, in line with the importance of thorough explanation building as part of the sensemaking process, our data show that students were influenced by the language of the question prompt throughout their explanation building. Expanding on this, we analyzed all questions that explicitly asked students to justify answers with an explanation (8, 10, 11, 15) and searched for instances in which wording from the question prompt were present in their explanation building. For example, “explain your reasoning” for Questions 10, 11, and 15 and “discuss the available evidence and construct another explanation” for Question 8. Table 4 summarizes these instances for each. The most striking observation from this analysis was the ways the question prompt was exhibited in student dialogue for Question 8 that was not seen to the same extent for the other questions. Students typically referenced specific wording from Question 8 such as evidence and explanation to support their construction of an explanation. Importantly, although instructors often include “explain your reasoning” at the end of a question to prompt student's explanations, these data suggest that scaffolding the question prompt with more explicit language that indicates how students should accomplish this ( e.g. , “discuss the available evidence and construct another explanation”) grounds the discussion and helps promote more robust explanation building for the group.

For the questions in which students participated in the structural components of sensemaking, we summarized the analysis in Fig. 8 , with the representation indicating whether we observed the indicated features of sensemaking (a green checkmark) or not (a red X). Overall, our analysis provides evidence for the extent to which students participated in the process of sensemaking, showing that there is more evidence to make the claim that Group 1 and Group 3 successfully engaged in sensemaking while completing Question 8. Although to a lesser extent, Group 3 also engaged in sensemaking for Question 12, and as explained above, the group came to a scientifically non-normative resolution, highlighting a limitation of the sensemaking framework. Finally, Group 2 completing Question 7 highlights an instance of a group participating in the structural components of the game with little evidence for the sensemaking epistemic form. This suggests the group's discourse may have been more aligned with the epistemic form of the answer-making game, that is, working to simply provide the correct answer ( Chen et al. , 2013 ).

Limitations

With regards to the gas law activity used for data collection, although the activity was designed using the principles of a POGIL activity, the activity has not been officially endorsed by the POGIL Project. Furthermore, with regards to the fidelity of implementation, the facilitator was a graduate student that had not been through formal training as a POGIL facilitator. Nevertheless, our implementation highlights the broad utility of these activities, given that universities generally rely on graduate teaching assistants to lead group activities like the gas law activity used for this study.

As another limitation, we found the sensemaking framework difficult to apply in practice, which involved making modifications and changes in order to develop a context-specific definition of sensemaking. Although our description of how to apply sensemaking was operationalized for our dataset, we posit that our analysis can be applied to other contexts with minor changes ( e.g. , what counts as an “entry condition” might change based on the data analyzed) and we found the framing related to finding evidence to build a case for sensemaking to be a productive direction for future work. Furthermore, we argue that sensemaking as a construct and what can be categorized as sensemaking depends on the context, requiring researchers to be explicit with respect to their methodological and analytical decisions.

Conclusions and implications

Sensemaking as a method of analyzing student problem solving.

Within our analysis, we made an assumption that an activity question can successfully serve as an entry condition into the sensemaking process. Given our in-depth analysis of the data indicating that students engaged in the markers of sensemaking discussed in the literature, this suggests that restating a question can serve as the entry condition for an episode of sensemaking. This differs from the proposed definition of an entry condition where students explicitly recognize a some gap in knowledge, resulting in the unfolding of the subsequent moves of the sensemaking game ( Odden and Russ, 2018 ). Based on our analysis, we argue that future work involving the sensemaking framework can include restating a question as a satisfactory entry condition. This serves as a lower threshold for observing engagement in sensemaking that is relevant for commonly used pedagogical approaches such as POGIL. Furthermore, there was evidence that students treated the activity questions similarly to the idea of vexing questions proposed by Odden and Russ (2019b) where students reiterated the question, resulting in further explanation building.

Next, we would like to address a question posed by Odden and Russ (2019b) regarding whether sensemaking can occur for students learning new material. Given the design of the activity was rooted in the learning cycle, which is intended to be a first introduction to content, this exploratory work suggests students can engage in the process of sensemaking while learning new material, with the qualifier that the questions need to be scaffolded intentionally to support this process. Our analysis provided evidence that Groups 1 and 3 participated in the process of sensemaking while completing Question 8; nevertheless, claims regarding sensemaking related to new material are limited since we would need more data regarding whether or not this was in fact students’ first encounter with the material ( i.e. , data regarding prior coursework would be necessary). More work can be done in the context of POGIL and other contexts to further investigate this aspect and uncover students’ sensemaking of new material.

