research on classroom behavior management

Classroom and Behavior Management Training Needs and Perceptions: A Systematic Review of the Literature

• Original Paper
• Published: 27 April 2023
• Volume 53 , pages 117–139, ( 2024 )

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• Stacy N. McGuire Ph.D., BCBA-D   ORCID: orcid.org/0000-0001-9074-8837 1 ,
• Hedda Meadan Ph.D., BCBA-D   ORCID: orcid.org/0000-0001-7098-6176 2 &
• Rebecca Folkerts Ed.M., BCBA   ORCID: orcid.org/0000-0003-4868-7115 2  

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Students with behavioral support needs are educated in elementary classrooms daily. However, teachers receive limited training to support students, resulting in limited preventative and intervention services for such students. It is currently unclear what types of training preservice and inservice teachers receive, their perceptions of such training, and the quality of that training.

The purpose of this systematic literature review was to identify the empirical evidence for various forms of classroom and behavior management training for preservice and inservice teacher training and participants’ perceptions of such training.

A systematic literature review was conducted using PRISMA guidelines. Articles published between 2004 and 2022, based on IDEA 2004, were included. The start date of the reauthorization of IDEA in 2004 was chosen because of its implementation of positive behavior supports for students with and without disabilities.

Twenty-two studies were included in the literature review based on inclusion criteria. The included studies presented findings about classroom and behavior management programs or strategies, as well as survey data based on teachers’ perceptions of classroom and behavior management training.

Results indicated preservice teachers receive limited training related to overall classroom management during their teacher preparation programs, but no studies could be found showing they receive any training related to behavior management. Inservice teachers receive far more training related to both classroom and behavior management but indicate a need for more training related to both.

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McGuire, S.N., Meadan, H. & Folkerts, R. Classroom and Behavior Management Training Needs and Perceptions: A Systematic Review of the Literature. Child Youth Care Forum 53 , 117–139 (2024). https://doi.org/10.1007/s10566-023-09750-z

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Accepted : 15 April 2023

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DOI : https://doi.org/10.1007/s10566-023-09750-z

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The Key to Effective Classroom Management

A three-phase process helps build strong teacher-student bonds, which can reduce disruptive behavior.

It’s a daunting but all-too-common sight for many teachers: A classroom full of rowdy students who are unable to focus on the lesson. Classroom management techniques may get things back on track, but valuable time has already been lost.

Many experienced teachers know that making meaningful connections with students is one of the most effective ways to prevent disruptions in the first place, and a new study set out to assess this approach . In classrooms where teachers used a series of techniques centered around establishing, maintaining, and restoring relationships, academic engagement increased by 33 percent and disruptive behavior decreased by 75 percent—making the time students spent in the classroom more worthwhile and productive.

“Strong teacher-student relationships have long been considered a foundational aspect of a positive school experience,” explains Clayton Cook, the lead author of the study and a professor at the University of Minnesota. When those relationships are damaged, student well-being may be affected, leading to academic and behavioral problems.

In the study, teachers used an approach called Establish-Maintain-Restore to build positive interactions with students—a total of 220 in fourth and fifth grade—and boost their sense of belonging. (A follow-up study with middle school teachers used the same strategies, with similar results.) Relationship-building was broken down into three phases: the first meeting, maintenance throughout the school year, and points when a relationship may suffer damage, with useful strategies for each phase.

Since it can be easy for some students to fall through the cracks, a relationship reflection form—like the one we share here—can help teachers take notes on each individual student and highlight ones who need the most attention.

Starting on a Positive Note

At the start of the school year, the teachers in the study made time for establishing relationships. “The goal is to ensure all students feel a sense of belonging that is characterized by trust, connection, and understanding,” Cook and his colleagues explain. For students with learning or behavioral problems, cultivating positive relationships provided “protective effects” that helped them stay focused on learning.

To establish positive relationships, teachers can:

• “Bank time” with students. Schedule one-on-one meetings with students to get to know them better. The goal is to “make deposits into the relationship” to help ease conflict in the future if you have to give constructive feedback or address disruptive behavior.
• Encourage student-led activities. Students feel more invested in their learning if given opportunity to share their interests . Teachers can step aside, be supportive, and listen.
• Welcome students into the classroom. Activities such as positive greetings at the door and icebreaker questions help create a warm classroom culture.
• Use positive communication techniques. Open-ended questions, reflective listening, validation statements, expressions of enthusiasm or interest, and compliments help students—especially shy or introverted ones—ease into classroom discussions.

Maintaining Relationships

Without active maintenance, relationships deteriorate over time, the study authors point out. Teachers may focus too much on academics and not enough on supporting students’ emotional well-being, slowly using up the banked time they initially built up with students.

Teachers can maintain relationships by continuing to implement the strategies above, and in addition they can:

• Take note of positive and negative interactions with students.  Teachers should aim for a five-to-one ratio.
• Regularly check in with students. Ask how they’re doing and what support they may need. In an Edutopia article, Todd Finley explains how 5x5 assessment time helped him focus on a handful of students every day.
• Acknowledge good behavior. When teachers focus attention on positive conduct, disruptive behavior is stemmed before it becomes an issue.

Repairing Harm Before Things Get Worse

Eventually, negative interactions such as misunderstandings, conflict, or criticism can weaken a teacher-student relationship. If these negative interactions are left unaddressed, students may feel disengaged and be less willing to participate in activities. They may also be more likely to misbehave, creating further damage. So it’s important for teachers to “intentionally reconnect” with students to restore the relationship to a positive state.

When relationships need repair, teachers can:

• Let go and start fresh. Teachers should avoid holding mistakes over a student’s head, instead giving them a chance to start each day with a clean slate.
• Take responsibility for their actions. Teachers can avoid blaming students when things go wrong, and think, “What could I have done to avoid the problem in the first place?” They shouldn’t be afraid to apologize when that’s called for—doing so helps build trust with students.
• Show empathy. There are two sides to every story, and a teacher can acknowledge that students may have a different perspective about what happened.
• Focus on solutions, not problems. Teachers can work with students to find a solution that everyone feels is fair.
• Separate the deed from the doer. It’s important to criticize the behavior, not the person. If teachers label children as “problem students,” there’s a danger that they’ll internalize that label, making it more likely that they’ll repeat the behavior in the future.

The takeaway: Effective classroom management starts with relationship building. When students feel a greater sense of belonging, they’re more likely to be academically engaged and demonstrate positive behavior.

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Causes, prevention, and interventions regarding classroom disruptions in digital teaching: A systematic review

Pierre meinokat.

Centre of Teacher Training, Karlsruhe Institute of Technology, Kaiserstraße 12, Geb. 20.52, 76131 Karlsruhe, Germany

Ingo Wagner

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Digitization and the Sars-CoV-2 pandemic are accelerating the use of digital tools in teaching. Therefore, this systematic literature review offers an overview of international studies with a particular focus on classroom disruptions and their causes, as well as on prevention and intervention strategies in digital settings. Selecting out of over 700 published articles from the last 20 years, the results show that, although the research on classroom management in general is numerous, the connection between digitization and classroom disruptions has received little attention so far. Studies of different methodological orientations have been conducted, but strongly teacher-focused. Also, there are conceptual inaccuracies leading to a variety of different findings and interpretations. Thus, this article provides a definition of the term digital teaching and critically discusses the classification of new findings, their emplacement in existing research, as well as their potential to expand existing models. Furthermore, the results summarize causes of disruptions in digital teaching, their possible prevention and intervention strategies.

Introduction

Classrooms without disruptions are desirable, yet utopic. Thanks to Kounin ( 1970 ), the field of classroom management has been researched greatly in the last 50 years, and findings show that handling disruptive classroom situations is part of classroom management (Durak & Saritepeci, 2017 ; Egeberg et al., 2016 ; Kubat & Dedebali, 2018 ; Moltudal et al., 2019 ). Moreover, “effective classroom management has been identified as a key predictor of student success” (Marquez et al., 2016 , p. 88). However, the emerging field of digital teaching has seldom been studied regarding disruptions.

Yet, information and communications technology (ICT) can be found in various parts of everyone’s daily life nowadays. For example, in the early 2000s, the first widely used mobile phones were produced, personal computers entered private households, the internet became accessible, and social media platforms like Facebook and YouTube were made available. Nowadays data is processed digitally at work, smart phones and tablets are more and more used on every educational level and modern science is based on international communication via technology. Undoubtedly, ICT has grown in importance and is receiving more and more attention. The penetration of educational areas with ICT and digitization in general seems inevitable, especially since the outbreak of the Sars-CoV-2 pandemic in 2020. Global education is being forced toward digitization faster than ever before and the use of ICT plays a crucial role in this. It is worth mentioning here that the use of ICT in education is important on different levels. Information systems like (e.g.) internet forums and social networks happen to get more and more integrated in daily educational practice as well as communication systems (like smartphones) and computer systems including their hardware (PCs, Laptops, etc.) and software (apps, learning platforms, etc.). While subjects like computer science are inevitably connected to ICT, other subjects are increasingly using the advantages of digital tools. With a first brief view on the field of classroom disruptions, the increasing usage of ICT evolves potential problems in a variety of areas. For example: problems with the school network, data protection for students’ and teachers’ data, etc. All these topics are located in one or more areas of information and communication technologies and therefore, this review will refer to all these different forms as ICT. All forms of education from primary education to university level are affected. International research focuses on education in digital settings in order to improve knowledge of teachers and students who are forced away from traditional face to face education. As part of this research, this review will contribute to attempts to improve the education of future teachers and will summarize information needed to train teachers on their classroom management skills.

The importance of a well-developed digitization (culture) and the proper use of ICT in teaching and learning had been highlighted around the world by governments and related institutes (e.g., Kultusministerkonferenz [KMK], 2017 ; U.S. Department of Education, 2017 ) even before this global incident. Although its importance has been recognized, current research “does illuminate the slow pace at which classroom management research has entered the digital age” (Cho et al., 2020 , pp. 8–9). Scientific research is responsible for accompanying this “digital revolution” (Collins & Halverson, 2018 , p. 1), and there is a need to enhance already existing research on classroom management and digitization in education while creating a new foundation for future research in digital teaching. Special attention should be paid to disruptions in digital teaching and how to prevent or deal with them in order to make such teaching as effective as possible. As a part of this task, this review aims to reveal the current state of international research in the field of classroom disruptions in digital settings and to discover possible research gaps to enhance the future quality of digital teaching.

