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Data Science in Education

Data science is a data interpretation and modeling method with applications across industries. With a degree in data science, or related programs such as statistics or computer science, students can stand out in the job market for highly lucrative career spots.

Data science applications in education are just as varying. Educators spend their time with students and need more time to analyze data. This, however, makes data analysis more important to them.

Data scientists utilize their programming, statistics, and content skills to develop data models that help educators do their jobs better. They also directly teach educators how to understand data at a deeper level, so they can actively apply it in educational settings to benefit student outcomes.

Likewise, data science in education is used to ensure data security for students and staff and data organization for system-wide use. This is partially through providing proper data collection by filtering out irrelevant or dangerous incoming data.

When data is not taken seriously in educational settings, research from the U.S. Department of Education shows that grades and test scores drop, and student behavior is worsened. Hence, data scientists in education are indispensable for optimal functioning.

Here are the top data science applications in education:

Data Science Applications in Education

This is different in higher education. Educational bureaucracies are present in grades K-12, with comparable data needs.

They are sifting through this data to figure out which is one of the specialties of data science and is central to data science applications in education.

Education is one of many disciplines here with many functions, however. Data science itself is different in all cases. Some data scientists specialize in database architecture building, while others interpret and analyze data to generate comprehensible reports for educators.

Data Security

Because of the prevalence of the internet in all realms of life, cyber security has become a hot-button topic. It will increasingly continue to do so indefinitely.

That’s why the Bureau of Labor Statistics predicts that jobs related to cyber security and computer science will generally grow from 2021-2031, making such jobs lucrative and highly secure.

For computers to function well, they need good cybersecurity software to prevent hackers from obtaining sensitive data and exploiting it.

This software should also detect potentially threatening data downloaded on one’s computer.

Data scientists can function as the cyber-security software of an entire organization, storing data manually and protecting it in real time from potential online threats.

Data science in education is, therefore, fundamental. Moreover, the data records educational intuitions possess and which they need to function appropriately contain highly sensitive information, including the personal and health information of students, faculty, and staff.

Hence, educational institutions need something more substantial than software to protect and manage their data.

The more sensitive the data is, and the larger the institution becomes, the more data science becomes necessary. This is exactly why data scientists are also critical to the government, healthcare, and the financial industry.

Data Collection

The larger an institution becomes, the more data it receives and transmits.

Most of the data anyone online receives daily could be more valuable. However, collecting such information over time can lead to needless disorganization.

Data scientists control data collection by institutions by filtering out useless and harmless data so that relevant information is given precedence and can be easier to find.

Have you ever noticed that spam still gets through even when anti-virus software programs are applied to one’s email?

Data science in education can be noticed when most student, staff, or faculty emails receive little spam. This is because a data scientist has filtered the data, preventing spam from passing.

Therefore, data collection in data science dovetails with data security, as collecting data properly ultimately prevents viruses and compromising malware from entering one’s computer system.

When such harmful data passes through, this can cause institution-wide malfunctions that slow down administrative processes and sometimes even delay proper payrolls.

Data Interpretation

Data interpretation is one of the most valuable functions of data science in education.

While educators chronically receive data, they can rarely use it if it needs to be interpreted and filtered.

Educators, however, need to use it. Having data related to student performance metrics can help them personalize their approaches to teaching, which according to the U.S. Department of Education, significantly improves grades, test scores, and student behavior.

Teachers provide data to data scientists, including a combination of teaching methods, individual student performance records, and interpersonal student responses to both.

Data scientists can use this information to create models that individualize approaches to teaching each student. For example, because not all students learn alike, traditional lecturing in front of a classroom with a chalkboard and PowerPoint cannot be the only method used. Students need more attention than that.

Educational data-driven initiatives work better than such traditional methods. This is because they not only adapt to personalize education from student to student but also adapt to unique changes in student learning methods throughout their development.

Higher education institutions can also use data scientists in education to train educators on how to approach their teaching practices with data in mind.

Receiving usefully interpreted data reports from a data scientist is one thing. However, deeply comprehending the data, so it is applicable in classroom or lecture hall settings is another. Using data this way requires formal training, which teachers seldom receive in graduate-level teaching school courses. Personalized education requires the collaboration of data scientists and educators in learning how to use data to better student outcomes.

In many cases, schools aren’t well-funded or well-staffed enough for teachers to give each student immense levels of one-on-one time.

Therefore, the data reports used by data scientists can help educators alter their lectures to include other forms of learning than simply listening or reading the chalkboard.

In all cases, data science in education reaps an immense degree of benefits from the products of data interpretation.

Data Science in Education Research

Graduate students in almost all academic disciplines need an understanding of data analysis to produce research and publish papers in academic journals.

In other words, to conduct academic research, academics must have a good understanding of data science. This is because most academic research in scholarly journals consists of interpreted data from which academics and scientists can draw conclusions or discussions.

Data scientists can find themselves tutoring graduate students across all university departments and even adjunct professing courses like psychological statistics.

This is because while many academic disciplines at a high-level use data science, often data science is not taught. Outside of direct data science coursework or related career or degree paths like statistics or actuarial science, one isn’t likely to get a lecture on how to code or create data models.

Career Change From Education to Data Science: How to Get Started as a Data Scientist

Often data scientists make more than their educator colleagues when they work in educational settings.

Educators tend to enter education out of genuine interest and to fulfill themselves because that’s all they’ve ever dreamed of doing. Unfortunately, many are also discouraged from education despite these positive feelings. Despite good union benefits, educators do not make an excellent salary on average.

If being an educator or teacher genuinely enriches your well-being, keep being one. However, data science is a lucrative and highly-involved alternative for those looking to change their careers without leaving education to improve their salary.

Entering data science requires formal training and education. There are dozens of certificate programs online that can lead to entry-level jobs in data science. Still, to get a high-salary job –specifically, a career in an educational institution—a master’s degree in data science is indispensable essential.

To be eligible for a master’s degree in data science, students must possess a bachelor’s degree in any field. Ideally, students should have a bachelor’s degree in data science. Still, in this context, it’s more likely that educators will have to start from scratch.

One way to get a head start on this is to look into your school’s IT department or other resource locations to see if data scientists are on hand who can function as a teacher.

It is always better to enter a degree with some knowledge of what you’re getting into than not, as –especially at the graduate level—coursework might come as a challenging shock.

Suppose you are an educator looking to make a lateral career move within the educational industry. In that case, data science is one of the best available. Click here to learn more about how you can make the leap from education to data science –and other related paths and programs — without leaving the ivory tower or any school grounds!

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Data Science in Education: Everything You Need to Know

Data science is revolutionizing every aspect of our lives, and education is no exception. In this digital age, where technology is intertwined with our daily routines, harnessing the power of data science provides numerous advantages for both students and educators. By leveraging data and analytics, educational institutions can personalize learning experiences, streamline administrative tasks, and make data-driven decisions to improve overall educational outcomes.

In this article, we’ll explore the benefits, applications, and considerations of data science in education.

• Understanding Data Science
• The Intersection of Data Science and Education
• How Data Science is Transforming Education
• Advantages of Data Science in Education
• Applications of Data Science in Education
• Considerations when Implementing Data Science in Education

1.  Understanding Data Science

Data science is a multifaceted discipline that involves extracting meaningful insights from vast amounts of data. It combines computer science, statistics, and domain knowledge to uncover patterns, make predictions, and inform decision-making. By utilizing sophisticated algorithms and machine learning techniques, data scientists can derive value from data and translate their findings into actionable solutions.

At its core, data science aims to understand the world through data. It involves collecting, cleaning, and analyzing data to uncover hidden patterns and correlations. The insights gained from data science can help us make informed decisions and drive innovation across various industries.

2.  The Intersection of Data Science and Education

The emergence of data science in education is redefining the educational landscape. Educators and institutions are increasingly recognizing the tremendous potential that data science holds in improving educational outcomes.

Data science in education isn’t just a passing trend; it has become an integral part of the educational ecosystem. With the rapid advancement of technology, the amount of data being generated in educational settings has skyrocketed. From online learning platforms to educational apps and learning management systems like Google Classroom , there is now an abundance of data that can be harnessed to gain insights and enhance educational practices.

One of the key drivers behind the emergence of data science in education is the need for personalized learning experiences. Every student is unique, with different strengths, weaknesses, and learning styles. Data science allows educators to analyze student data and tailor instruction to meet the specific needs of each individual. By identifying areas where students are struggling or excelling, educators can provide targeted interventions and support, ultimately maximizing learning outcomes.

Read next: How to use qualitative data in education for better student outcomes

3.  How Data Science is Transforming Education

Data science is revolutionizing education by enabling personalized learning experiences. By analyzing student data, such as academic performance, learning style preferences, and engagement levels, educators can tailor instruction to meet the unique needs of each student. This individualized approach maximizes learning outcomes and ensures that students receive the support they require.

Data science also offers opportunities for predictive analytics in education. By leveraging historical data, educators can identify students who may be at risk of dropping out or struggling academically. This early intervention allows educators to provide targeted support, ultimately increasing student success and retention rates.

Furthermore, data science can help identify patterns and trends in educational practices. By analyzing data from various sources, such as standardized tests, classroom observations, and student surveys, educators can gain insights into what teaching methods and strategies are most effective. This information can then be used to inform instructional design, curriculum development, and professional development initiatives.