Lastly, in terms of future work concerning the sensemaking framework, we encourage researchers to use the framework to investigate student's reasoning across other concepts in chemistry and across science. As we highlighted, POGIL activities and collaborative learning that is rooted in the learning cycle present a useful environment to investigate students’ sensemaking related to the development of new concepts, which the community can build on with our study. Additionally, although outside the scope of this manuscript, we noted a pattern among the facilitator's discourse and its effect on how students engaged in sensemaking, with the facilitator discourse involving two general categories of interjections: (1) discourse that was able to quickly guide student reasoning to facilitate further explanation building and (2) discourse that diverged away from student-led discussion, resulting in a discussion dominated by the instructor. This emergent theme compliments the existing literature that previously noted the importance of the facilitator in the classroom ( Daubenmire and Bunce, 2008 ; Kulatunga and Lewis, 2013 ; Warfa et al. , 2014 ; Becker et al. , 2015 ; Daubenmire et al. , 2015 ; Moon et al. , 2016 ; Stanford et al. , 2016, 2018 ) and we suggest future work is needed that specifically focuses on leveraging the sensemaking epistemic game as a means of analyzing the role of the facilitator in supporting student learning.

Supporting student engagement in the process of sensemaking

With regards to the non-structural pieces that we used as a way to assess students’ epistemic form, real-world context and construction of scientific explanations specifically deal with the structure of a task or question. Therefore, another connection to practice for those wanting to engage students in sensemaking would be to relate questions to real-world examples, as was done in Question 8 of our activity. As seen in our data, this provided the potential for students to connect the presented data with their personal experiences. Furthermore, by requiring a written explanation, the group is asked to formally outline their argument which can also further engage students in the process of sensemaking. We therefore suggest instructors use questions that ask students to provide written explanations as part of the question. This is of particular importance given constructing explanations and engaging in argument from evidence are highlighted as critical science practices under the Next Generation Science Standards ( National Research Council, 2012 ).

Finally, in addition to asking students to provide their reasoning for constructed response questions, explicit scaffolding is necessary to engage students in robust explanation building. While the other aspects above reflect a binary characterization of a question ( i.e. , the question is connected to real-world context or is not), depth of engagement in explanation building exists more as a spectrum for potential student participation. We noted evidence that students often used language from specific question prompts such as in Question 8, “discuss the available evidence and construct another explanation”. In contrast, prompts using the generic “explain your reasoning” tended to not provide a strong scaffold for students to construct explanations. We argue that by using more scaffolded question prompts similar to Question 8—prompting that explicitly communicates how we want to students to explain their reasoning—can serve to better support students’ robust explanation building and promote engagement in sensemaking. Using this work, we can construct questions that prompt students to go beyond the simple task of providing an answer and engage them in the process of sensemaking while they develop a response.

Conflicts of interest

Gas laws: using a dynamic computer simulation to construct the ideal gas law.

http://mw2.concord.org/tmp.jnlp?address=http://mw.concord.org/public/student/gaslaws/gaslab.cml

2. Use the simulation to explore the relationship between volume and temperature. How does the volume change when you increase the temperature? How does the volume change when you decrease the temperature?

3. Use the simulation to explore the relationship between volume and pressure. How does the volume change when you increase the pressure? How does the volume change when you decrease the pressure?

a. Is volume proportional or inversely proportional to the number of molecules? (Circle one)

b. Is volume proportional or inversely proportional to temperature? (Circle one)

c. Is volume proportional or inversely proportional to pressure? (Circle one)

7. Use the equation you constructed and the conditions provided below to determine the numerical value of the gas constant R (including units). Provide your answer with 4 digits past the decimal.

P = 1.0 atm

n = 1.0 mol

Application

• The official rules of the NFL require footballs to be inflated to a gauge pressure between 0.85 to 0.92 atm when measured by the referees; the rules do not specify the temperature at which measurement is to be made, but referees typically do this in the locker room (293–296 K).