Two main concepts appear when focusing on the objective of this review. First, there is the concept of classroom disruptions . This is defined as “behavior a reasonable person would view as being likely to substantially or repeatedly interfere with conduct of a class” (Stockton University, 2001 , p. 1). The mentioned behavior can further be described as “any behavior that interferes with teaching and learning” (Franken, 2020 , p. 445). However, current research is missing sorts, types, or patterns of classroom disruptions besides problematic student behavior. A deeper look into existing research reveals that classroom disruptions are seldom investigated on their own and are mostly found in a contextual setting. This setting is widely known as classroom management and is much more researched than classroom disruptions alone. It was Kounin ( 1970 ) who conveyed the term classroom management to a wider audience. It was defined as “the actions teachers take to create an environment that supports and facilitates both academic and social-emotional learning” (Evertson & Weinstein, 2006 ). During the last 50 years of research into classroom management, scientists have come up with numerous models regarding what it is and what it includes, in contrast to research on the specific sub-area of classroom disruptions. As Balli ( 2011 ) mentioned, three models have appeared most often in teacher education research. First, the assertive discipline model (Canter, 2009 ) was criticized for its more traditional (student and teacher) behavior approach and was updated with more emphasis on establishing a positive classroom climate through rules and procedures (Balli, 2011 , p. 246). Second, Kounin’s ( 1970 ) withitness and group management model demanded teachers to remain aware and revealed the need to multitask and the risk of focusing on one student for too long (Balli, 2011 , p. 246). Third, the choice theory model by Glasser ( 1986 ) advised teachers to be lead managers in a democratic classroom rather than boss managers controlling students (Balli, 2011 , p. 246). While these models focused on a more constructional view of teachers’ behavior, recent research has shifted its focus to categories of classroom management in daily practice (Greenberg et al., 2014 ).

Three different reviews, Simonsen et al. ( 2008 ), Oliver et al. ( 2011 ), and Epstein et al. ( 2008 ), together included over 150 different studies of classroom management from the last 60 years. “Despite the wide variation in research citations in these sources, there is congruence in their findings on essentially five strategies for classroom management” (Greenberg et al., 2014 , p. 3). These big five are as follows: rules, routines, praise, misbehavior, and engagement (Greenberg et al., 2014 , pp. 3–4). Misbehavior, as an essential part of classroom management, leads to disruptions in classrooms and therefore “harm[s …] the teaching/learning process” (Maddeh et al., 2015 , p. 144). This review will search for disruptive situations within a learning process to have a wide overview of possible actions that can be understand as classroom disruptions. To do this in a systematic way, mentioned models and definitions will be used. Yet, what all models and definitions have in common is that they lack a connection to a digital setting.

The second main concept of this review is digital teaching . While digitization seems to be a widespread social topic, a definition of digital teaching is still lacking. First, as a temporary conceptual definition, this review concentrates on the two individual terms digital and teaching . “Nowadays the term digital is used instead of such previously used terms as information and communication technology (ICT) or information technology when talking about technology-related skills” (Ilomäki et al., 2016 , p. 656). Based on this definition this review will understand the term digital related to any kind of technology supported settings. The second term teaching on the other side has a wide field of definitions that can be found in numerous publications. For the purpose of this review the authors decided to use the definition of the Stellenbosch University ( 2021 ): “Teaching can be defined as engagement with learners to enable their understanding and application of knowledge, concepts and processes.” Classes are mentioned as a common place for this engagement. In combination with and without the term digital , various forms of settings can be found: face-to-face teaching in a non-digital setting, face-to-face teaching supported by digital tools or “virtual classroom[s]” (Kennedy-Clark, 2011 ; Park et al., 2019 ). These different kinds of classes were often considered from a learner’s perspective where it is not about the teaching and more about the learning aspect. This is supported by the fact that, in contrast to digital teaching, digital learning is described by several terms, some of which are used synonymously: “digital learning” (Harju et al., 2019 ), “e-learning” (Basak et al., 2018 ; Kriesen, 2011 ), “blended learning” (Akhtar et al., 2017 ; Mason, 2005 ), and “mobile learning” (Burden et al., 2019 ; Charles, 2012 ). This shows the need for a clearer definition of digital teaching, which will be addressed through this review.

When faced with disruptive situations in a digital setting, two possible, timewise-different points of view appeared: the time before, called prevention, and the time during/after a disruption, when an intervention may be applied. To create a greater understanding of each complete situation, research has often looked at these two periods simultaneously (Cerezo et al., 2017 ; Walonoski & Heffernan, 2006 ). Intervention strategies were often related to teachers’ behavior (Gebbie et al., 2012 ), while prevention strategies often searched for causes (Handley et al., 2016 ). To generate a complete overview of the current literature on classroom disruptions in digital teaching, this review covers articles about causes, prevention, and interventions. To date, despite the high relevance resulting from the increase in digital teaching, no such overview exists.

Research questions

To create an overview, discover problems, and find research gaps, this systematic literature review elaborated multiple research questions (RQs):

RQ 1: Which terminologies can be found in research literature to describe digital teaching and the disruptions within it?

Rq 2: what are the methodological approaches used in previous research on the subject of disruptions in digital teaching, rq 3: how does research systematize disruptions in digital teaching.

• RQ 4: What are the causes of disruptions in digital teaching and what prevention and intervention strategies exist to deal with them? To answer this question properly, three sub-questions were generated:

RQ 4.1: What are the causes of disruptions in digital teaching?

Rq 4.2: how can disruptions in digital teaching be prevented, rq 4.3: what intervention strategies regarding disruptions in digital teaching are addressed in the research literature.

This review used the following databases: Education Resources Information Center (ERIC) , Academic Search Ultima ( via EBSCOhost), and Web of Science. All the databases included pedagogical literature as well as literature related to the computer scientifical/technological field. This led to a higher output of potential studies to review and decreased the chance of missing publications in this field of research. Since the number of investigations on digitization has grown over the last decades, only studies published in the last 20 years were included. Also, only English-language, peer-reviewed, study-based articles were integrated to maintain a high and international scientific standard. The general review procedure was based on the PRISMA statement of Moher et al. ( 2009 ).

For the literature research, a search string was developed based on the current scientific point of view, as explained in the framework earlier. Based on the terminologies found in the classroom management models and the big five, word clusters based around (mis-)behavior, discipline, classroom, and disruption were created for the concept of classroom disruptions. Due to the missing definition of digital teaching, closely related terms, like learning and education, were connected to analogies for the term digital, like electronic and virtual. In combination, this led to this search string (which was modified in terms of syntax to fit in the different databases):

("behavior management" OR "behaviour management" OR "behavior problems" OR "behaviour problems" OR "behavior referrals" OR "behaviour referrals" OR "discipline policy" OR "discipline referrals" OR "classroom dilemmas" OR "classroom management" OR "disruptive behavior" OR "disruptive behaviour" OR "school discipline" OR "student behavior" OR "student behaviour" OR "misbehavior" OR "misbehaviour") AND ("e-learning" OR "digital learning" OR "digital education" OR "virtual classroom" OR "electronic learning" OR "blended learning").

Because of problems in converting the search string for different databases, truncation was avoided. This search string created a total of 724 hits. Duplicates were eliminated using the reference management software Citavi, leaving 705 articles divided between the databases as follows:

• ERIC: 453 articles
• Academic Search Ultimate (via EBSCOhost): 228 articles
• Web of Science: 24 articles

These 705 articles were screened based on their titles and abstracts. During the title screening, another 12 articles were found to be duplicates, so 693 articles passed to the title and abstract screening. Exclusion criteria were set to eliminate articles that did not match the scope. Due to lack of a definition of digital teaching, the abstracts were searched for possible digital or digital supported executions of teaching and a consideration of disruptions inside or related to this setting. Therefore, we focused on the main aspects that can be found in the title of this review as well: Are there causes of classroom disruptions mentioned? Can prevention or intervention strategies be found/expected to be part of the article? Does the title/abstract at least mention classroom disruptions or synonymously understood terms? Is there some sort of digital setting acknowledgeable in the title or the abstract? Articles not fitting this purpose were excluded. Studies located in a non-school setting, like advanced technical training for jobs or medical education, were excluded as well. Often connected to a medical point of view were behavioral issues based on emotional and behavioral disorders, attention-deficit/hyperactivity disorder (ADHD), and likewise. Since this review focuses on the educational context, medical contexts were also eliminated. Also, the term cheating was found several times during the screening but was not included in this review because of its different intention of use. These criteria, in combination with the organizational framework (a study, published in English, etc.), were applied by the authors and a co-worker to create a reliable outcome of 50 articles left for a selective review. The final coincidence rate of 98% was reached through 3 screening steps. The researchers and co-worker screened the articles independently from each other, and their results were shared after screening the first 50, then half of, and finally all the abstracts. General problems in understanding different designations were discussed, and a uniform understanding, which will be part of the discussion section of this review, was created during these iterations. This procedure left 50 articles for the next step.

During the selective review, the articles were reviewed based on their findings. In general, the same inclusion criteria were used on the selective review as for the title and the abstract earlier in the process. The authors focused on the described methods, the results and conclusion out of the results found in the article. This more selective procedure allowed to scan all 50 articles in more detail and to decide if they fit in the purpose of this systematic review. Studies that did not meet the given criteria, as well as those that were not studies were excluded. This reduced the results down to a final total of 16 articles, that deal at least partially with causes, prevention and intervention strategies on disruptions in the context of digital or digital supported teaching.

These 16 articles have been fully analyzed. Therefore, we have read the full texts several times with a strict focus on a specific research question. With the help of the knowledge organization functions of the reference management software Citavi, we were able to generate and collect answers to the research questions depending on the orientation and detail of the respective article. Table ​ Table4 4 (Appendix 1 ) shows the entire collection of results. Figure  1 illustrates the reviewing process.

Procedure according to the PRISMA statement (Moher et al., 2009 )

Overview of reviewed articles with findings on disruptions in digital teaching

All 16 reviewed articles are listed in Table ​ Table4 4 (Appendix 1 ). The results of this review will be presented according to the research questions.