Another area where data science is transforming education is in the field of educational research. Researchers can use data science techniques to analyze large datasets and uncover new insights into learning processes, educational interventions, and educational policies. This research can inform evidence-based decision-making and drive continuous improvement in educational practices.

Read next: How data analytics is reshaping the education industry

4.  Advantages of Data Science in Education

Enhancing learning experiences.

One of the significant advantages of data science in education is its ability to enhance learning experiences. By analyzing student data, educators can gain insights into individual learning styles and preferences. This allows them to adapt their teaching methods, instructional materials, and assessments to suit each student’s unique needs. As a result, students are more engaged, motivated, and invested in their learning journey.

For example, data science can help identify patterns in student performance and behavior. By analyzing data from various sources such as online learning platforms, classroom activities, and assessments, educators can identify areas where students may be struggling or excelling. This information can then be used to tailor instruction and provide targeted interventions to support student learning.

Data science can also help educators personalize the learning experience by recommending relevant resources and activities based on individual student data. By leveraging machine learning algorithms, educational platforms can analyze a student’s past performance, interests, and learning style to suggest personalized learning paths. This not only helps students to learn at their own pace but also ensures that they are exposed to content that is most relevant and engaging to them.

Streamlining Administrative Tasks

Data science also streamlines administrative tasks, allowing educators to focus more on teaching and supporting students. By automating mundane and time-consuming tasks, such as grading assessments and generating reports, educators have more time and energy to devote to instructional activities. Data-driven instruction improves efficiency and enables educators to provide a higher quality of education.

Furthermore, data science can help optimize resource allocation in educational institutions. By analyzing data on student enrollment, course demand, and resource utilization, institutions can make informed decisions about resource allocation. This includes determining the number of teachers needed, the allocation of classroom space, and the availability of learning materials. By optimizing resource allocation, educational institutions can ensure that students have access to the necessary resources and support for their learning.

Data science can also assist in identifying trends and patterns in student attendance and engagement. By analyzing data on student attendance, participation in class activities, and engagement with online learning platforms, educators can identify students who may be at risk of disengagement or dropping out. This early identification allows educators to intervene and provide additional support to these students, increasing their chances of success.

5.  Applications of Data Science in Education

Personalized learning through data science.

Data science enables personalized learning by leveraging student data to create tailored educational experiences. Through the analysis of student performance, interests, and progress, educators can develop personalized learning paths and recommend targeted resources. This individualized approach ensures that students receive the right support at the right time and enables them to reach their full potential.

Predictive Analytics in Education

Predictive analytics is another powerful application of data science in schools. By examining past student performance and behaviors, educators can identify patterns that indicate potential challenges or opportunities. This proactive approach allows educators to intervene early, provide tailored support, and prevent academic setbacks. Predictive analytics also facilitates resource allocation, helping educators allocate resources efficiently based on student needs.

6.  Considerations when Implementing Data Science in Education

Ethical considerations.

When implementing data science in education, it is crucial to address ethical considerations. Student privacy and data security must be paramount. Educators and institutions must handle student data responsibly, ensuring that it is kept confidential and used only for legitimate educational purposes. Transparency and informed consent are essential, as students and parents should be aware of how their data is collected, stored, and utilized.

Technical Challenges

Implementing data science in education also comes with technical challenges. Educational institutions must have the necessary infrastructure, tools, and expertise to collect, analyze, and interpret data effectively. Investing in reliable data systems, data governance, and data literacy training becomes imperative to fully harness the power of data science in education.

Ultimately, for data science to be successfully integrated into education, educators must have the necessary training and skills. Professional development opportunities should be provided to help educators develop data analysis skills, familiarize themselves with data tools and technologies, and understand how to interpret and apply data insights effectively. Empowering educators with data literacy enables them to leverage data science to inform their instructional practices and drive student success.

In conclusion, data science has the potential to revolutionize education by providing personalized learning experiences, streamlining administrative tasks, and enabling data-driven decision-making.

Venturing into the realm of data science can be both exhilarating and challenging. But, what if you had a tool to make sense of all that data and use it efficiently for better educational outcomes? That’s where the Inno™ Starter Kits shine. Tailored to make data science in education more accessible and actionable, these kits are a game-changer for educators eager to harness the power of data. Dive deeper and discover how Inno™ Starter Kits can be your ultimate companion in your data science journey in the educational sphere.

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Data Science in Education Using R

Second edition.

Ryan A. Estrellado, Emily A. Freer, Joshua M. Rosenberg, and Isabella C. Velásquez

📘 Notice! This is the website for the second edition of Data Science in Education Using R. For the first edition, visit datascienceineducation-1ed.netlify.app/

Welcome to Data Science in Education Using R! Inspired by {bookdown}, this book is open source. Its contents are reproducible and publicly accessible for people worldwide. The online version of the book is hosted at datascienceineducation.com .

There’s this story going around the internet about an eagle egg that hatches in a chicken farm. The eagle egg hatches near the chicken eggs. The local hens are so busy doing their thing that they don’t notice the baby eagle egg is not their own. The eagle chick is born into the world and, having no knowledge of its own eagleness, joins its new family on a nervous and exciting first day of life. Over the next few years the baby eagle lives as chickens live. It eats chicken feed, learns to fly in short choppy hops a few feet at a time, and masters the rapid head jabs of the chicken strut.

One day, while strutting around the chicken farm, the young eagle sees something soaring through the sky. The flying creature has long wings, which it stretches wide before tucking them back in and angling itself downward for a dive towards the earth. The sight of this other-worldly bird stirs something in the young eagle.

Over the next few weeks the eagle finds it can’t shake the vision of the soaring eagle from its mind. It tests the conversational waters during feeding time. It wonders out loud, “What if we tried to fly more than two feet off the ground?” The other chickens stare back. The young eagle, uncertain if these stares are ambivalence or the default chicken eye position, begins to ponder the only way forward. It must learn to fly high while living with the chicken family it loves.

This is a book about learning to program in R while working in education. It’s for folks who feel at home in the education community but are looking out into the world and wondering how to use data better. It’s about being a great educator and wondering if it’s too late to learn to code. It’s about being an educator who’s learning to code and wondering if there are others you can learn with.

We were on Twitter a lot in November of 2017. We talked about things like debugging R code, interpreting model coefficients, and working on spreadsheets with three header rows. We kept coming back to these topics over and over again. It was like having an obscure hobby with online friends because it’s hard to find local knitters who only knit Friends characters, or vinyl collectors who only collect Swedish disco albums. When you work as a data science consultant in education or as an educator learning data science, it’s hard to find that professional community that just gets you. Going to education conferences is great, but the eyes glaze over when you start talking about regression models. The data science conferences are super, but the group at the cocktail table gets smaller when you vent about the state of aggregate test score data.

We started talking about data science in education online because we wanted to be around folks who do data science in education. We wrote this book for you, so you can learn data science with datasets you can find in education work. We don’t claim to be experts at education or data science, but we’re pretty good at talking about what it’s like to do both in a time where doing both is just starting to take off.

So give your chicken family a big hug, open up your laptop, and let’s start learning together. Turns out, there are a lot more hatchlings wanting to be eagles and chickens at the same time.

Figure 0.1: The Tweet That Started It All

Acknowledgements

This work was supported by many individuals from the DataEdu Slack channel ( https://dataedu.slack.com/ ). Thank you to everyone who contributed code, suggested changes, asked questions, filed issues, and even designed a logo for us: Daniel Anderson, Abi Aryan, Jason Becker, William Bork, Jon Duan, Ben Gibbons, Erin Grand, Ellis Hughes, Ludmila Janda, Jake Kaupp, Nathan Kenner, Zuhaib Mahmood, David Ranzolin, Kris Stevens, Bret Staudt Willet, and Gustavo Velásquez.

Thank you to the data scientists in education that took time to share their stories with us: Isabella Fante, LaCole Foots, Tobie Irvine, Arpi Karapetyan, John LaPlante, and Andrew Morozov.

Thank you to the editor of this book at Routledge, Hannah Shakespeare. We appreciated Hannah’s incisive, constructive feedback, interest, and support for the book and our unique approach to writing it - one which involved writing the book “in the open” (through GitHub) and sharing it on a freely-available website.

Dedications

To my husband, Dan, who supports me every day and has believed in this book from day one. To my family and to Gus, who accompanied me on the journey.

To my wife, Lucy, and my sons, Dylan and Adam, for enduring so much typing during dinner. And to Dan Winters, for enduring so many plots over coffee.

To Mara and Sharla, for supporting me and cheering me on and reminding me that no matter how challenging it seemed, I could do the thing. To Hadley, for the retweet that changed my life and made this book possible. To Miriam, for the compassion and guidance and inspiration. And to Leo, Miles, Abby, and Jinx, who have all been a part of this journey with me.

To Katie and Jonah and to Teri, Joel, Aaron, and Jess, who took an interest in it from its beginning through its completion.

To my loving family, in particular my older brother Gustavo E., who never tells me to go read the manual.

If you would like to cite this book, please use the citation below:

Estrellado, R. A., Freer, E. A., Motsipak, J., Rosenberg, J. M., & Velásquez, I. C. (2020). Data science in education using R . London, England: Routledge. Nb. All authors contributed equally.