• The AFC championship conference took place in Foxborough, MA on Jan 18, 2015. The temperature on the field was 282 K.

• The pressure of the footballs was measured on the field at halftime; the following measurements were recorded. All measurements are in atm.

Use the evidence above and your understanding of the Ideal Gas Law to answer the following questions.

8. The NFL assumed that the range of ball pressures was due to intentional cheating. In your groups, discuss the available evidence and construct another explanation.

a. Use the simulation to explore the relationship between volume and particle mass. What changes, if any, do you observe in volume as particle mass increases?

b. Use the simulation to explore the relationship between volume and particle size. What changes, if any, do you observe in volume as particle size increases?

c. Use the simulation to explore the relationship between volume and particle attraction. What changes, if any, do you observe in volume as particle attraction increases?

High Temperature or Low Temperature (Circle one)

11. Under what conditions for pressure will these new variables ( e.g. , particle attraction) have more of an effect? Explain your reasoning.

High Pressure or Low Pressure (Circle one)

12. Use the table below to modify the Ideal Gas Law to indicate how particle mass, particle size, and particle attraction influence each variable ( P , V, n , T ). To illustrate, the final row, temperature ( T ), has been completed for you.

e. Column 1 shows the ideal gas law which has been solved for the variable of interest for each row. You do not need to do anything in Column 1.

f. For Column 2, circle the variable that will influence P , V, n , or T under non-ideal conditions. For the completed row ( T ), none of the variables influence temperature (option iv).

g. For Column 3, circle the operator (+, −) to indicate how you think the equation will change in “real” (non-ideal) conditions given the variable selected in Column 2. In the completed row ( T ), since none of the variables influence temperature, T real = T ideal , so the variable d was crossed out and replaced with a zero.

14. The actual measured volume ( V real ) is larger or smaller (circle one) than expected ( V ideal ).

15. Would you expect helium (atomic radius = 28 pm) or xenon (atomic radius = 140 pm) to behave more like an “ideal gas”? Explain your reasoning.

16. Consider a Superbowl that will take place in Minnesota, where the all-time low temperature is −60 °F (222 K). A weather model predicts that the temperature may be near this all-time low on the game date. What recommendations do you have for the referees regarding the pressure of the footballs to be used in the game? Sketch a particulate-level representation to describe the gas molecules inside the football (1) in the locker room and (2) outside. Assume the volume is held constant.

Acknowledgements

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The Cynefin Framework Sense-Making Guide

As the information environment gets larger with more players becoming involved, problems are becoming increasingly more complex. It is important for leaders to think through problems and situations to discover new solutions and make innovative plans.

The Cynefin framework is a sense-making tool used to help leaders think through a problem or situation to find a solution. It encourages decisions to be made based on circumstances and addresses the uncertainty of complex projects and systems. It breaks down problems to fit into four domains: Obvious, Complicated, Complex and Chaotic. In between these domains, you have "disorder," or "confusion." Disorder is used when you don't know where you are or when you are between two domains. The first step out of disorder is to gather more information to move into a defined domain. This framework improves sense-making and the likelihood of operation planning success by providing direction for a leader to find the best solution.

This framework complements the traditional communication strategy and research approaches such as the Military Decision Making Process (MDMP) and the Joint Planning Process (JPP), as a tool for problem framing before problem-solving. In addition, Cynefin recognizes the causal differences that exist between different types of systems in complex social environments. Communication professionals must think differently about different problems. There is no one-size-fits-all approach and the actions taken depend on which domain a problem is in.

Explore to learn more about the four domains, the leadership role and the potential dangers while problem-solving in each domain.

Cynefin Framework

Complicated.

• is easily predictable
• is a repeating pattern
• has clear cause and effect
• has a single, obvious answer
• has known-knowns (awareness and understanding of all parts of the problem)

WHEN THE PROBLEM IS OBVIOUS

Just as the solution to walking in the rain is using an umbrella, an obvious solution to a problem requires you to:

• Use "best-practice" solutions
• Communicate in clear and direct ways
• Avoid over complicating the problem
• Ensure that proper processes are in place
• Delegate roles and duties
• Use fact-based management (acting in a straight-forward manner based on knowledge of the ordered world)