The results with regard to this research question will be shown separated in terms of the general term digital teaching and the disruptions therewithin.

Digital teaching

With respect to the terms applied to digital teaching , the authors used different word cluster concepts, which varied in terms of their precision. While some of them mentioned very general explanations, like “internet” (Boyaci, 2010 , p. 208), “online” (Hummel et al., 2015 , p. 670; Li & Titsworth, 2015 , p. 41), “software” (Boyaci, 2010 , p. 213), or “tools” (Charles, 2012 , p. 10), others decided to name the exact tools, like “cell phones” (Charles, 2012 , p. 4; Storch & Juarez-Paz, 2019 , p. 12), “laptop” (Rosen & Beck-Hill, 2012 , p. 228), and “video” (Kurz & Batarelo, 2010 , p. 46). The majority of the researchers opted for grouping the terminology.

Some research held on to the term digital and combined it with other terms to create word clusters for digital teaching. This let to designations like “digital learning” (Blundell et al., 2019 , p. 1), “digital […] system” (Homer et al., 2018 , p. 137), “digital technologies” (Blundell et al., 2019 , p. 1), or “social digital networks” (Charles, 2012 , p. 4).

Another group of terms, like “computer based” (Muir et al., 2013 , p. 1), “data-based” (Bruhn et al., 2020 , p. 1), and “web based” (Boyaci, 2010 , p. 208), was more related to computers and electronic data processing.

The field of virtual reality contained terms such as “mixed reality” (Judge et al., 2013 , p. 88), “virtual classroom” (Muir et al., 2013 , p. 1), “virtual environment” (Judge et al., 2013 , p. 92; Muir et al., 2013 , p. 4), and “virtual technology” (Judge et al., 2013 , p. 88).

The approach of not mentioning the term digital directly but of instead applying connections to the word technology was used, for example, “educational technology” (Rosen & Beck-Hill, 2012 , p. 226), “Information and Communication Technology (ICT)” (Heitink et al., 2017 , p. 96; Moltudal et al., 2019 , p. 80), “mobile technologies” (Charles, 2012 , p. 14), and “technology based” (Bruhn et al., 2020 , p. 1).

Table ​ Table1 1 shows the division of the conceptual groups. Detailed information on which terminology can be found in which article can be seen from Table ​ Table4 4 in the Appendix 1 . Observing the amount of different designations used, it became clear that there is still no standardized terminology when it comes to digital teaching.

Taxonomy—groups of terms for digital teaching

Disruptions

In accordance with the digital teaching terms, those describing the types of disruptions could also be systematized into subgroups.

By far the biggest group of terminologies found was connected to the term behavior . Combinations like “behavior disruptions” (Judge et al., 2013 , p. 95), “behavior management” (Judge et al., 2013 , p. 88), “behavioral expectations” (Storch & Juarez-Paz, 2019 , p. 12), “challenging behavior” (Bruhn et al., 2020 , p. 1), “classroom behavior management” (Muir et al., 2013 , p. 1), “disruptive or unengaged behavior” (Judge et al., 2013 , p. 90), “inappropriate behaviors” (Ho, 2004 , p. 386), “incompatible behavior” (Judge et al., 2013 , p. 88), “misbehaviors” (Li & Titsworth, 2015 , p. 41), “negative behavior” (Homer et al., 2018 , p. 140), and “problem behaviors” (Bruhn et al., 2020 , p. 4) were mentioned.

Some authors used wider terms, such as “being off-task” (Homer et al., 2018 , p. 141) or “non-academic activities” (Moltudal et al., 2019 , p. 81), or declared them to be a “crime” (Moltudal et al., 2019 , p. 84), when talking about disruptive issues.

With the terms “rule-breaking” (Charles, 2012 , p. 8) and “rule violations” (Baker et al., 2016 , p. 22), researchers focused more on the advantages and problems of rules. This can also be connected to “disciplinary issues” (Rosen & Beck-Hill, 2012 , p. 234).

The complexity of classroom management was found in terminological combinations as well: “classroom behavior management” (Muir et al., 2013 , p. 1), “classroom management dilemmas” (Hummel et al., 2015 , p. 670), and “typical classroom management situations” (Kurz & Batarelo, 2010 , p. 49). As Muir et al. ( 2013 ) showed, the boundaries were sometimes blurry.

Table ​ Table2 2 shows the division of the conceptual groups. Detailed information on which terminology can be found in which article can be seen from Table ​ Table4 4 in the Appendix 1 .

Taxonomy—groups of terms for classroom disruptions

Since the research field has still not been explored much, the methods used in the research studies vary. Table ​ Table4 4 (Appendix 1 ) shows the kind of research design, the number of participants, and the digital tools used in each article.

Of the 16 articles considered for this review, 7 articles (Baker et al., 2016 ; Blundell et al., 2019 ; Boyaci, 2010 ; Charles, 2012 ; Kurz & Batarelo, 2010 ; Muir et al., 2013 ; Storch & Juarez-Paz, 2019 ) made use of a purely qualitative approach, and 3 (Heitink et al., 2017 ; Ho, 2004 ; Judge et al., 2013 ) chose a quantitative one, leaving 6 studies that utilized a mixed-methods approach (Bruhn et al., 2020 ; Homer et al., 2018 ; Hummel et al., 2015 ; Li & Titsworth, 2015 ; Moltudal et al., 2019 ; Rosen & Beck-Hill, 2012 ). In terms of the qualitative articles, content analyses based on written texts and semi-structured interviews were applied the most. Questionnaires appeared in most of the quantitative articles. As the mixed-methods approach implies, the use of research methods from both areas had multiple goals, for example, to “explore and discover (qualitatively), and then test and confirm (quantitatively)” (Moltudal et al., 2019 , p. 84).

The numbers of participants in each study differed as well. The smallest groups were n = 6 by Blundell et al. ( 2019 ) and Judge et al. ( 2013 ). By far the biggest number of participants was found in the quantitative (second) part of Moltudal et al.’s research ( 2019 ) (n = 2579). It is worth mentioning that every study included teachers somehow. Practicing teachers were part of 10 studies (Blundell et al., 2019 ; Bruhn et al., 2020 ; Charles, 2012 ; Heitink et al., 2017 ; Ho, 2004 ; Homer et al., 2018 ; Li & Titsworth, 2015 ; Moltudal et al., 2019 ; Rosen & Beck-Hill, 2012 ; Storch & Juarez-Paz, 2019 ), and 6 studies mentioned beginning/pre-service teachers (Baker et al., 2016 ; Boyaci, 2010 ; Hummel et al., 2015 ; Judge et al., 2013 ; Kurz & Batarelo, 2010 ; Muir et al., 2013 ). Combined with this, in 5 studies, students were also a part of the research design (Bruhn et al., 2020 ; Homer et al., 2018 ; Li & Titsworth, 2015 ; Moltudal et al., 2019 ; Rosen & Beck-Hill, 2012 ).

The digital tools used differed just as much as the school levels at which they were used. Table ​ Table3 3 gives an overview what form of tool was used for which level of education. To make it easier to compare, the levels of education were divided into primary education (like elementary school), secondary education (middle and high school) and university level. If mentioned in the article, the level of class (grade or year) is shown separately in an own column. As mentioned, the authors encountered the research field in a variety of ways, which is why there is no clear connection between digital tools used and the level of education. Furthermore, most studies do not specify their model of teaching. This is due to the fact that, on the one hand, the majority of the studies only partially deal with classroom disruptions and, on the other hand, a tendency towards recommendations for action can be recognized. This takes the focus away from the specific disruptive situation and is therefore no more analyzed in this review. In addition, an assignment to existing models of teaching, such as direct instructions, cooperative or project-based learning, in the articles can at best be speculative.

Forms of digital tools used and their associated school level

Of the 16 studies, 2 approached the field of digitization on a more abstract level. Moltudal et al. ( 2019 ) took a deeper look into the digital competences of teachers in relation to the matter of classroom management and managing disruptive behavior. Li and Titsworth ( 2015 ), using an online classroom simulation as part of their study, developed the Student Online Misbehavior scale (SOMs).

Only two research groups made an attempt to identify concrete disruptions within teaching and systemize them into categories. Ho ( 2004 ) delivered six types of problems, and for each of these, a video vignette was watched by participants. The types “were based on preliminary studies” (Ho, 2004 , p. 378) in which teachers from Australia and Hong Kong responded to an open-ended question. This led to the identification of three “major types of problems[…:] learning motivation problems, disruptiveness in class, and inappropriate interpersonal behaviors” (Ho, 2004 , p. 378). Teachers were asked about problem behaviors in general. Therefore, it can be assumed that these types of problems were not exclusively connected to digital teaching. This statement was supported by the fact that none of the examples given concerning the types of problems had a digital context or angle (Ho, 2004 , p. 379).

This issue was taken up by Li and Titsworth ( 2015 ). They created the SOMs (Li & Titsworth, 2015 , p. 41). In multiple studies, the researchers used misbehaviors reported by teachers who were teaching online classes to create four factors that influence misbehavior (including a total of 15 items – see Table ​ Table4 4 in the Appendix 1 ):

• “Seeking unallowed assistance”
• “Internet slacking”
• “Aggressiveness”
• “Lack of communication” (Li & Titsworth, 2015 , p. 47)

The Researchers developed this scale through two separate studies. In the first study the purpose “was to inductively generate a typology of online student misbehaviors that could form an inventory for use in subsequent studies of online classes” (Li & Titsworth, 2015 , p. 42). Results of the surveys generated twenty student misbehavior types which are summed up in the second study into the four factors of the SOMs by further exploration through additional surveys.

RQ 4: What are the causes of disruptions in digital teaching and what prevention and intervention strategies exist to deal with them?

To look in more detail at the causes of disruptions, as well as at prevention and intervention strategies, three sub-questions were answered.

Of the 16 studies analyzed, 6 mentioned causes of disruptions within digital settings. As Blundell et al. ( 2019 ) and Storch and Juarez-Paz ( 2019 ) noted, digital tools themselves were sometimes a source of distraction. The possibilities offered by technological devices, like smartphones or tablets, seemed to also create opportunities to go off-task. As a major reason for this, teachers referred to a lack of self-regulation or self-discipline. “Teachers held students most responsible for displaying inappropriate behaviors (lack of […] self-discipline) and themselves as least responsible” (Ho, 2004 , p. 386).