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Data science for analyzing and improving educational processes

• Published: 29 October 2021
• Volume 33 , pages 545–550, ( 2021 )

Cite this article

• Shadi Aljawarneh 1 &
• Juan A. Lara 2  

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In this full review paper, the recent emerging trends in Educational Data Science have been reviewed and explored to address the recent topics and contributions in the era of Smart Education. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, frameworks and techniques research projects in the area of Data Science applied to Education, using different approaches such as Information Fusion, Soft Computing, Machine Learning, and Internet of Things, among others. Based on this systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area.

Avoid common mistakes on your manuscript.

Introduction to the domain of research

The term Data Science (DS) refers to an interdisciplinary field that involves a series of methods, processes and systems, with the aim of extracting knowledge from data. DS, which is a discipline very related to Computing, has proved to be of great application in very different domains, particularly Education (Klašnja-Milićević et al., 2017 ). In Educational environment, there are lots of learning-related processes involved, and great amounts of potential rich data are generated in educational institutions continuously. In order to extract knowledge from those data for a better understanding or learning-related processes, the use of DS approach seems to be useful and necessary (Mitrofanova et al., 2019 ).

The application of DS in the field of Education may result of great interests for involved stakeholders (students, instructors, institutions, …) since the extracted knowledge from educational data would be useful to deal with educational problems such as students’ performance improvement, high churning rates in educational institutions, learning delays, and so on. There are a series of disciplines related to Educational Data Science, such as Educational Data Mining and Learning Analytics (Romero & Ventura, 2020 ), and all of them are of importance for this special issue.

In this introductory paper, Sect.  2 includes the summaries of the selected papers. Section  3 includes a set of recommendations for researchers, practitioners and scholars to improve their research quality in this area. In Sect.  4 , the conclusions have been dawn.

Related work: the selected papers

The purpose of this special issue is to present original contributions of studies on the application of DS techniques in order to extract knowledge of interest for educational stakeholders as long as the analysed data represent a particular educational process and the knowledge extracted is used to improve that process in some way. We have considered papers that include discussions of the implementation of software and/or hardware approaches that also focus on the implications for the improvement of any learning process. Priority has been be given to papers that demonstrate a strong grounding in learning theory and/or rigorous educational research design. We have considered studies focused on tertiary and further education of any type (e-learning, blended and traditional education). All accepted works include an exhaustive validation and include extraordinarily new ideas in the area.

The special issue includes 10 papers, which have been subject to a rigorous peer-review process. Each paper has been reviewed by three independent experts. The rest of this section includes a summary of the selected papers.

The research presented in “Multilayered-Quality Education Ecosystem (MQEE): An Intelligent Education Modal for Sustainable Quality Education”, by Verma et al., intends to unfold some hidden parameters that are affecting the quality education ecosystem (QEE). Academic loafing, unawareness, non-participation, dissatisfaction, and incomprehensibility are the main parameters under this study. A set of hypothesis and surveys are exhibited to study the behaviour of these parameters on quality education at the institution level. The bidirectional weighted sum method is deployed for precise and accurate results regarding boundary value analysis of the survey. The association between parameters understudy and quality education is illustrated with correlation and scatter diagrams. Academic loafing, the hidden and unintended rudiment that affects the QEE is also defined, intended and explored in this work.

In the paper “Improving prediction of students’ performance in Intelligent Tutoring Systems using attribute selection and ensembles of different multimodal data sources”, Chango et al. intend to predict university students’ learning performance using different sources of performance and multimodal data from an Intelligent Tutoring System. They collected and preprocessed data from 40 students from different multimodal sources: learning strategies from system logs, emotions from videos of facial expressions, allocation and fixations of attention from eye tracking, and performance on post-tests of domain knowledge. Their objective is to test whether the prediction could be improved by using attribute selection and classification ensembles.

In “Automated Text Detection from Big Data Scene Videos in Higher Education”, Manasa et al. employed a novel approach to clean up the video frames to feed a neural network model based on region proposal network (RPN) with convolutional neural networks by finding appropriate anchor ratios to extract the text candidates. The trained their model with extracted frames to predict for the test videos. The proposed method is evaluated on ICDAR Video text benchmark datasets and few publicly available test datasets to achieve high recall.

In the paper “Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques”, by Sáiz-Manzanares et al., the main objective is to test the effectiveness of three teaching modalities (all using Online Project-based Learning -OPBL- and Flipped Classroom experiences and differing in the use of virtual laboratories and Intelligent Personal Assistant -IPA-) on Moodle behaviour and student performance taking into account the covariate "collaborative group". Both quantitative and qualitative research methods were used. With regard to the quantitative analysis, differences were found in student behaviour in Moodle and in learning outcomes, with respect to teaching modalities that included virtual laboratories. Similarly, the qualitative study also analysed the behaviour patterns found in each collaborative group in the three teaching modalities studied.

The study titled “Fuzzy-based Active Learning for Predicting Student Academic Performance using autoML: a step-wise approach”, by Tsiakmaki et al., introduces a fuzzy-based active learning method for predicting students' academic performance which combines, in a modular way, autoML practices. A lot of experiments were carried out, revealing the efficiency of the proposed method for the accurate prediction of students at risk of failure. These insights may have the potential to support the learning experience and be useful the wider science of learning.

In the paper “Peer Assessment Using Soft Computing Techniques”, by Pinargote-Ortega et al., a peer assessment scenario was applied at the Universidad Técnica de Manabí (Ecuador). Students and professors evaluate some works through rubrics, assign a numerical score, and textual feedback grounding the reasons why such numerical score is determined. Interesting scenario to detect inaccuracy between both assessments. It is proposed a model with soft computing techniques to detect inaccuracy and reduce the workload of the professor in the correction process.

In “A Novel Automated Essay Scoring Approach for Reliable Higher Educational Assessments”, Beseiso et al. present a transformer-based neural network model for improved Automatic Essay Scoring performance using Bi-LSTM (Bidirectional Long Short-Term Memory) and RoBERTa language model based on Kaggle’s ASAP (Automated Student Assessment Prize) dataset. The proposed model uses Bi-LSTM model over pre-trained RoBERTa language model to address the coherency issue in essays that is ignored by traditional essay scoring methods, including traditional Natural Language Processing pipelines, deep learning-based methods, a mixture of both. The comparison of the experimental results on essay scoring with human raters concludes that the proposed model outperforms the existing methods in essay scoring in terms of QWK (Quadratic Weighted Kappa) score.

The main goal of the research presented in the paper “Personalized training model for organizing blended and lifelong distance learning courses and its effectiveness in Higher Education”, by Bekmanova et al., is to improve the personification of learning in higher education. The proposed flexible model for organizing blended and distance learning in higher education involves the creation of an individual learning path through testing students before the start of training. Based on the learning outcomes, the student is credited to the learning path. The training path consists of mandatory and additional modules for training; additional modules can be skipped after successfully passing the test, without studying these modules. The paper examines the composition of intelligent learning systems: student model, learning model and interface model.

In the paper “IoT Text Analytics in Smart Education and Beyond”, Mohammed et al. highlight the main components of IoT analytics, along with a comprehensive background of text analytics used techniques and applications. This paper provides a comprehensive survey and comparison of the leveraged IoT Text Analytics models and methods in Smart Education and many other applications.

Finally, in “A Framework to Capture the Dependency between prerequisite and Advanced Courses in Higher Education”, Hriez & Al-Naymat propose a new graph mining algorithm combined with statistical analysis to reveal the dependency relationships between Course Learning Outcomes (CLOs) of prerequisite and advanced courses. In addition, a new model is built to predict students’ performance in the advanced courses based on prerequisites. The evaluation proves that the proposed algorithm is accurate, efficient, effective, and applicable to real-world graphs more than the traditional algorithm.

Discussions and recommendations

A number of recommendations have been suggested to improve the research in this field as follows:

The papers selected for inclusion in this special issue have described a number of data science techniques for extracting knowledge for educational data. However, the knowledge extracted is only applicable to the problem addressed. It is desirable to obtain general models that can be applied in other scenarios (López-Zambrano et al., 2021 ).

Most research is focused on analyzing only one source of educational data. However, in current smart classrooms, a lot of different multi-source and multi-modal data are recorded and it can be very interesting to fuse those data in order to obtain richer and more accurate models.

Many DS approaches generate models that are hard to interpret, in spite of the fact that they can obtain very accurate results. However, interpretability is a requirement in Education sometimes, since it helps understand the learning processes and, therefore, improve them by interventions.

Current educational models are designed on the premise of ubiquity, particularly in the event of emergencies such as the one caused by the Covid-19 pandemic (Maatuk et al., 2021 ). In this scenario, the student needs to be able to self-regulate his or her learning, which is hard sometimes. It is very important to count on tools for personalized learning that adapt to each student depending on his or her emotions at a certain moment. The use of virtual affective agents is a promising line nowadays.

Conclusions

In this special issue, 10 selected papers have been included that present important advancements in the area of Educational Data Science. The selected papers include interesting studies about the development of this area, works about promising existing technologies and outstanding research about theories and methods that will play a crucial role in the future of this discipline.

As guest editors, we are aware of the fact that this issue cannot completely cover all the advancements in this area, but we expect that this special issue can stimulate further research in the domain of Educational Data Science.

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Acknowledgements

We would like to thank the referees who have reviewed the papers for providing a valuable feedback to authors. We would also like to thank the authors for their manuscripts that represent an important contribution to the existing knowledge in the area. Finally, we would like to thank Professor Stephanie L. Moore, Editor-in-Chief, and editorial assistants of Journal of Computing in Higher Education for their support during the preparation of this issue.