DANGER SIGNALS

• Complacency and comfort
• Desire to over or underestimate complexity of the problem
• Inflexible or outdated thinking
• No challenge of received wisdom
• Over-reliance on best practice if context shifts

RESPONSE TO DANGER SIGNALS

• Create communication channels to spark ideas and challenge orthodoxy
• Stay connected without micromanaging
• Avoid assuming things are simple or chaotic; analyze the problem and evaluate appropriately
• Recognize both the value and limitations of best practice
• needs expert knowledge to assess the problem
• has a cause-and-effect relationship that is discoverable but not apparent
• has more than one possible "correct" answer
• has known unknowns (you know the questions but don't know the answers yet)

WHEN THE PROBLEM IS COMPLICATED

Just as a hurricane requires you to watch the news to know how and when to evacuate, a complicated problem requires you to:

• Coordinate a panel of experts
• Use "good-practice" solutions
• Listen to conflicting advice
• Determine a course of action
• Execute the plan
• Experts overconfident in their own solutions or in the efficacy of past solutions
• Over-analyzation a.k.a. "Analysis Paralysis"
• Viewpoints of non-experts excluded
• Encourage external and internal stakeholders to challenge expert opinions to combat outdated or inflexible thinking
• Use experiments and games to force people to think outside of the familiar
• is in flux and unpredictable
• has multiple, competing ideas for cause and effect
• requires creative and innovative approaches
• has solutions that are emergent and instructive
• needs deep analysis of the context to ideate solutions
• has unknown-unknowns (unaware of what questions to even ask)

WHEN THE PROBLEM IS COMPLEX

Just as the best way to avoid a tornado's path is uncertain and unpredictable, a complex problem's solution requires you to:

• Develop and experiment to gather more knowledge and understanding of how the problem operates in the environment
• Use "emergent-practice" solutions
• Use methods that can help generate ideas: open up discussion, set barriers, stimulate attractors, encourage dissent and diversity, manage starting conditions and monitor emergency
• Execute and evaluate the results of each experiment to begin determining next steps
• Use pattern-based leadership (responsive and reactive leadership in a disordered world)
• Temptation to fall back into habitual command-and-control mode
• Temptation to look for facts rather than allowing patterns to emerge
• Desire for accelerated resolution of problems or exploitation of opportunities
• Be patient and allow time for reflection
• Use approaches that encourage interaction and communication so patterns can emerge and ideas can flow
• has HIGH turbulence (no control over the situation, high tension)
• has no clear cause-and-effect relationships so there is no point in looking for right answers
• is unknowable (nothing about the problem or answer is known)
• requires many decisions to make and no time to think through logically

WHEN THE PROBLEM IS CHAOTIC

Just as the appropriate response to snow in the desert would be inconceivable and unknown, a chaotic problem requires you to:

• Prioritize containment until the long-term solution can be determined
• Take immediate action to reestablish order (command-and-control)
• Use "novel-practice" solutions
• Provide clear and direct communication
• Take action to move or remediate your problem to be "complex"
• Applying a command-and-control approach longer than needed
• "Cult of the leader"
• Missed opportunity for innovation
• Set up mechanisms, such as parallel teams, to take advantage of opportunities afforded by a chaotic environment
• Encourage advisers to challenge your point of view once the crisis has abated

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Home > College of Education and Human Development > School of Teaching and Learning Faculty Publications > 40

School of Teaching and Learning Faculty Publications

Fourth-grade students’ sensemaking during multi-step problem solving.

Gabriel T. Matney , Bowling Green State University Follow Jonathan D. Bostic , Bowling Green State University Follow Miranda Fox , Bowling Green State University Tiara Hicks , Bowling Green State University Toni May Greg Stone

Document Type

Copyright statement, creative commons license.

Publisher's Statement

© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license .

Repository Citation

Matney, Gabriel T.; Bostic, Jonathan D.; Fox, Miranda; Hicks, Tiara; May, Toni; and Stone, Greg, "Fourth-grade Students’ Sensemaking During Multi-step Problem Solving" (2022). School of Teaching and Learning Faculty Publications . 40. https://scholarworks.bgsu.edu/teach_learn_pub/40

Publication Date

Publication title.

The Journal of Mathematical Behavior

https://doi.org/10.1016/j.jmathb.2022.100933

Available for download on Friday, January 26, 2024

Since April 06, 2022

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