These issues in terms of self-regulation and self-discipline were observed in conjunction with another missing factor. In Boyaci’s ( 2010 ) study, multiple students “focused on limited communication” (Boyaci, 2010 , p. 225), which did not appear to be an issue in non-digital, face-to-face education. The importance of good and detailed communication between teachers and students was demonstrated by the fact that multiple studies recommended a change in teaching style to more of a moderating and mentoring style (Boyaci, 2010 ; Moltudal et al., 2019 ).

However, it was not only the communication between students and their teachers that was observed to be important. As Charles ( 2012 ) showed, a possible cause of distraction was a lack of communication and coordination between teachers and officials. “Teachers enforce […] rules differentially” (Charles, 2012 , p. 10). This made it difficult to predict what would be a distraction for each participant in a class.

Almost half of the reviewed articles (7 out of 16) gave an answer to RQ 4.2. Among these answers, “rule-setting” (Charles, 2012 , p. 12) seemed to be the most advised prevention strategy. A clear understanding of what is appropriate and what is not was found to be essential for using digital tools in teaching.

Another strategy mentioned by Greenberg et al. ( 2014 ), not only useful in digital teaching, is creating routines: giving students and teachers the possibility of knowing what is coming next and how to act properly to avoid disruptions (Baker et al., 2016 , pp. 26–28).

Charles ( 2012 ) suggested the development of meta-awareness. This includes teachers’ participation and underlines the aforementioned change of teaching style to a more moderating role.

Concrete strategies, like permanent and “individual communication” (Boyaci, 2010 , p. 213), “active monitoring” (Storch & Juarez-Paz, 2019 , p. 16), and “the use [of a] language of understanding” (Baker et al., 2016 , p. 32), were also discussed.

However, for all the strategies it was important that teachers felt comfortable in terms of their teaching (Blundell et al., 2019 , p. 9). It is important to mention that all the advice given was not exclusively limited to disruptions in digital teaching but could definitely be used within this setting.

Homer et al. ( 2018 ) showed that the use of a digital token system could lead to increased positive behavior and prevent problematic attitudes. This was not exclusively linked to problematic behavior within a digital setting, but it showed how parts of digitization themselves could be a prevention strategy. It also tried to use digitization as an opportunity to enhance existing strategies.

Of the 16 articles reviewed, 3 provided an answer to this question. Storch and Juarez-Paz ( 2019 ) recognized the need to warn students to stay on-task or return to it when the usage of cell phones was observed to have led them off-task (Storch & Juarez-Paz, 2019 , p. 14).

As part of their results, Bruhn et al. ( 2020 ) showed that an app could be used as an intervention strategy. Since students could monitor their rated behavior themselves, negative changes could be avoided by the students. Changes in student behavior were indirectly initiated by the teacher’s rating (Bruhn et al., 2020 , p. 8). Like Homer et al.’s ( 2018 ) study, this showed a possible digital solution to a not exclusively digital problem.

Although Baker et al. ( 2016 ) gave a couple of possible intervention strategies for pre-service teachers, those strategies were not specifically designed to deal with disruptions in digital teaching.

As was already suspected, the pure quantity of terms found in answer to RQ 1 (Which terminologies can be found in research literature to describe digital teaching and the disruptions within it?) shows a significant need for further development. Since there is no standardized designation for teaching that uses digital tools or is based on them, it is inevitable to find multiple approaches to naming this construct.

The results of this review show that there are two general approaches: naming or grouping the tangible tools, for example, video, computer-based, digital system, or trying to combine these tools under the term technology and, optionally, combining it with an enhanced setting, like educational technology or information and communication technology. Technology is defined as “scientific knowledge used in practical ways” (Oxford University Press, 2020b ). This does not explain what these practical ways are exactly and what knowledge is used for it. Assuming digital is defined as “using a system of receiving and sending information as a series of the numbers one and zero” (Oxford University Press, 2020a ), it is conceivable that digital tools are required for the use of such technology.

On the other hand, along with the digital context, a clear understanding of the teaching setting varies from article to article. In general, three different types of teaching/learning capable of using digital tools were found among the studies: (1) purely online education in which teachers and students are only in touch online, (2) purely face-to-face education enhanced by digital tools and in which teachers and students are physically present at the same place at the same time, and (3) a mixed version called blended learning. This three-way division, in connection with the understanding of the term digital discussed above, allows an attempt at defining digital teaching:

Digital teaching is the generic term for online learning, digitally enhanced face-to-face learning, and blended learning, assuming that digital tools are used as technology to enable or support the respective form of teaching.

Another terminological problem in this review is associated with the term disruption . Results show that disruptions in teaching are often described as a sort of behavior, often associated with a related negative declaration, like inappropriate or problem behavior. However, the term behavior covers a wide area of understanding. Suler ( 2004 ), for example, sees behavior management in online learning as management to create a better learning output. Greenberg et al. ( 2014 ), on the other hand, show that the management of (disruptive mis-)behavior is indisputably connected to classroom management. Therefore, it is important to take a detailed look at the usage of the term behavior in order not to miss a possible connection to classroom disruptions, and then to separate those understandings that do not mention disruptions.

When mentioning classroom disruptions, it is noticeable that not all of the reviewed articles clarify what specific disturbing situations are declared as disruptions. Some authors give examples like “student told me he hated me” (Baker et al, 2016 , p. 29), “students wanted to use digital technologies at times and in ways that were different to planned uses” (Blundell et al., 2019 , p. 8), “daydreaming in class, not completing homework, talking in class, lesson disruption, bullying, and rudeness to the teacher” (Ho, 2004 , p. 378), “talking out of turn” (Homer et al., 2018 , p. 141), “pupil continues to disturb the lesson by insulting his peers” (Hummel et al., 2015 , p. 672), “Seeking Unallowed Assistance, Internet Slacking, Aggressiveness, and Lack of Communication” (Li & Titsworth, 2015 , p. 41) and “student’s discipline issues” (Rosen & Beck-Hill, 2012 , p. 234). Other authors do not give explicit examples for disruptions but refer to (pre-service) teacher’s strategies (Baker et al., 2016 ; Judge et al., 2013 ; Muir et al., 2013 ) or perceptions and attitudes (Boyaci, 2010 ; Charles, 2012 ; Heitink et al., 2017 ; Kurz & Batarelo, 2010 ; Storch & Juarez-Paz, 2019 ). This illustrates understandings what classroom disruptions in detail contain, but the results are so divers that it is not possible to deduce which disturbances might be more relevant than others. Since this question is related to a subjective view it does not surprise that different teams of authors mention, that the severity of each disruption depends on the teacher’s perception (e.g. Charles, 2012 ; Moltudal et al., 2019 ; Storch & Juarez-Paz, 2019 ). This shows the teacher centered orientation of current research as well as a still missing analysis of classroom disruptions in detail.

While there are various definitions and models about classroom management, such things are rare for classroom disruptions alone. As mentioned, this topic can be found more embedded in classroom management models. This missing focus may often lead to a superficial perspective on disruptions as articles predominantly consider the misbehavior of students. If combined with the quite new field of digital teaching, this results in several approaches to discussion. On the one hand, there is need for a detailed model about classroom disruptions that includes more than just inappropriate student behavior. Teacher behavior as well as external factors, especially related to the newly used digital tools, can be causing disruptions as well. Bringing in the factor of digitization and the new use of ICT inside a classroom while disruptions occur, this creates multiple possible assumptions: Does ICT create disruptions that did not occur in former education? On the other hand, as Bruhn et al. ( 2020 ) show, the proper use of ICT can help teachers and students to handle disruptive situations better than without digital tools. This inevitably raises the question: Can digitization of teaching reduce disruptions? Scientific research on this field must face the fact that digital teaching has multiple dimensions and therefore, a complex answer can be assumed.

Since the use of ICT in daily life often creates new problems like missing connection, lack of electricity and so on, the growth of digital teaching may create a chance to shift the focus from classroom disruptions as mainly student-caused to a more open view of all possible triggers. In addition, a specific connection between the use of certain ICT and the occurrence of certain disruptions has been unexplored so far.

The models, causes, and prevention and intervention strategies found are rarely tied to a digital setting. This is not surprising keeping in mind that digital teaching is an enhanced form of teaching and is therefore likely to face problems and strategies that non-digital teaching has already considered. However, isolated results (for example, Homer et al. ( 2018 )) show that there is a chance to use the strategies developed for non-digital teaching and enhance teaching in general with digital tools to take advantage of them and to deal with disruptions that occur in both digital and non-digital teaching. As disruptions are key in terms of classroom management, there is an urgent need to look deeper into opportunities like this.

In general, findings show that even after a structured selection process studies differ in their point of view on disruptions in digital teaching. Some studies focused on classes (and their disruptions) that are supported by digital tools, some studies investigated pure online (and therefore digital) classes and other studies used digital tools to evaluate disruptions in teaching. This realization raises the questions of an additional filtering of the selected studies. A more precise look would be able to offer more detailed results. Yet, it should be noted that further selection would also lead to an even smaller number of samples.

The results in relation to RQ 2, regarding the methodological approaches of previous research in terms of the subject of disruptions in digital teaching, show that the approaches in this field are methodologically broad. This may be explained by the fact that this field of research has still not been explored much. On the one hand, this has led to a pleasant variety of methodological approaches, but on the other hand, scientific comparison is therefore missing. In general, ongoing concepts shared between scientists and educators and developed by multiple researchers are missing as well. None of the reviewed studies was implemented as a longitudinal study, although approaches like that of Li and Titsworth ( 2015 ) used an iterative process to successfully develop a scale. Since this field of research is, compared to other parts of educational research, relatively young, future studies may fill this gap. This review provides a definition for digital teaching and clarifies the possibilities for enhancing existing models so that future research based on it seems promising.

Although the answers to RQ 3, how research systematizes disruptions in digital teaching, are the least elaborated on, they create a lot of potential for discussion. The SOMs derived by Li and Titsworth ( 2015 ) shows a first step to having a more detailed view of disruptions in digital teaching. Since the scale focuses on online teaching, the question arises of whether this scale might be transferable to other digital settings. During the development process, inputs given by teachers focused on online teaching, but transferring this to face-to-face or blended teaching seems practicable as well. It will be interesting to see if future scales in the area of digital teaching differ significantly from the SOMs.