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Aljawarneh, S., Lara, J.A. Data science for analyzing and improving educational processes. J Comput High Educ 33 , 545–550 (2021). https://doi.org/10.1007/s12528-021-09299-7

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Using Data Science to Transform K-12 STEM Education

Blikstein is now an internationally-known expert in educational technology and learning analytics, an associate professor of communications, media and learning technology at Teachers College, and an affiliate associate professor of computer science at Columbia University. He says the school was a “formative experience” that shaped his views on education and influenced his career. “It was a place where it was a pleasure to learn. In my work, I try to come up with ways that reproduce that joyous experience of learning for all students.”

He pioneered the practice of bringing makerspaces to public schools, where students work together on projects in free and open environments—like in his elementary school. Following that success, he created FabLearn in 2010 and built digital fabrication labs in middle and high schools in Russia, Mexico, Spain, Australia, Finland, Brazil, Denmark, Thailand, and the U.S. Overall, 22 countries have used his ideas to create fab labs and makerspaces in schools. His overall objective is to help students learn by building and creating—to learn by “systematic exploration”.

At Teachers College, Blikstein, who received his doctorate in learning sciences from Northwestern University, directs the Transformative Learning Technologies Lab and FabLearn. His team conducts research on how new technologies can improve K-12 learning in science, technology, engineering, and math. One of the team’s major initiatives is to expand the makerspace movement into public schools, particularly less affluent districts that serve underprivileged students. But to do that, Blikstein must first prove to policymakers that makerspaces spur creativity and enhance learning. And to make that case, he needs one essential thing: data.

“Public school systems will only adopt makerspaces if we collect data that measure their success,” Blikstein says. “It’s difficult to measure creativity, but my teams have advanced the research methods to help us do that.”

Blikstein, who joined the Data Science Institute (DSI) this semester, uses a novel data collection method called multimodal learning analytics . In pilot studies, he installs cameras and sensors that detect how students move and work in makerspaces. He also uses biosensors that determine students’ stress levels and eye trackers to track where they focus their attention. Afterward, he applies machine learning techniques and algorithms to mine that data for interesting patterns—patterns about how students move their hands, their eye movements, and their interest levels. His data helps teachers and researchers understand children’s actions: if they’re learning, if they are engaged, or if they are too stressed out to concentrate.

“We analyzed students as they worked on hands-on activities like building robots and found that children who are more active—the ones in whom we detected more hand movements and gestures—tend to learn more. But we were surprised to find that a better predictor of learning was not how active students were, but rather how many times they alternated between being active and passive while working on a project. Our hypothesis is that those moments of physical inactivity are actually for reflection and systematization of knowledge, so one important takeaway from this research is that we need to build moments of reflection into hands-on activities. Not only hands-on, but heads-in.”

Blikstein says he joined DSI because he was drawn to the “data for good” mission and its insistence on the responsible use of data. For example, people are not always aware of how their data is being used, nor how profitable it is to commercial entities. He thinks it is important to teach K-12 students about “surveillance capitalism”, or how some companies harvest personal data to predict behavior and persuade consumers. “We have a generation of middle and high school students who are generating tons of data, but they don’t really understand it.”

Students typically learn tools from the 19 th century, but Blikstein says they should be learning modern data science by working on hands-on projects that help society, including studying the spread of coronavirus. “So much of the discussion today about the virus revolves around data science. And the tools scientists use to study the virus—the tracking maps, the curves, and forecasts, or the graphs that illustrate how the virus spreads—are all data science tools,” he notes. “Why not teach students to use data science to understand the science behind the virus, so they can’t be hoodwinked by those who discount the use of science to understand problems like the virus?”

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Foundations of Data Science for Students in Grades K-12: A Workshop

We live in a world defined by data. Individuals who can work with data are in high demand, but too few professionals are prepared to meet the need. Ultimately, the challenge reaches well down into the K-12 sphere -- our future data scientists are in today's elementary, middle, and high schools. This summer, there will be a two-day workshop to delve more deeply into the research around these issues. The workshop will bring together a diverse set of stakeholders to advance the conversation around what is needed in grades K-12 to enable students to navigate the emerging data-driven world.

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Foundations of Data Science for Students in Grades K-12: Proceedings of a Workshop

On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled Foundations of Data Science for Students in Grades K–12. Speakers and participants explored the rapidly growing field of K-12 data science education, by surveying the current landscape, surfacing what is known, and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators. To support these conversations, four papers were commissioned and discussed during the workshop. This publication summarizes the presentations and discussion of the workshop.

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• What competencies make up data fluency?
• How can these data fluency competencies be measured? 

Tools and instruction

• What kinds of learning experiences might help bring about these data fluency competencies?
• What tools and data sets are needed to support young learners in acquiring data understanding and skills?
• How can K-12 data science education be designed to specifically reach students who have been traditionally marginalized and/or underrepresented in STEM?

Integrating datascience into the K-12 system

• How can learning with data be meaningfully integrated with K–12 education, in both STEM and non-STEM classes?
• How well prepared is the current teacher workforce for teaching data science-related content in K-12 settings? What strategies can be used to enhance teachers’ expertise related to data science?

The evidence base and future directions

• What bodies of research can be leveraged to gain insight on the development of data fluency and how best to support students?
• What are the critical gaps in the current knowledge base?
• What are the highest priority next steps for research and practice?

The workshop planning committee will refine the specific topics to be addressed, develop the agenda, and select and invite speakers and other participants. After the workshop, a proceedings of a workshop will be prepared by a designated rapporteur in accordance with institutional guidelines. 

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7 Applications of Data Science in Education

• Bhumika Dutta
• Aug 30, 2021

Introduction

Data is such an asset that can be used by people to accomplish many feats. With the advancement of technology, the availability of data is also increasing and data science has been successful in analyzing, managing, and tackling the data every day. 

Due to this, many sectors have readily incorporated data science under their wings. It has revolutionized all the industries and helped them increase their performance and efficiency. There are many applications of data science in different fields and one of them is the field of education. 

Education plays a vital role in the upliftment of society and it is very important to have a strong and developed educational system. There is a huge amount of data coming from the educational sector. K-12 school and district records, digital archives of instructional materials and gradebooks, and student answers on course surveys are all examples of educational data. 

Data science of actual classroom interaction is also becoming more popular and common - it may be used to capture how classroom management and education are carried out.

Educational data is becoming increasingly valuable in higher education, as a growing number of online courses are being used. It even extends to the private sector, where personnel are educated and task issues are solved via online forums, threads, and distributed problem-solving methods through assignments. 

According to this source , here are many advantages that we will get on using data science in education, like:

Educational data science would prepare teachers to investigate various types of educational data, as well as to give meaning to educational systems, their issues, and prospective remedies, and to build a deeper knowledge and experimentally verified forms of answers.

Educators would be able to undertake data visualisation , data reduction and description, and prediction tasks with the help of educational data science.

For practitioners, data visualisation may make information more intuitive and consumable.

Many complicated records and fields of data about pupils can be deciphered via data reduction.

In this article, we are going to discuss the applications of Data Science in the field of education and how it helps in the betterment of the sector.

(Must read: Data science in risk management )

Applications of Data Science in Education:

Student assessment data:.

In a classroom, there are many different types of students who are taught at once by a single teacher. It is very common for a percentage of students to excel and for a number of students to not understand the class properly. 

Assessment data can help the teachers determine their students’ understanding and modify their teaching strategies for the future. 

Earlier, the assessment techniques were not in real-time, but as Big Data analytics advances, it became possible for the teachers to understand the requirements of their students in real-time through the performance of their students. 

There are tools like ZipGrade that help in quicker assessments through multiple choice questions as it provides summaries and insights. This process, although beneficial, can be a little tedious and time-consuming.

Social skills:

Social skills are very important for any student as it plays a huge role in their academic as well work life. Without social or emotional skills, a student cannot connect or interact with his/her fellow peers and hence, fail to develop a relationship with his surroundings. 

Educational institutions have a critical role to play in supporting the development of social-emotional skills. This is an example of a non-academic talent that has a significant impact on pupils' learning skills. 

Even though there have been statistical surveys that can assess these skills, with the advancement of technology, now there are Data science techniques that can help in the better assessment. It is feasible to acquire such huge amounts of data and integrate it with current technologies to create better results using formalised knowledge discovery models in Data Science and  Data Mining approaches. 

Furthermore, data scientists can use the collected data to use various predictive analytical tools to assist teachers in understanding the students' motivation for studying the course.

Data of Guardian:

Guardians/Parents also play an essential role in the education of children. Many troubled students perform below average in the school due to negligence of parents. So, it becomes very important for the teachers to communicate with the parents/guardians of all the students by arranging regular parent-teacher meetings. 

To ensure maximum attendance in those conferences, data science can be used. It is used to filter out the students whose parents did not show up and analyse the history or similarity between all the families with such behaviour. This can help the teachers personally communicate with those parents instead of sending generic emails or messages to all the parents continuously. 

(Must read: Applications of IoT in education )

Curricular data:

With the amount of competition increasing in the field of education, schools and  universities need to stay up to date with industry expectations in order to deliver relevant and improved courses to their students. 

Keeping up with the expansion of industry has become a huge problem for colleges, and hence they are adopting Data Science tools to evaluate market trends in order to accommodate this. 

Data science may be beneficial for studying industrial patterns and assisting course makers in imbibing relevant subjects by using various statistical measurements and monitoring approaches. Furthermore, colleges may use predictive analytics to evaluate demand for new skill sets and tailor courses to meet those needs.

Behavioral data:

There are many cases of misbehavior or indiscipline in educational institutions by students. Every time something like that happens, a designated staff member is required to put an entry in the system. 

The course of action for every incident can be determined by judging the severity of the action, as every action should have separate punishments. This can be a time-consuming task for the staff as they have to go through all the logs and then determine the severity to avoid unfair punishment. This is where natural language processing may assist. (Learn more through this NLP tutorial )

There should be plenty of log entries to utilise to create a severity level classifier inside a school that has been around for a few years. If the disciplinary staff and instructors could see it as well, it would save them time as the entire process will become automated. 

Instructor performance:

Students' grades are determined by their teachers. While numerous evaluation approaches have been employed to evaluate teacher effectiveness, the majority of them have been manual in nature. 

Student evaluations of instructors' performance, for example, have long been the ultimate standard for measuring instructional techniques. All of these approaches, however, are inefficient and time-consuming to evaluate. 

Reading student feedback and coming up with an analogy is also a time-consuming process. It is now feasible to track teacher performance because of a breakthrough in data science. This is true not only for historical data, but also for real-time data. 

As a consequence of the real-time monitoring of teachers, thorough data gathering and analysis are feasible. With big data technologies , we can also store and handle unstructured data such as student reviews. It is also feasible to evaluate the feelings of the reviews using Natural Language Processing and offer a thorough study of instructor performance.

(Related blog: 5 Steps of data analysis )

Student Demographics:

There are usually a lot of students attending a particular educational institute if it has been established for a while. There is a lot of data and demographics related to students like attendance, performance, extra-curriculum or even dropout rates that needs to be recorded by the institution. 

It is impossible for the teachers or staff to keep track of all the data personally, so they can take the help of Data science. Data about students may be found in systems like PowerSchool, ATS, from teachers, and in school network-only data pools. 

Using this data, the teachers can recognise the students who are performing poorly and solve any issues they might be facing. 

Data science in education is very essential as it ultimately promises a better future for society. Many modern educational platforms offer mixed autonomy, which presents an intriguing challenge in terms of data science approach. 

(Suggested blog: Benefits of AR in education )

Despite the fact that the learner has a lot of freedom in how she goes through the course, the teaching system has a lot of power to make recommendations and steer the student's learning path.

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Data Science is transforming the education sector, bringing it into the digital age. Discover the multiple possibilities offered by Big Data and AI for school systems, and why it's a prime field for Data Scientists.

The education sector generates large volumes of data. Schools, colleges and universities have at their disposal a vast amount of data on students through their school records, grades and results, or even their information sheets.

This data can be analyzed and exploited in numerous ways to open up new possibilities. In this way, Data Science and Machine Learning are helping to modernize the world of education.

How is Data Science transforming the education sector?

Data Science can be used in many ways in the field of education. Here are the main current possibilities.

Adaptive learning

Every student is unique. Everyone learns in a different way. This makes it very difficult, if not impossible, to choose an ideal method for all students in the same class. By using a uniform method, some will learn very quickly, while others will be “dropped” along the way and remain on the sidelines.

Big Data and Data Science enable teachers to use adaptive learning techniques. Depending on each student’s abilities and learning style, it is possible to choose personalized techniques optimized on an individual scale.

Informing parents

Student data can be analyzed to assess their performance. In this way, teachers can inform parents of any problems that may be affecting their children’s performance in different subjects.

Parents can then better supervise their children and monitor their activities. Similarly, this approach enables schools to take various initiatives to improve the education system and enhance the learning experience for students.

Teacher evaluation

School principals can use Data Science to better “monitor” and evaluate teachers. In particular, this enables them to check their methods, and identify which are the most effective.

The analysis of data such as student results, absenteeism rates or their own feedback can highlight the strengths and weaknesses of individual teachers. Teachers can then use the results as a basis for progress and improvement.

Improving student performance

By analyzing student data, we can assess their performance in depth, and improve it by taking appropriate action. Schools can make changes that benefit students, and help them solve their problems.

When a student’s grades deteriorate day by day, the teacher can use Big Data to identify the cause of the problem and help remedy it. Schools themselves can identify their weaknesses and find areas for improvement to maximize their students’ results.

Predicting student success

Data Science and Machine Learning can also predict a student’s success in a particular course or across all subjects. A system trained on data from previous students can determine whether a student is likely to fail or has every chance of succeeding.

It is therefore possible for AI to alert teachers if a student needs extra attention. The teacher can then create optimal learning conditions for each student.

In the same vein, Big Data also makes it possible to track how quickly students finish their tests. This means they can be given more or less time, depending on their real needs. If a course’s absentee rate soars, changes can also be made.

Helping students choose their path

The world of work is constantly evolving, but school curricula sometimes seem to stand still. As a result, the education sector can be “out of touch” with professional reality.

Data Science enables us to keep abreast of market trends , so that we can better prepare students for the future. Curricula can be updated based on data, and adapted to meet modern business requirements.technology to better guide those struggling to find their way…

Going a step further, artificial intelligence can even predict each student’s vocation. Based on the data, the system will be able to suggest that a student work in the industry or sector that best suits him or her. Guidance counsellors can therefore draw on this technology to better guide those struggling to find their path…

Attracting students

Private and public higher education institutions can use student data analysis to discover which programs captivate and interest students the most.

In this way, institutions can increase their attractiveness. Data Scientists can therefore help schools to better understand their students, and offer them infrastructures and teaching that meet their needs.

Data-driven decision-making

If a school decides to test a new teaching or assessment technique, it can turn to Data Science to verify its effectiveness. For example, these new methods can be tested only in certain classes, and their results compared with those of other students.

If results are up in the class where the method is being tested, and teachers see increased student engagement, it makes sense to generalize it. Data science can therefore help managers and teachers to make the best decisions for the greatest number of people.

Some examples of how Data Science is used in education

Around the world, many educational establishments are already using Data Science in their classrooms and administrations. Here are just a few examples.

The University of Florida uses Data Science to analyze student data. This makes it possible to monitor and predict student performance. The impact is tangible, as student grades have risen since the implementation of this method.

Georgia State University also uses Data Science and Machine Learning tools to discover insights in student data. This makes it possible to identify classes where students’ grades are not satisfactory. A support program was developed to remedy the problem and improve results.

Thanks to this system, the graduation rate rose from 32% in 2003 to 54% in 2014. In addition, student data is used to solve student retention or dropout problems.

Arizona State University , considered one of the best universities in the USA, is of course exploiting Data Science. The mathematics department has developed a system called “Adaptive Learning”, based on the analysis of student data.

This system collects a wide variety of data on students, such as their grades, strengths and weaknesses, and interests. If a student starts to encounter difficulties, teachers will receive a notification.

They can then take the appropriate concrete measures. Once again, this system has significantly improved student performance. In addition, the drop-out rate has fallen by 5.4%.

For its part, the University of Nevada collects and analyzes student data to identify trends. It can then offer a personalized experience to each student.

The challenges of Data Science in education

According to a study published by the Publications Office of the European Union, the main change brought about by Big Data in education relates to the possibility of monitoring and evaluating educational systems.

By analyzing student data, it is possible, for example, to check whether different courses are of interest to them. Courses, programs and assessments can be adapted and personalized to improve results.

However, the use of Big Data in education is still limited by several obstacles. First of all, the use of Data Science in this field can also pose an ethical problem. Student data can be considered personal, even intimate.

Yet some institutions go so far as to monitor students’ personal blogs in order to incorporate them into their analysis systems. It is therefore important that limits are set and that the use of data is supervised.

Furthermore, the immense volume of data generated by students is difficult to process. Education systems have neither the skills nor the tools to analyze it properly. There is therefore a strong demand for data science professionals.

In conclusion, data science offers many opportunities for educational establishmentsnaround the world. The various existing tools enable schools to improve the fruits of their teaching.

Big Data analysis can be used to monitor and improve student and teacher performance. However, to fully exploit the data, the education sector needs Data Scientists.

If you’re interested in this field, you can easily make yourself useful by offering your skills as a data science expert. To acquire these skills, take a Data Scientest training course with us.

Now you know how Data Science will transform the world of education . Find out how it’s being used in the healthcare sector, and discover the different professions involved in Big Data.

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Title: deep learning for educational data science.

Abstract: With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of various interrelated research communities -- have turned their attention to leveraging these powerful algorithms within the domain of education. Use cases range from advanced knowledge tracing models that can leverage open-ended student essays or snippets of code to automatic affect and behavior detectors that can identify when a student is frustrated or aimlessly trying to solve problems unproductively -- and much more. This chapter provides a brief introduction to deep learning, describes some of its advantages and limitations, presents a survey of its many uses in education, and discusses how it may further come to shape the field of educational data science.

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Table of Contents

Practical applications of data science in education, inspiring the future with data science in education, effects of data science in education across workforce.

Data science has a massive impact across many facets of education — from customized learning experiences and more efficient administrative programs to augmenting educators in their daily teaching practices. 

Data, analytics, and artificial intelligence (AI) have become increasingly linked to the impact of the pandemic on the educational experience. Demand for virtual learning platforms and more personalized, interactive, AI-fueled learning tools are shaping the education market and contributing to innovative learning and tutoring techniques, and elearning platform development. The AI in education market already surpassed $1 billion in 2020 and is anticipated to grow at a CAGR of more than 40 percent between 2021 and 2027, reaching approximately $20 billion. 