Besides Li and Titsworth ( 2015 ), none of the reviewed studies created a new systematization for student misbehavior, and the SOMs does not answer the following questions: Does digitization provoke misbehavior that is not yet covered by the existing categories? Furthermore, how does digitization affect teachers’ misbehavior? As mentioned earlier in the framework of this review, models for classroom management already exist. It is not surprising that the findings of the different articles partly fit into these models: developing meta-awareness (Charles, 2012 ) fits into the choice theory model by Glasser ( 1986 ), letting students know what is acceptable behavior and what is not (Baker et al., 2016 ; Greenberg et al., 2014 ) corresponds with the assertive discipline model by Canter ( 2009 ), concrete strategies mentioned in Storch and Juarez-Paz ( 2019 ) relate to the model of Kounin ( 1970 ), and the often-mentioned rule-setting (Moltudal et al., 2019 ) can be found as one of the big five strategies in classroom management (Greenberg et al., 2014 ). The Classroom Disruption Protocol mentions the unallowed usage of cell phones, what is mentioned by Charles ( 2012 ) as well as parts of the SOMs by Li and Titsworth ( 2015 ) fits in parts of the major disruptions mentioned in the protocol. All the existing models refer to existing classroom practices but miss a connection to digitization. Furthermore, most models deal with classroom management in general and do not focus on the disruptions therewithin. An extension of an existing model into a digital setting or a model for disruptions in digital teaching is needed. It is conceivable, for example, that one or more specializations of the Classroom Disruption Protocol could represent a possible result here. Perhaps existing concepts are unsuitable for various reasons. This would make it necessary to develop a new type of model.

Research questions 4.1 to 4.3 deliver answers regarding what current research reports are the causes of disruptions in digital teaching and how to prevent them or intervene in relation to them. Among the results, the lack of student self-discipline is often mentioned as one of the main causes. This underlines the aforementioned focus of the research as being on the teacher’s perspective and is supported by the fact that all the studies used (pre-service) teachers as participants. Some studies included students as well, but the focus on the teacher’s perspective is clear. Studies that include the student’s perspective have the potential to deliver results that teacher-focused research cannot provide. Unfortunately, most of the analyzed articles are missing this potential opportunity. It is obvious that a deeper insight into disruptions from the student’s perspective will lead to a greater quantity of and interesting results.

Student and teacher behavior is often mentioned in this review. The leadership style of teachers is linked to classroom situations (like disruptions) and students’ learning outcomes (Solomon et al., 1964 , p. 23). Since this review focuses on the aspect of disruptive classroom situations, there was no focus on teachers’ behavior strategies. Nevertheless, it must remain clear that there is no possibility of denying the connection between these two fields. Classical “core elements of General Didactics” (Zierer & Seel, 2012 , p. 5) already understand interaction, and thus the behavior dimensions of those involved, as an essential part of interaction in teaching. The lack of findings with regard to specific disruptive situations in digital teaching suggests the need to investigate these further before developing possible prevention and intervention strategies. This review does not claim to predict whether there is a further field to explore in terms of research about behavior strategies, but since the idea that digitization can enhance existing concepts has already been mentioned, it can be assumed with optimism that future research will discover development potential here too.

Considering different perspectives on this field of (future) research seems to be a possible way of obtaining a more selective view of the three different types of digital teaching as well. It is noticeable that the majority of studies either focus on online learning or face-to-face learning. The blended learning approach, with its combined character, may be able to identify different disruptions from both groups. Keeping in mind that science has the obligation to expand existing research, this seems to be a good way of connecting the old and the new.

Limitations

Since the selection of digital tools, the degree of influence and application as well as integration differ greatly depending on the subject, teacher, and type of school, it was difficult to assign this review to one specific area of ICT. In particular, further research may address relationships between types of disruptions and linked ICT areas like data security, requirements for educational platforms or databases. This may also lead to valuable insights on educationally influenced software developments.

While creating the search string for the literature research, it was impossible to include all existing digital tools. To avoid missing relevant articles, the search string included a lot of common combinations and big word groups, like the earlier mentioned terms of behavior , technology , and classroom management . Because of the lack of a uniform definition, this review had to be conducted on this very general level. As a consequence, the search term, which was equipped with general terms but nevertheless focused, sometimes also included articles that clearly missed the main perspective of the review. The review process therefore resulted in a significant reduction in the number of articles to be reviewed. This problem could not be circumvented due to the conceptual obstacles in the beginning, but an established standardized review-procedure was applied.

The large number of used designations created a need for a larger quantity of and more detailed exclusion criteria, while leaving wide fields of research open. During the reliability check process, the researchers stopped twice to adjust the criteria and to create a uniform understanding of all the terms. All the required understandings are based on the fact that there is a lack of uniform definitions in the first place. Nevertheless, it is possible that missing word combinations or the creation of a concrete understanding of specific terms “too late” in the review process led to very specific articles not being found. The suggestion regarding the development of uniform terminologies will help future research in this area.

Future directions

Concerning one of the main findings of this review, further research should be directed at the field of digital teaching and the disruptions within. Observing digital teaching, interviewing students and teachers about their opinions, and keeping in mind that new generations of future teachers are growing up in a more digital world will create multiple opportunities for future research while keeping its practical relevance in mind. As already mentioned, research is still very focused on the teacher’s perspective. Having a more detailed view of disruptions in digital teaching from a student’s point of view seems promising.

The gathered information will be useful in terms of rethinking answers regarding causes of disruptions in digital teaching and possible prevention and intervention strategies. This will also open up new possibilities and potential for international comparison since a major part of the research (n = 8) in this field is located in the United States.

As mentioned, future research should focus on investigating concrete disruptive situations in digital teaching. For this, it will be important to have a clear understanding of what is considered to be a disruption and what is not. Since teaching and learning inevitably go together, opinions from teachers and students are important here. Future teachers are growing up in a more digital world and ICT, not only but especially in education, is becoming more and more important. One main goal of our research is to optimize teaching and teacher education. Therefore, adapting existing research (like the SOMs) or developing new models seems very promising. For example, an interview study with (prospective) teachers and students could provide basic knowledge that is necessary for designing a new model for classifying disruptions in digital teaching. Future research should also consider to investigate disruptions in specific situations and therefore have a look at actual practiced digital teaching.

ICT has entered daily life, Digitization has entered education. With classroom management having been an important factor in terms of teaching for at least half a decade now and dealing with disruptive classroom situations being a part of successful classroom management, the possibilities and risks regarding the use of ICT within are getting more and more attention. The global Sars-CoV-2 pandemic forced educational systems worldwide into digital teaching faster than ever before and as part of an increased scientifically research on this field of interest, this review presented missing links to the important subject of classroom disruptions in digital teaching. Reviewing international, English-language articles about causes, prevention, and interventions with regard to disruptions in digital teaching from the last 20 years, this review shows the lack of precision when it comes to terminology and the absence of comprehension in terms of disruptive situations in a digital setting. The results point out that researchers are using multiple ways to approach this. The findings related to disruptions are still very general, largely teacher-sided, and not specific to digital teaching. With indications that digitization in teaching and learning has the possibility to enhance education, promising results will rely on more practically relevant and deeper research in the future.

Open Access funding enabled and organized by Projekt DEAL. Partial financial support was received from the Vector Foundation and the Gips-Schüle-Foundation.

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• Study Protocol
• Open access
• Published: 25 August 2023

Implementation resources to support teachers’ use of behavioral classroom interventions: protocol of a randomized pilot trial

• Gwendolyn M. Lawson   ORCID: orcid.org/0000-0003-4363-3169 1 , 2 ,
• Julie Sarno Owens 3 ,
• David S. Mandell 2 ,
• Samantha Tavlin 1 ,
• Steven Rufe 4 ,
• Aaron R. Lyon 5 ,
• Ricardo Eiraldi 1 , 2 , 6 &
• Thomas J. Power 1 , 2 , 6  

Pilot and Feasibility Studies volume  9 , Article number:  151 ( 2023 ) Cite this article

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Teacher-delivered behavioral classroom management interventions are effective for students with or at-risk for attention-deficit/hyperactivity disorder (ADHD) or other disruptive behavior challenges, but they can be difficult for teachers to use in the classroom. In this study, we will pilot test a package of implementation strategies to support teachers in using behavioral classroom interventions for students with ADHD symptoms.

We will use a 2-group, randomized controlled trial to compare outcomes for teachers who receive Positive Behavior Management Implementation Resources (PBMIR), a theory and data-driven implementation resource package designed to increase teacher implementation of behavioral classroom management interventions, with those who do not receive this additional implementation support. We will measure teacher implementation outcomes (e.g., observed fidelity to behavioral classroom interventions) and student clinical outcomes (e.g., ADHD-related impairment, ADHD symptoms, student–teacher relationship, academic performance) before and after an 8-week intervention period for both groups; we will also measure teacher-reported acceptability, appropriateness, and feasibility for the PBMIR group following the intervention period.

If there is preliminary evidence of feasibility and effectiveness, this pilot study will provide the foundation for evaluation the PBMIR at a larger scale and the potential to improve outcomes for students with or at risk for ADHD.

Trial registration

This clinical trial was registered at ClinicalTrials.gov. ( https://clinicaltrials.gov/ ) on 8/5/2022 which was prior to the time of first participant enrollment. The registration number is: NCT05489081.

Peer Review reports

Behavioral classroom management interventions, delivered by teachers in classrooms, show evidence for improving student behavioral and academic engagement outcomes [ 4 ]. Evidence-based classroom management interventions emphasize both antecedent- and positive consequence-based approaches to manage student classroom behavior, and are consistent with schoolwide frameworks such as Positive Behavior Interventions and Supports (PBIS; [ 32 , 33 ]. They can be delivered to all students in the class (i.e., universal or Tier 1 practices), as well as with students who need additional support (i.e., targeted or Tier 2 interventions). These interventions are important for supporting children with or at-risk for attention-deficit/hyperactivity disorder (ADHD), for whom they show strong evidence of success [ 9 , 10 ]. However, teachers often do not use Tier 1 and Tier 2 behavioral classroom interventions as designed, if they use them at all [ 14 , 23 , 28 ]. This suggests a need for implementation strategies to support teachers in using these interventions. Implementation strategies are most likely to be effective when they are theory driven and target specific, malleable factors that influence provider behavior [ 22 ].