Interestingly, the changing educational landscape is also being shaped by external demand for highly specific skill sets in the workforce as companies respond to digital disruption. As a result, the future of education and the future of the workforce are becoming intrinsically connected.  

Data science, data analytics , AI, cloud , and IoT are currently being leveraged to improve upon and personalize the education process and experience at both the K-12 and higher education levels.

Enhance Knowledge Retention 

IoT is one of the most adaptable technologies for the modern learning experience because IoT-enabled devices provide an accessible vehicle for communication, educational materials and resources, and supportive visualizations that improve knowledge retention and understanding. For example, a speech-to-text based system on such a device enables automated note-taking, which allows the student to simply absorb a lecture rather than divide their attention between listening and writing.

Anticipate Student Graduation and Dropout Rates

Higher education institutions are increasingly leveraging data science in education and machine learning solutions to predict scenarios such as which students are most likely to enroll, graduate, and be ready for a career in their chosen area of study. These capabilities also help educational providers track patterns in student dropout rates and the corresponding demographic and educational factors to predict potential future dropouts so they can proactively intervene and allocate resources to prevent it. 

Deeper Understanding of Student Progress

Advanced analytics, including AI, is also being used to gain insights into academic performance so that teachers, faculty, parents, and students can better understand how a student is responding to certain tests, for example. This information can then be applied to modify the corresponding learning or teaching path to improve academic outcomes. 

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Accessible Education for Dispersed Student Base

Additionally, many students are working remotely during the pandemic and virtual classrooms and cloud based e-learning platforms, along with customized applications, are becoming the norm as educators strive to deliver high quality learning programs for a dispersed student base. Augmented and virtual reality are similarly providing exciting simulations and gamification to promote a more immersive remote learning experience. 

Educational Chatbots

Intelligent chatbots are being used by schools to help address pervasive absenteeism. For example, an AI-driven two-way text messaging system was developed to help children who frequently miss classes by enabling teachers to touch base with the student’s family. It also offers tailored, around the clock support for students who are experiencing difficulties learning.

Molding the Future Workforce

While education institutions are investing in new technologies and data science in education to address current challenges and objectives, they’re also keeping the future in mind, particularly the future of the workforce. Data science disciplines, including data analytics, are vital to these institutions as they respond to changing economic and social conditions, evolving technologies and disruption, and a new era of work .

Academic institutions are increasingly dependent on analytics to inform the development of their academic programs, degrees, training, and certifications so that they better align with fluctuations in job demand and skill gaps. Many higher education institutions are turning to predictive analytics to assess future demand for employees according to various education levels, business domain, and specific roles. This enables them to prepare for emerging workforce trends and make insights-driven decisions regarding courses that are able to effectively address the changes and developments expected to occur in the labor market. 

A data-centric perspective of evolving workforce demands further allows academic institutions to help students become more career-ready and close the disparity between education and employability , which is a growing concern amongst employers.

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Data science is responsible for numerous opportunities and innovations in the education industry recently as the events and disruptions of the past 18 months capitulated the world into a new era of academic and workforce demands. To learn how to shape your own career path that could impact the future of education or the workforce, explore Simplilearn .  If you are looking forward to advancing your career in data science then enroll in Simplilearn’s Data Science course .

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• Open access
• Published: 13 January 2017

The use of data science for education: The case of social-emotional learning

• Ming-Chi Liu 1 &
• Yueh-Min Huang 1  

Smart Learning Environments volume  4 , Article number:  1 ( 2017 ) Cite this article

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The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education.

Introduction

Recently, AlphaGo, an artificially intelligent (AI) computer system built by Google, was able to beat world champion Lee Sedol at a complex strategy game called Go. AlphaGo’s victory shocked not only artificial intelligence experts, who thought such an event was 10 to 15 years away, but also educators, who worried that today’s high-value human skills will rapidly be sidelined by advancing technology, possibly even by 2020 (World Economic Forum 2016 ). Such potential technologies also catch some reflections of the relevance of certain educational practices in the future.

At the same time, emerging AI technologies not only pose threats but also create opportunities of producing a wide variety of data types from human interactions with these platforms. The broad availability of data has led to increasing interest in methods for exploring useful knowledge relevant to education—the realm of data science (Heckman and Kautz 2013 ; Levin 2013 ; Moore et al. 2015 ). In other words, data-driven decision-making through the collection and analysis of educational data is increasingly used to inform policy and practice, and this trend is only likely to grow in the future (Ghazarian and Kwon 2015 ).

The literature on education data analytics has many materials on the assessment and prediction of students’ academic performance, as measured by standardized tests (Fernández et al. 2014 ; Linan and Perez 2015 ; Papamitsiou and Economides 2014 ; Romero and Ventura 2010 ). However, research on education data analytics should go beyond explaining student success with the typical three Rs (reading, writing and arithmetic) of literacy in the current economy (Lipnevich and Roberts 2012 ). Furthermore, the availability of data alone does not ensure successful data-driven decision-making (Provost and Fawcett 2013 ). Consequently, there is an urgent need for further research on the use of an appropriate data-analytic thinking framework for education. The purpose of this paper is first to identify research goals to incorporate an appropriate data-analytic thinking framework for pursuing such goals, and second to present a case study of social-emotional learning in which we used the data science research methodology.

Defining data science

Dhar ( 2013 ) defines data science as the study of the generalizable extraction of knowledge from data. At a high level, Provost and Fawcett ( 2013 ) defines data science as a set of fundamental principles that support and guide the principled extraction of information and knowledge from data. Furthermore, Wikipedia defines data science (DS) as extracting useful knowledge from data by employing techniques and theories drawn from many fields within the broad areas of mathematics, statistics, and information technology. The field of statistics is the core building block of DS theory and practice, and many of the techniques for extracting knowledge from data have their roots in this. Traditional statistical analytics mainly have mathematical foundations (Cobb 2015 ); while DS analytics emphasize the computational aspects of pragmatically carrying out data analysis, including acquisition, management, and analysis of a wide variety of data (Hardin et al. 2015 ). More importantly, DS analytics follow frameworks for organizing data-analytic thinking (Baumer 2015 ; Provost and Fawcett 2013 ).

Vision for future education

Character. Disposition. Grit. Growth mindset. Non-cognitive skills. Soft skills. Social and emotional learning. People use these words and phrases to describe skills that they also often refer to as nonacademic skills (Kamenetz 2015 ; Moore et al. 2015 ). Among these various terms, the social-emotional skills promoted by the Collaborative for Academic, Social and Emotional Learning ( http://www.casel.org/ ) have mostly been accepted by the broader educational community (Brackett et al. 2012 ). A growing number of studies show that these nonacademic factors play an important role in shaping student achievement, workplace readiness, and adult well-being (Child Trends 2014 ). For example, Mendez ( 2015 ) finds that nonacademic factors play a prominent role in explaining variation in 15-years-old school children’s’ scholastic performance, as measured by the Program for International Students Assessment (PISA) achievement tests. Lindqvist and Vestman ( 2011 ) also find strong evidence that men who fare poorly in the labor market—in the sense of unemployment or low annual earnings—lack non-cognitive rather than cognitive abilities. Furthermore, Moffitt et al. ( 2011 ) find that the emotional skill of self-control in childhood is associated with better physical health, less substance dependence, better personal finances, and fewer instances of criminal offending in adulthood.

Due to a new understanding of the impact of nonacademic factors in the global economy, a growing movement in education has raised the focus on building social-emotional competencies in national curricula. In fact, countries like China, Finland, Israel, Korea, Singapore, the United States, and the United Kingdom currently mandate that a range of social-emotional skills be part of the standard curriculum (Lipnevich and Roberts 2012 ; Ren 2015 ; Sparks 2016 ). The movement involves some complex issues ranging from the establishment of social and emotional learning standards to the development of social and emotional learning programs for students, and to the offering of professional development programs for teachers, and to the carrying out of social and emotional learning assessments (Kamenetz 2015 ).

However, as argued by Sparks ( 2016 ), research studying these skills has not quite caught up with their growing popularity. A number of authors raise various directions for future research in social and emotional learning. Child Trends ( 2014 ), for instance, conducted a systematic literature review of different social-emotional skills and highlighted the need for further research on the importance of the following five skills: self-control, persistence, mastery orientation, academic self-efficacy, and social competence. Moreover, Moore et al. ( 2015 ) provide conceptual and empirical justification for the inclusion of nonacademic outcome measures in longitudinal education surveys to avoid omitted variable bias, inform the development of new intervention strategies, and support mediating and moderating analyses. Likewise, Levin ( 2013 ) and Sellar ( 2015 ) both suggest that the development of data infrastructure in education should select a few nonacademic skill measures in conjunction with the standard academic performance measures. Furthermore, Duckworth and Yeager ( 2015 ) note that how multidimensional data on personal qualities can inform action in educational practice is another topic that will be increasingly important in this context.

Although all those issues have varying significances regarding the measurement and development of social and emotional learning, the following two research goals are priorities for studies of social and emotional learning:

Developing assessment techniques,

Providing intervention approaches.

These two research areas strongly affect the development of social-emotional skills, which are the principal concerns of the domains of education and data science, and which can be studied to derive evidence-based policies. To consider these issues, this paper focuses on (a) the suggested data science research methodology that is applicable to reach these goals, and (b) the case study of social-emotional learning in which we used the data science research methodology.

Methodology review for data science

To better pursue those goals, it could be useful to formalize the knowledge discovery processes within a standardized framework in DS. There are several objectives to keep in mind when applying a systemic approach (Cios et al. 2007 ): (1) help ensure that the quality of results can contribute to solving the user’s problems; (2) a well-defined DS model should have logical, well-thought-out substeps that can be presented to decision-makers who may have difficulty understanding the techniques behind the model; (3) standardization of the DS model would reduce the amount of extensive background knowledge required for DS, thereby leading directly to a knowledge discovery process that is faster, more reliable, and more manageable.

In the context of DS, the Cross-Industry Standard Process for Data Mining (CRISP-DM) model is the most widely used methodology for knowledge discovery (Guruler and Istanbullu 2014 ; Linan and Perez 2015 ; Shearer 2000 ). It has also been incorporated into commercial knowledge discovery systems, such as SPSS Modeler. To meet the needs of the academic research community, Cios et al. ( 2007 ) further develop a process model based on the CRISP-DM model by providing a more general, research-oriented description of the steps. Applications of Cios et al. process model follow six steps, as shown in Fig.  1 .

Cios et al.’s process model. Source: adapted from Cios and Kurgan ( 2005 )

Understanding of the problem domain

This initial step involves thinking carefully about the use scenario, understanding the problem to be solved and determining the research goals. Working closely with educational experts helps define the fundamental problems. Research goals are structured into one or more DS subtasks, and thus, the initial selection of the DS tools (e.g., classification and estimation) can be performed in the later step of the process. Finally, a description of the problem domain is generated.

An example research goal would be: Since meaningful learning requires motivation to learn, researchers are interested in real-time modeling of students’ motivational orientations (e.g., approach vs. avoidance). Similarly, researchers might be interested in developing models that can automatically detect affective states (e.g., anxiety, frustration, boredom) from machine-readable signals (Huang et al. In Press ; Lai et al. 2016 ; Liu et al. 2015 ).

Understanding of the data

This step includes collecting sample data that are available and deciding which data, including format and size, will be needed. To better understand the strengths and limitations of the data, it also includes checking data completeness, redundancy, missing values, the plausibility of attribute values. Background knowledge can be used to guide these checks. Another critical part of this step is estimating the costs and benefits of each data source and deciding whether further investment in collection is worthwhile. Finally, this step includes verifying that the data matches one or more DS subtasks in the last step.

For example, researchers may decide to analyze log traces in an online learning session to make inferences about students’ motivational orientations. Moreover, researchers may choose to collect physiological data (such as facial expression, blood volume pulse, and skin conductance data) to develop models that can automatically detect affective states.

To date, DS has relied heavily on two data sources (Siemens 2013 ): student information systems (SIS, for in generating learner profiles, such as grade point averages) and learning management systems (LMS). For example, Moodle ( https://moodle.org/ ) and Blackboard ( http://www.blackboard.com/ ) can record logs for user activity in courses, forums, and groups. Linan and Perez ( 2015 ) suggest using Google Analytics to gather information about a site, such as the number of visits, pages visited, the average duration of each visit, and demographics. Massive open online courses (MOOCs) may also provide additional data sets to understand the learning process. For instance, Leony et al. ( 2015 ) show how to infer the learners’ emotions (i.e., boredom, confusion, frustration, and happiness) by analyzing their actions on the Khan Academy Platform. Moreover, a variety of physiological sensors have been used to increase the quality and depth of analysis (Kaklauskas et al. 2015 ), such as wearable technologies (Schaefer et al. 2016 ).

Social computing systems refer to the interplay between people’s social behaviors and their interactions with computing technologies (Cheng et al. 2015 ; Lee and Chen 2013 ). These systems can extract various kinds of behavioral cues and social signals, such as physical appearance, gesture and posture, gaze and face, vocal behavior, and use of space and environment (Zhou et al. 2012 ). Analyzing this information can enable the visually representation of social features, such as identity, reputation, trust, accountability, presence, social role, expertise, knowledge, and ownership (Zhou et al. 2012 ).

There are also open datasets that can be used for research on social and emotional analytics, such as PhysioBank, which includes digital recordings of physiological signals and related data for use by the biomedical research community (Goldberger et al. 2000 ); DEAP, a database for emotion analysis using physiological signals (Koelstra et al. 2012 ); and DECAF, a multimodal dataset for decoding user physiological responses to affective multimedia content (Abadi et al. 2015 ). Verbert et al. ( 2012 ) further review the availability of such open educational datasets, including dataTEL ( http://www.teleurope.eu/pg/pages/view/50630/ ), DataShop ( https://pslcdatashop.web.cmu.edu/ ) and Mulce ( http://mulce.univ-bpclermont.fr:8080/PlateFormeMulce/ ). As highlighted by Siemens ( 2013 ), taking multiple data sources into account provides more information to educators and students than a single data source.

Preparation of the data

This step concerns manipulating and converting the raw data materials into suitable forms that will meet the specific input requirements for the DS tools. For example, some DS techniques are designed for symbolic and categorical data, while others handle only numeric values. Typical examples of manipulation include converting data to different types and discretizing or summarizing data to derive new attributes. Moreover, numerical values must often be normalized or scaled so that they are comparable. Preparation also involves sampling, running correlation and significance tests, and data cleaning, which includes removing or inferring missing values. Feature selection and data reduction algorithms may further be used with the cleaned data. The end results are then usually converted to a tabular format for the next step.

Cios and Kurgan ( 2005 ) demonstrate that the data preparation step is by far the most time-consuming part of the DS process model, but educational DS research rarely examines this. Cristóbal Romero et al. ( 2014 ) survey the literature on pre-processing educational data to provide a guide or tutorial for educators and DS practitioners. Their results showed these seven pre-processing tasks: (1) data gathering, bringing together all the available data into a set of instances; (2) data aggregation/integration, grouping together all the data from different sources; (3) data cleaning, detecting erroneous or irrelevant data and discarding it; (4) user and session identification; identifying individual users; (5) attribute/variable selection, choosing a subset of relevant attributes from all the available attributes; (6) data filtering, selecting a subset of representative data to convert large data sets into smaller data sets; and (7) data transformation, deriving new attributes from the already available ones.

Mining of the data

At this point, various mining techniques are applied to derive knowledge from preprocessed data (see Table  1 ). This usually involves the calibration of the parameters to the optimal values. The output of this step is some model parameters or pattern capturing regularities in the data.

Evaluation of the discovered knowledge

The evaluation stage serves to help ensure that the discovered knowledge satisfies the original research goals before moving on. Only approved models are retained for the next step, otherwise the entire process is revisited to identify which alternative actions could be taken to improve the results (e.g., adjusting the problem definition or getting different data). The researchers will assess the results rigorously and thus gain confidence as to whether or not they are qualified. Scheffel et al. ( 2014 ) conduct brainstorming with experts from the field of learning analytics and gather their ideas about specific quality indicators to evaluate the effects of learning analytics. We summarize the results in Table  2 . The criteria provide a way to standardize the evaluation of learning analytics tools.

In addition, the domain experts will help interpret the results and check whether the discovered knowledge is novel, interesting, and influential. To facilitate their understanding, the research team must think about the comprehensibility of the models to domain experts (and not just to the DS researchers).

As suggested by Romero and Ventura ( 2010 ), visualizing models in compelling ways can make analytics data straightforward for non-specialists to observe and understand. For example, Leony et al. ( 2013 ) propose four categories of visualizations for an intelligent system, including time-based visualizations, context-based visualizations, visualizations of changes in emotion, and visualizations of accumulated information. The main objective of these visualizations is to provide teachers with knowledge about their learner’s emotions, learning causes, and the relationships that learning has with emotions. Verbert et al. ( 2014 ) also review works on capturing and visualizing traces of learning activities as dashboard applications. They present examples to demonstrate how visualization can not only promote awareness, reflection, and sense-making, but also represent learner’s goals and enable them to track progress toward these. Epp and Bull ( 2015 ) explored 21 visual variables (e.g., arrangement, boundary, connectedness, continuity, depth, motion, orientation, position, and shape) that have been employed to communicate a learner’s abilities, knowledge, and interests. Manipulating such visual variables should provide a reasonable starting point from which to visualize educational data.

Use of the discovered knowledge

This final step consists of planning where and how to put the discovered knowledge into real use. A plan can be obtained by simply documenting the action principles being used to impact and improve teaching, learning, administrative adoption, culture, resource allocation and decision making on investment. The discovered knowledge may also be reported in educational systems, where the learner can see the related visualizations. These visualizations can provide learners with information about several factors, including their knowledge, performance, and abilities (Epp and Bull 2015 ). Moreover, the results from the current context may be extended to other cases to assess their robustness. The discovered knowledge is then finally deployed.

However, according to the findings of Romero and Ventura ( 2010 ) survey, only a small minority of studies can apply the discovered knowledge to institutional planning processes. One of the barriers to this is individual and group resistance to innovation and change. Macfadyen and Dawson ( 2012 ) thus highlight that the accessibility and presentation of analytics processes and findings are the keys to motivating participants to feel positive about the change. Furthermore, the initial iteration may not be complete or good enough to deploy, and so a second iteration may be necessary to yield an improved solution. Therefore, the diagram shown in Fig.  1 represents this process as a cycle and describes several explicit feedback loops, rather than as a simple, linear process.