Behavioral classroom management interventions encompass a wide range of practices, which can vary along several dimensions, including whether they are designed to be applied to the entire class (i.e., Tier 1), or to targeted students only (i.e., Tier 2); whether they use antecedents, consequences, or both; and the time and effort they require to implement. Evidence suggests that teachers use some specific interventions more than others [ 28 ] and our prior work indicates that teachers’ intentions to use behavioral classroom interventions vary by specific intervention [ 20 ]. Our prior work also found that teacher-reported barriers and facilitators to using behavioral interventions differ between Tier 1 and Tier 2 interventions [ 21 ]. Together, this suggests that implementation strategies to support teachers in using behavioral classroom interventions should be tailored to specific intervention components.

The Theory of Planned Behavior [ 1 ] may be useful for developing implementation strategies because it delineates two sets of factors that may influence teachers’ implementation: factors that promote their intentions to implement an intervention and factors that promote their ability to act on strong intentions [ 13 ]. It also delineates specific psychological determinants of intentions: attitudes (e.g., whether a teacher “likes” or “dislikes” behavioral classroom management strategies for ADHD), social norms (e.g., whether the teacher perceives that other teachers use behavioral classroom management strategies or whether their supervisor expects them to use them), and self-efficacy (e.g., whether teachers believe that they have the necessary skills to successfully implement behavioral classroom management practices). These determinants provide potential mechanisms for increasing implementation among teachers who report low intentions to use a behavioral intervention [ 13 ]. At the same time, for teachers who do not implement a behavioral intervention despite positive intentions to do so, implementation strategies should target teachers’ ability to act on their intentions [ 13 ]. Theories of habit formation [ 25 ], which propose that automatic behavior is triggered by situational or contextual cues, suggest specific strategies, such as reminders in the environment, to use when intentions are strong but not acted upon. For example, if a teacher is ambivalent about using a behavioral classroom intervention, they may benefit from messages targeting attitudes or norms (e.g., narrative from other teachers about how they have found it helpful). On the other hand, if a teacher intends to use the intervention but struggles to actually do so within a busy classroom environment, they may benefit from reminders or strengthening habits.

Our team developed an implementation resource package (“Positive Behavior Management Implementation Resources, PBMIR”) to support teachers in using Tier 1 and Tier 2 behavioral classroom interventions, particularly with students with elevated levels of hyperactivity, inattention or impulsivity. The resource package is informed by the Theory of Planned Behavior [ 1 ] and theories of habit formation [ 25 ], to target attitudes, norms, self-efficacy, habits, and reminders; it specifically targets barriers and facilitators identified by teachers as important in a previous study [ 21 ]. It uses a modular format to support teachers in using four key behavioral classroom management interventions: behavior-specific praise (i.e., providing frequent verbal acknowledgment by specifically labeling praise-worthy behavior; e.g., [ 6 ], precorrections (i.e., reminding students about behavioral norms prior to a time when behaviors of concern might be likely; e.g., [ 7 ]; brief and specific behavior corrections (i.e., consistently correcting behavior in a clear, concise and calm way; e.g., [ 28 ]; and use of daily behavior reports to provide feedback on specific behavioral goals (e.g., [ 28 ]. The PBMIR also includes two additional modules to support factors that were identified as critical for implementation of the target interventions: student–teacher relationships and adult wellness.

We developed the PBMIR through an iterative, community-partnered process, in which we made revisions based on feedback obtained from teachers during a series of try-outs of versions of the PBMIR; we interpreted try-out data and made revisions in partnership with a Program Development Team of stakeholders (e.g., teachers, coaches, administrators). We designed this resource package to fit within existing school structures, align with school-wide Positive Behavior Interventions and Supports (PBIS), and be feasible to sustain with existing resources. If effective, deploying this resource package as an implementation strategy at scale may help address the critical need of supporting teachers in using evidence-based behavioral classroom interventions. An important first step, though, is to conduct a pilot study testing the feasibility, acceptability, and preliminary effectiveness of the PBMIR.

The main objective of this study is to pilot test the implementation strategy resource package. We will evaluate the teacher-reported feasibility, acceptability and appropriateness of the resource package. We will also collect data on teacher implementation outcomes and child effectiveness outcomes to examine the promise of the resource package.

Setting and sample

The study will be conducted in elementary or elementary-middle schools from a large urban school district in the Northeast United States. The district’s student body is racially and ethnically diverse (about 50% Black/African American, 21% Hispanic/Latino, 14% Non-Hispanic White, 7% Asian, and 5% Multiracial or Other races). Approximately 80% of these students live in households that are income-eligible for free or reduced-price meals. We will recruit schools that have already adopted schoolwide Positive Behavioral Interventions and Supports (PBIS) and will obtain written permission from principals for their schools’ participation, following school district processes.

Our target sample size is 30 teachers (teaching grades Kindergarten through 5th grade; with students of ages approximately 5 through 12) within participating schools over a two-year period. Teachers will nominate students in their classroom to participate (see “ Procedures ” section). We will enroll 2 students per teacher, for a target sample size of 60 students. These sample sizes were selected to be consistent with guidelines for pilot studies to determine the feasibility of recruiting participants, collecting outcome data, and retaining participants in the protocol [ 34 ].

Recruitment and informed consent

All procedures are approved by the institutional research board at the Children’s Hospital of Philadelphia and the research review committee of the participating school district. Any protocol amendments will be reviewed and approved by the IRB and school district review board prior to any changes taking place, and ClinicalTrials.gov will be updated to reflect any relevant changes.

Teachers will be recruited from participating schools. See Table 1 for the schedule of enrollment, interventions, randomization, and data collection. After providing informed consent for their participation, teachers will nominate two students from their classroom to participate. Teachers will be instructed to nominate students who “show elevated levels of inattentive, hyperactive or impulsive behaviors;” whom “you think may benefit from increased use of positive behavior management practices,” and “for whom you could use additional support in managing their behaviors.” Teachers will complete the Impairment Rating Scale (IRS; [ 11 ], a 7-point scale ranging from 0 to 6, for each of these students, without providing identifying information about the student. To meet inclusion criteria, a student must receive a score of 3 or greater on at least one IRS domain. If one or more of the nominated students does not meet this criterion, the teacher will nominate an additional student until two students are identified who meet this criterion. Students will be excluded from participation if they have a special education classification of intellectual disability, if their primary presenting concern is psychosis or autism spectrum disorder based on parent- or teacher- report, or if they present as in acute risk of harm to self or others, such that participation would be clinically inappropriate because they warrant more intensive intervention.

For each student who meets criteria, teachers will be asked to contact the student’s parent/legal guardian to obtain verbal permission for the research team to contact them, and for the teacher to provide the research team with their contact information. If a student’s parent/legal guardian declines permission or cannot be reached, the teacher will nominate an additional student and will complete an IRS screening form regarding the student. If it is not possible to identify two children within a classroom who meet screening criteria and for whom informed consent can be obtained, we will allow the teacher to participate with one enrolled child.

After permission from the parent/legal guardian is obtained, the teacher will provide the research team with the student’s name, parent’s name, contact information, and preferred method of contact. The research team will reach out to the parent/legal guardian and obtain their informed consent for the student’s participation.

Randomization

Teachers will be randomized to condition blocked on grade level (i.e., grades K-2 vs. 3–5) and school. The principal investigator will generate the allocation sequence using online software (Sealed Envelope Ltd., 2022.) Students will be assigned to their teachers’ condition. A member of the research team will notify teachers of their assigned condition after baseline data collection is complete. If they are assigned to the “PBMIR” condition, the research team will schedule a meeting with them the week following randomization to provide them with a copy of the PBMIR, and the 8-week time period will start at that meeting. If they are assigned to the “Implementation Support as Usual” condition, the 8-week time period will start the following week, in order to ensure the timelines are comparable between conditions. Teachers in the “Implementation Support as Usual” condition will be permitted to receive any implementation support that they would otherwise receive related to classroom management (which could include trainings or instructional coaching), and this support will be documented via teacher report at endpoint. Teachers in this condition will receive a copy of the PBMIR electronic, written, and tangible resources following post- data collection to thank them for their participation. Children in both conditions will continue to receive any therapy or medication that they would otherwise receive during the study period, and parents will be asked to report about any medication their child takes at baseline and endpoint.

Positive Behavior Management Implementation Resources

The Positive Behavior Management Implementation Resources (PBMIR), previously developed using an iterative, community-partnered process, is organized into six modules: (1) student–teacher relationships; (2) adult wellness; (3) behavior-specific praise; (4) precorrections; (5) calm behavior-specific corrections; and (6) daily behavior reports.

Teachers will engage with the resource package for a period of 8 weeks. Each teacher is assigned a guide, who consults with the teacher during four, brief (i.e., 15–20 min) meetings during this period to support their engagement. The guide assists the teacher with goal setting, identifying relevant resources, building motivation, problem solving, and reflecting on progress. The guide supports the teacher in selecting modules to work on, working within a consistent structure: first focusing on student–teacher relationships (either class-wide or with the specific focal student), then focusing on behavior-specific praise, then proceeding to either precorrections or calmly-delivered behavior-specific corrections, and moving on to daily behavior reports last. Adult wellness resources for teachers are used if and when relevant.

Guides are supported by a written manual that consists of four sections: (1) an introduction with an overview, guiding beliefs (e.g., teachers are experts in their classrooms and their professional practice), and policies (e.g., confidentiality, scheduling); (2) an overview of the Theory of Planned Behavior; (3) process strategies (i.e., rapport building, motivational interviewing, goal setting, problem solving), and session agendas (i.e., a broad outline for the first session, follow-up sessions, and final session). Individuals serving in the guide role should be familiar with the elementary school classroom context, child development, and core behavioral principles, but guides do not need to have a specific educational background.

In addition to guide meetings, the resource package includes the following components: (1) written one-page documents (e.g., overview, tip sheet with messages targeting attitudes, norms, and strategies to use reminders, document with sentence stems to provide sample language); (2) fillable planning guides; (3) optional text message reminders, for which teachers can select the frequency and time to receive them; (4) the option to follow an ‘Instagram’ account with reminder posts and tips; (5) videos about the target practices, including teacher testimonials; (6) a notepad to use for self-monitoring; and (7) tangible resources and reminders, including stickers and sticky notes. Teachers set their goals and choose the resources to use in collaboration with their guide.

Feasibility of research procedures

Recruitment rate.

We will track the number of teachers and students enrolled in the study. We will also examine the proportion of nominated and eligible students for whom informed consent is obtained.

Response rate

We will calculate the proportion of parent- and teacher- report surveys completed at each time point out of the number of teachers and students enrolled and randomized across conditions.

Retention rate

We will record the number of teachers and students who withdraw from the study and will compute the retention rate for each group of participants (i.e., teachers, students) as the proportion of participants who do not withdraw out of the number of enrolled and randomized teachers and children across conditions.

Engagement with the resource package

We will track teachers’ use of specific aspects of the resource package by tracking: 1) the number of guide meetings attended by each teacher in the PBMIR condition; 2) the percentage of teachers in the PBMIR condition who agree to receive text message reminders; and 3) the percentage of teachers in the PBMIR condition who choose to follow the Instagram account.

Teacher implementation outcomes

Acceptability.

Teachers in the resource package condition will complete the Acceptability of Intervention Measure (AIM; [ 37 ], which consists of four items (e.g., “[Intervention] is appealing to me”) on a 5-point Likert scale, regarding the resource package. Teachers will complete this measure at the end point, following their 8-week participation period. This measure has shown acceptable reliability (alpha \(\ge\)  0.83) and test–retest reliability (Pearson correlations above 0.70) in prior samples.

Appropriateness

Teachers will complete the Intervention Appropriateness Measure (IAM; [ 37 ], which consists of four items (e.g., “[Intervention] seems suitable”) on a 5-point Likert scale, regarding the resource package. Teachers will complete this measure at the end point. This measure has shown acceptable reliability (alpha \(\ge\)  0.87) and test–retest reliability (Pearson correlations above 0.70) in prior samples.

Feasibility

Teachers will complete the Feasibility of Intervention Measure (FIM; [ 37 ], which consists of four items (e.g., [Intervention] seems easy to use”) on a 5-point Likert scale, regarding the four target interventions supported by the resource package. Teachers will complete this measure at the end point. This measure has shown acceptable reliability (alpha \(\ge\)  0.88) and test–retest reliability (Pearson correlations above 0.70) in prior samples.

A member of the study team will conduct classroom observations at the baseline, midpoint and end point of study participation for teachers in both conditions. These observations will use a modified version of the Student Behavior Teacher Response (SBTR) scale [ 27 ]. Observers will record counts of focal student norm violations and teacher responses to norm violations (i.e., no response, inappropriate response, appropriate response). Observers will also record counts of teacher use of behavior-specific praise, unlabeled praise, precorrections (directed at the focal students and overall), and teacher behaviors related to using daily behavior reports for focal students. We will observe these practices at both the universal and targeted levels because three of the target practices can be employed class-wide. Finally, the observer will rate the teachers’ global competence regarding classroom management and supportive relationships (with the whole class and with each target student) on a 7-point scale (from “poor” to “excellent”) following each observation. Approximately 20% of observations will be conducted by two observers so that inter-observer agreement can be calculated.

Child outcomes

Student–teacher relationship scale.

Teachers will complete the Student–Teacher Relationship Scale [ 29 ] about participating children at the baseline and the end point. This scale assesses teachers’ perceptions of their relationship with individual students. The STRS has shown adequate test–retest reliability (r = 0.89) and good internal consistency (α = 0.89; [ 29 ]. It generates three subscales (conflicts, closeness, dependency) and an overall total score. The total score will be used as a secondary outcome measure.

ADHD symptoms and impairment

Teachers and parents will complete the NICHQ Vanderbilt Scale [ 38 ] about participating children at the baseline and the end point. The inattention and hyperactivity/impulsivity scales on the Vanderbilt have high internal consistency, acceptable test–retest reliability, and convergent validity [ 3 ]. Teacher-report and parent-report on items related to adaptive functioning (e.g., “disrupting class,” “relationship with peers”) will be used as a primary outcome measure, and teacher-reported and parent-reported inattention and hyperactivity/impulsivity symptoms will be used as secondary outcome measures.

Academic performance rating scale

Teachers will complete the Academic Performance Rating Scale [ 8 ] about participating children at the baseline and the end point. This scale assesses teacher judgment of students’ academic functioning across two subscales: Academic Success (i.e., academic achievement) and Academic Productivity (i.e., day-to-day performance). The subscales are each measured by 8 items, rated on 5-point scales. They have acceptable internal consistency (0.72–0.95), stability (0.88–0.95), criterion-related validity, and sensitivity to intervention [ 12 , 24 ]. Teacher-reported Academic Productivity will be used as a primary outcome measure and teacher-reported Academic Success will be used as a secondary outcome measure.

Direct behavior rating multi-item scales (DBR-MIS)

Teachers will complete ratings of child behavior using Direct Behavior Rating Multi-Item Scales (DBR-MIS; see [ 36 ]. For this study, teachers will rate behaviors in two domains: Engagement (5 items rating frequency of these behaviors on a 7-point scale ranging from Never to Almost Always) and Disruptive Behavior (5 items rating degree to which each item is a problem on a 7-point scale ranging from Not a Problem to Serious Problem). These domains of the DBR-MIS have demonstrated treatment sensitivity [ 17 ]. Teachers will complete these ratings at baseline, midpoint and endpoint. The average rating within domains will be used as secondary outcome measures.

Homework Performance questionnaire–parent form (HPQ-P)

Parents will complete the Homework Performance Questionnaire-Parent Form [ 30 ] at baseline and endpoint. Parents will complete this measure for target students in grades 1–5. The student self-regulation factor, which has shown strong internal consistency (i.e., alpha's between 0.92 and 0.94; [ 30 ] will be used as a secondary outcome measure.

Potential mediators

Teacher intentions questionnaire.

Teacher intentions to implement each of the four intervention components will be measured using a standardized 4-item questionnaire [ 31 ]. The items will use validated stems designed to probe provider intentions to use a specific practice (e.g., “I intend to…”), measured on a 7-point scale from “Strongly disagree” to “Strongly agree.” Teachers will be asked to complete these items “regarding students in your class who show elevated levels of inattentive, impulsive, or hyperactive behaviors.” Additionally, teachers will be asked to complete these items regarding participating children after the children have enrolled in the study.

Teacher determinants of intentions questionnaire

Teachers will complete a questionnaire with validated, standardized item stems (Fishbein and Ajzen, 2010) to report on their attitudes, norms, and self-efficacy for using behavioral classroom management interventions “regarding students in your class who show elevated levels of inattentive, impulsive, or hyperactive behaviors.”

Teacher self-rated habit index (SRHI)

Teachers will complete the SRHI, a 12-item scale that assesses the automaticity of a behavior, as well as its frequency of repetition and assimilation into one’s self-identity, regarding their use of behavior-specific praise at baseline and end point [ 35 ]. The SRHI has shown high internal consistency (i.e., alpha’s between 0.85 and 0.95) and convergent validity (i.e., correlation of r  = 0.58 with response-frequency measure; [ 35 ].

Barriers and facilitators questionnaire

Teachers will report on the extent to which each of 10 potential barriers (five classified as beliefs that weaken intentions, and five classified as challenges that interfere with execution) and 12 facilitators (three classified as beliefs that strengthen intentions, seven classified as factors that assist with executing, and two classified as factors that assist with learning how to use the practice) impact their use of behavior-specific praise at baseline and endpoint. We developed these items from qualitative interview data from teachers in an earlier study [ 21 ].

Analytic approach

The primary goals of the analyses are to examine the feasibility and preliminary effectiveness of the PBMIR package. We will examine the recruitment rate, response rate, retention rate to assess the feasibility of the research procedures, inform adjustments to procedures as necessary, and inform sample size estimates in a larger trial as indicated. Consistent with recommendations [ 2 ] we will use traffic light criteria to determine whether the research protocol should proceed to a full trial unchanged (“green”), with adaptations (“amber”), or not at all (“red”). The specific criteria are listed in Table  2 .

We use descriptive statistics to examine teacher-reported acceptability, appropriateness, and feasibility of the implementation resource package. We then will conduct independent sample t tests to examine whether the intervention and control groups differ on baseline teacher and student outcomes.

To compare teacher use of the four behavioral classroom interventions between the implementation strategy and control group, we will estimate means, standard deviations (SD), and 95% confidence intervals (CI) of observed teacher use of the four interventions and teacher global competence ratings. Similarly, to compare child-level outcomes between the groups, we will estimate means, SDs, and 95% CI of child primary and secondary outcomes. We will examine baseline and post-intervention scores, as well as change scores. We will compute effect sizes for each outcome measure by calculating the difference in change scores for the implementation strategy versus control group between post intervention and baseline and dividing this amount by the pooled standard deviation of the change score for implementation strategy and control [ 26 ].

Although the study is not powered for significance testing, we will conduct exploratory analyses examining between-group differences. We will use multi-level linear models, implemented in HLM software, to account for the nested data structure (i.e., students nested within teachers, nested within schools). We also will examine change in the measure of teacher intentions and teacher-reported implementation barriers, and explore with regression analyses the extent to which these variables have the potential to mediate the effect of intervention on outcomes, consistent with the conceptual model for mediation [ 15 ]. These analyses will determine whether there is preliminary evidence to support changes in intentions and specific barriers as plausible mechanisms for the implementation strategy.

The goal of this pilot study is to examine the feasibility and promise of the package of PBMIR, designed to help teachers strengthen student–teacher relationships and implement evidence-based behavioral classroom interventions, particularly for students with or at-risk for ADHD. If the pilot study results indicate that the PBMIR is acceptable, feasible and promising, this pilot study will provide the foundation for evaluating the PBMIR at a larger scale. Therefore, this research has the potential to ultimately lead to improved teacher effectiveness and student outcomes. Because the research is being conducted in a large, urban school district that serves a predominantly marginalized and minoritized student body, and the PBMIR is designed to be appropriate, feasible, and sustainable to implement in this context, it also may be appropriate for other large, urban school districts. Given that children spend much of their time in the classroom setting, improving the implementation of evidence-based practices for children with or at-risk for ADHD in this context offers considerable promise for improving population-level mental health and promoting health equity.

There are several notable strengths to this project. The PBMIR were developed through an iterative, community-partnered process, which is key to ensuring contextual appropriateness and feasibility [ 5 , 18 ]. The implementation resource supports target practices that align with school-wide PBIS as implemented in the school district and uses terminology consistent with school and district initiatives. The PBMIR is informed by theory by targeting key constructs that shape individual’s intentions (i.e., attitudes, norms, self-efficacy) and their ability to act on intentions (i.e., habits, reminders); it was also informed by prior quantitative and qualitative data from teachers [ 20 , 21 ]). The theory-driven, data-driven, and community-partnered foundational work for this study improves the likelihood that the PBMIR will be effective, acceptable, contextually appropriate, and feasible. We will collect data regarding both teacher implementation outcomes and student outcomes, using multiple methods (rating scale, direct observations) and informants (i.e., parent report, teacher report). Finally, we anticipate that the study will generate knowledge about potential mechanisms for implementation strategies (e.g., habits) that may generalize beyond the context of behavioral classroom interventions in schools.

We acknowledge several potential limitations, as well as potential practical or operational issues. First, the study is designed as a pilot study to establish feasibility and promise of the PBMIR and to lay the foundation for a larger scale trial; it is not powered for significance testing. Because of this goal, we are collecting data on a large number of outcome measures, and therefore may be at risk for type I and type II errors. Second, there are considerable challenges to participant recruitment, retention, and the implementation of research procedures in historically under-resourced schools. Many of these challenges have been heightened during the COVID-19 pandemic. We therefore anticipate challenges with recruitment and data collection, and are planning approaches to minimize burden, promote engagement using incentives ethically, and ensure alignment with school and district priorities. We also note that we do not plan to collect child-report outcome measures, which could add valuable information. Finally, we acknowledge that the PBMIR is designed as an individual, teacher-level implementation strategy, and does not target important school-level factors such as leadership and climate. School-level variability in these organizational factors may be important for the implementation and outcomes of the PBMIR, but the current study is not designed to examine these relationships, given the relatively small number of included schools.

If the pilot study results support the resource packages’ promise, the next step in this program of research would be to evaluate the PBMIR at a larger scale through an adequately-powered, randomized hybrid trial (i.e., a trial measuring both implementation outcomes and effectiveness outcomes simultaneously, see [ 19 ]. This would enable us to more definitively assess the PBMIR’s effectiveness regarding teacher implementation outcomes and student outcomes. Moreover, a larger scale trial would also support the examination of individual and organizational factors that serve as mediators and moderators of implementation outcomes.

Availability of data and materials

De-identified data will be available upon request from the corresponding author.

Abbreviations

Attention-Deficit/Hyperactivity Disorder

Positive Behavior Interventions and Supports

Student Behavior Teacher Response

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Acknowledgements

The development of the PBMIR was a collaborative effort between the study team and many other partners. We gratefully acknowledge the teachers who participated in try-outs and provided feedback, and partners at the PBIS office of the partnering school district. We also acknowledge the Program Development Team, which included TiQuann Jett-Jamison, Alexandra Smink, Aubrey Depa, Hasana Ahmad, Anne McKendry, Brianna Heflin, Adell Shaw, Rashida Alexander, and the rest of the Program Development Team. Finally, Rebecca Dolfman, Devon Linn, and Burrell Smithen contributed to this work.

This research is supported by K23MH122577.

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Authors and affiliations.

Children’s Hospital of Philadelphia, Philadelphia, USA

Gwendolyn M. Lawson, Samantha Tavlin, Ricardo Eiraldi & Thomas J. Power

Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA

Gwendolyn M. Lawson, David S. Mandell, Ricardo Eiraldi & Thomas J. Power

Ohio University, Athens, USA

Julie Sarno Owens

Rufe Educational Consulting, LLC, Schwenksville, PA, USA

Steven Rufe

Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA

Aaron R. Lyon

Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA

Ricardo Eiraldi & Thomas J. Power

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Contributions

GL wrote the first draft of the protocol, assembled the study team, secured the grant funding, and provided overall project leadership. All other authors are mentors, advisors, collaborators, or coordinators on the grant and have provided input into the design of the study, measurement, and/or recruitment planning. All authors reviewed and provided feedback for this manuscript. The final version of this manuscript was vetted and approved by all authors.

Corresponding author

Correspondence to Gwendolyn M. Lawson .

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Ethics approval and consent to participate.

This study was approved by the Children’s Hospital of Philadelphia Institutional Review Board under protocol number 22–019825 It was also approved by the school district research review committee. We will obtain written teacher consent, using a paper or online consent form, prior to participation in the study. We will obtain written or verbal consent from parents/legal guardians for their child’s participation. Children who are age 12 or older, or who will turn 12 during the course of study participation, will provide assent for participation. Data will be stored on REDCap. Copies of only coded data (i.e., with participant unique study ID, questionnaire responses,) will be downloaded from the REDCap for data analysis purposes. These data will be stored in password-protected files on the Principal Investigator’s office computer, in a secure server for research.

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Not applicable.

Competing interests

Steven Rufe receives consulting fees from the Children’s Hospital of Philadelphia and Drexel University. All other authors declare they have no competing interests to report.

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Lawson, G.M., Owens, J.S., Mandell, D.S. et al. Implementation resources to support teachers’ use of behavioral classroom interventions: protocol of a randomized pilot trial. Pilot Feasibility Stud 9 , 151 (2023). https://doi.org/10.1186/s40814-023-01381-4

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Received : 27 March 2023

Accepted : 19 July 2023

Published : 25 August 2023

DOI : https://doi.org/10.1186/s40814-023-01381-4

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4. challenges in the classroom.

In addition to asking public K-12 teachers about issues they see at their school, we asked how much each of the following is a problem among students in their classroom :

• Showing little to no interest in learning (47% say this is a major problem)
• Being distracted by their cellphones (33%)
• Getting up and walking around when they’re not supposed to (21%)
• Being disrespectful toward the teacher (21%)

Some challenges are more common among high school teachers, while others are more common among those who teach elementary or middle school.

• Cellphones: 72% of high school teachers say students being distracted by their cellphones in the classroom is a major problem. A third of middle school teachers and just 6% of elementary school teachers say the same.
• Little to no interest in learning: A majority of high school teachers (58%) say students showing little to no interest in learning is a major problem. This compares with half of middle school teachers and 40% of elementary school teachers. 
• Getting up and walking around: 23% of elementary school teachers and 24% of middle school teachers see students getting up and walking around when they’re not supposed to as a major problem. A smaller share of high school teachers (16%) say the same.
• Being disrespectful: 23% of elementary school teachers and 27% of middle school teachers say students being disrespectful toward them is a major problem. Just 14% of high school teachers say this.

Policies around cellphone use

About eight-in-ten teachers (82%) say their school or district has policies regarding students’ use of cellphones in the classroom. Of those, 56% say these policies are at least somewhat easy to enforce, 30% say they’re difficult to enforce, and 14% say they’re neither easy nor difficult to enforce.

High school teachers are the least likely to say their school or district has policies regarding students’ use of cellphones in the classroom (71% vs. 84% of elementary school teachers and 94% of middle school teachers).

Among those who say there are such policies at their school, high school teachers are the most likely to say these are very or somewhat difficult to enforce. Six-in-ten high school teachers say this, compared with 30% of middle school teachers and 12% of elementary school teachers.

Verbal abuse and physical violence from students

Most teachers (68%) say they have experienced verbal abuse from their students, such as being yelled at or verbally threatened. About one-in-five (21%) say this happens at least a few times a month.

Physical violence is far less common, but about one-in-ten teachers (9%) say a student is physically violent toward them at least a few times a month. Four-in-ten say this has ever happened to them.

Differences by school level

Elementary school teachers (26%) are more likely than middle and high school teachers (18% and 16%) to say they experience verbal abuse from students a few times a month or more often.

And while relatively small shares across school levels say students are physically violent toward them a few times a month or more often, elementary school teachers (55%) are more likely than middle and high school teachers (33% and 23%) to say this has ever happened to them.

Differences by poverty level

Among teachers in high-poverty schools, 27% say they experience verbal abuse from students at least a few times a month. This is larger than the shares of teachers in medium- and low-poverty schools (19% and 18%) who say the same.

Experiences with physical violence don’t differ as much based on school poverty level.

Differences by gender

Teachers who are women are more likely than those who are men to say a student has been physically violent toward them. Some 43% of women teachers say this, compared with 30% of men.

There is also a gender difference in the shares of teachers who say they’ve experienced verbal abuse from students. But this difference is accounted for by the fact that women teachers are more likely than men to work in elementary schools.

Addressing behavioral and mental health challenges

Eight-in-ten teachers say they have to address students’ behavioral issues at least a few times a week, with 58% saying this happens every day .

A majority of teachers (57%) also say they help students with mental health challenges at least a few times a week, with 28% saying this happens daily.

Some teachers are more likely than others to say they have to address students’ behavior and mental health challenges on a daily basis. These include:

• Women: 62% of women teachers say they have to address behavior issues daily, compared with 43% of those who are men. And while 29% of women teachers say they have to help students with mental health challenges every day, a smaller share of men (19%) say the same.
• Elementary and middle school teachers: 68% each among elementary and middle school teachers say they have to deal with behavior issues daily, compared with 39% of high school teachers. A third of elementary and 29% of middle school teachers say they have to help students with mental health every day, compared with 19% of high school teachers.
• Teachers in high-poverty schools: 67% of teachers in schools with high levels of poverty say they have to address behavior issues on a daily basis. Smaller majorities of those in schools with medium or low levels of poverty say the same (56% and 54%). A third of teachers in high-poverty schools say they have to help students with mental health challenges every day, compared with about a quarter of those in medium- or low-poverty schools who say they have this experience (26% and 24%). 

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Table of contents, ‘back to school’ means anytime from late july to after labor day, depending on where in the u.s. you live, among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

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