The case of social-emotional learning

In this section, we describe a case study in which we used the data science research methodology. The research was initiated with an instructor who wanted to understand university students’ motivation for learning during a semester. We thus started to help this instructor through understanding the problem (Step 1). The instructor explained that university students’ motivation for learning varies over a long semester. Monitoring their motivation can help in providing the right motivated strategies at the right time. We thus went on to the next step: understanding the data (Step 2). Although the use of the motivated strategies for learning questionnaire (MSLQ) (Garcia and Pintrich 1996 ) can gather data about students’ motivation, the questionnaire measures were quite long and were not sensitive to change over time. Inspired by the concept of teaching opinion survey implemented at the end of a semester, we decided to collect text data to evaluate university students’ motivation to learn. After repeatedly going through Steps 1 and 2, the research problem became “predicting university students’ motivation to learn based on teaching opinion mining.”

In this experiment, we employed the motivated strategies for learning questionnaire to collect the respondents’ motivation states. In addition, an open-ended opinion survey about the challenges they faced on the F2F course and recommendations to the teacher with regard to adjusting instruction was utilized to collect the text data. One hundred and fifty-two university students (62 females, 90 males; mean age ± S.D. = 21.1 ± 7.5 years) completed the survey for this study. They were taking face-to-face computer courses at four universities in southern Taiwan.

In the data preparation step (Step 3), we first calculated the mean score of MSLQ. Those respondents with a score less than the mean were labeled as low motivation (LM) students, while those with more than the mean were labeled as high motivation (HM) students. The sample consisted of 76 LM and 76 HM students (the mean was equal to the median).

We then continued to process the textual data. Because textual data is unstructured, the aim of data preparation is to represent the raw text by numeric values. This process contained two steps: tokenizing and counting. In the tokenizing step, we used the CKIP Chinese word segmentation system (Ma and Chen 2003 ) to handle the text segmentation. In the counting step, term frequency-inverse document frequency (TF-IDF) was used as an indicator parameter to extract text features. TF-IDF is a measure of how frequent a term is in a document, and how rare a term is in many documents.

In mining the data (Step 4), we applied a support vector machine (SVM) to classify the respondents. The dataset was randomly split into two groups: a training set and a testing set. The training set consisted of 138 instances (90%) and the testing set of 14 instances (10%). We constructed a model based on the training set and made predictions on the testing set to evaluate the prediction performance. In the evaluation of the model (Step 5), the rate of correct predictions over all instances was measured to represent the accuracy of the prediction model. Through removing the 1074 stop words and substituting the 39 words having similar meanings, the results revealed that the accuracy of the prediction model could be up to 85.7%. We used a free data analysis software, RapidMiner, to perform the analysis (See Fig.  2 ). Therefore, in the final step the instructor could predict students’ motivation to learn during the whole semester using computer-mediated communication, such as instant messaging (Step 6).

The analysis process in RapidMiner

We further iterated the process by redefining the research problem as “finding groups of respondents using similar terms to describe an opinion.” In mining the data, the K-Means clustering method was used to partition the respondents into two clusters. The cluster model revealed that Cluster 1 had 89 respondents, and Cluster 2 had 63. ANOVA was performed to determine how the score of MSLQ was influenced by participant’s clusters (see Table  3 ). Significant effects across different work methods were found for the two clusters, F (1, 150) = 14.33, p  = .000. Table  3 indicates that the Cluster 2 had a higher mean score of MSLQ than Cluster 1. The cluster model also found that the top three important terms for were “考試(exam)”, “報告(presentation)”, and “作業(homework)” for Cluster 1 and “老師(instructor)”, “同學(peer)”, and “自己(oneself)” for Cluster 2. In other words, the terms used in Cluster 1 concerned more about the value component of MSLQ. However, the terms used in Cluster 2 concerned more about the expectancy component of MSLQ. Therefore, the instructor could use these terms to roughly provide interventions to improve students’ motivation for learning.

The broad availability of data has led to the development of data science. This paper’s research goals are to stimulate further research and practice in the use of data science for education. It also presents a DS research methodology that is applicable to achieve these goals. A well-defined DS research model can help ensure that quality of results, contribute to better understanding the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Through an examination of large data sets, a DS methodology can help us to acquire more knowledge about how people learn (Koedinger et al. 2015 ). This is important, as it contributes to the development of better intervention support for more effective learning.

This paper also describes the emerging field of social-emotional learning and its challenges. It has been proposed that the social-emotional competencies that occur between people will become very important to education in the future. Although research suggests that social-emotional qualities have a positive influence on academic achievement, most related studies examine these qualities in relation to outcome measurement and prediction, and more work is needed to develop interventions based on this research (Levin 2013 ). Therefore, this paper presents a case study of social-emotional learning in which we used the data science research methodology.

Several large problems remain to be addressed by researchers in this field. Before incorporating the approaches recommended in this work in large-scale education settings, we should select a few social-emotional skill areas and measures. This investment in data acquisition and knowledge discovery by DS will enable a deeper understanding of school effects and school policy in this context, and would avoid pulling reform efforts in unproductive or detrimental directions (Whitehurst 2016 ). Moreover, explicit privacy regulations, such as anonymity in data collection and consent from the parents in a K-12 setting, also need to be addressed. Slade and Prinsloo ( 2013 ) recommend collaborating with students on voluntarily providing data and allowing them to access DS outcomes to aid in their learning and development. We hope the issues we have highlighted in this paper help stimulate further research and practice in education.

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This research is partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. under Grant no. MOST 105-2511-S-006 -015 -MY2.

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Liu, MC., Huang, YM. The use of data science for education: The case of social-emotional learning. Smart Learn. Environ. 4 , 1 (2017). https://doi.org/10.1186/s40561-016-0040-4

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MIT Professional Education Applied Data Science Program: Leveraging AI for Effective Decision-Making curriculum is designed by MIT faculty to equip you with the necessary skills, knowledge, and confidence to excel in the industry. It covers the technologies, including Machine Learning, Deep Learning, Recommendation Systems, ChatGPT, applied data science with Python, Generative AI, and others. The curriculum ensures that you are well-prepared to contribute to data science efforts in any organization.

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• Python Foundations - Libraries: Pandas, NumPy, Arrays and Matrix handling, Visualization, Exploratory Data Analysis (EDA) Pandas is a commonly used library in Python, which is used to analyse and manipulate data. NumPy is a package in the Python library, where you can use this package for scientific computing to work with arrays. An array is a data structure that stores various elements or items at contiguous memory locations. A matrix is a two dimensional (2D) array where data (elements/items) is stored in the format of rows and columns. Visualization is the process to represent data and information in a graphical form. Exploratory Data Analysis (EDA) enables you to uncover patterns and insights frequently with visual methods within some data.
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• Networks: Examples (data as network versus network to represent dependence among variables), determine important nodes and edges in a network, clustering in a network In this chapter, you will learn about networks and various examples of a network, like data as a network versus network to represent dependence among variables, determine important nodes and edges in a network, and clustering in a network.

In this week, you will explore the fundamentals of Supervised Machine Learning and Prediction, including some key algorithms and widely-used techniques.

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• Introduction to Supervised Learning - Regression Supervised learning is a technique that helps you analyze and cluster labelled data sets. Regression is a statistical technique in machine learning that manages the relationship between dependent and independent variables with the help of one or more independent variables.
• Introduction to Supervised Learning - Classification​​​​​​​ Classification, as the name implies, is a procedure to classify/categorize a data set into various categories. This can be performed on both structured and unstructured data.
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The next module in the program for applied Data Science teaches you about decision trees, random forests, and time series analysis.

• Decision Trees A Decision Tree is a popular supervised machine learning algorithm, which is used for both classification and regression problems. It is a hierarchical structure in which the internal nodes denote the dataset features, branches indicate the decision rules, and each leaf node represents the result.
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The next module in this applied Data Science course is Deep Learning. Deep Learning is an application of Machine Learning and Artificial Intelligence.

• Intro to Neural Networks Neural networks are inspired by the human brain, which is used to extract deep/high-level information from the raw input, like images, objects, etc. This chapter introduces you to artificial neural networks in deep learning.
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Learn about the different types of recommendation engines, how they are produced, and their specific applications to business use-cases.

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Earn a professional certificate in Applied Data Science from the Massachusetts Institute of Technology (MIT) Professional Education. This program’s comprehensive and exhaustive curriculum nurtures you into a highly skilled professional in Applied Data Science, which later helps you land a job at the leading organizations worldwide.

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Yes, the program has been designed keeping in mind the needs of working professionals. Thus, you can learn the practical applications of data science from the convenience of your home and within an efficient 12-week duration.

The learners are required to bring their own laptops; however, the necessary technology requirements shall be shared during the enrollment process.

The program has a broad scope, is challenging, and uses a continuous evaluation system. In order to evaluate a learner’s progress throughout the program, quizzes, case studies, assignments, and project reports are used.

The duration of this program is 12 weeks, which includes recorded lectures from award-winning MIT faculty. Each learner mandatorily needs to submit 3 projects that include a project for the first course - Foundations for Data Science, 1 project of their choice out of the 5 projects associated with core courses taught by MIT Faculty, and a 3-week Applied Data Science capstone project.

No, Applied Data Science Program is an online professional certificate program offered by MIT Professional Education in collaboration with Great Learning. Since it is not a degree/full-time program offered by the university, therefore, there are no grade sheets or transcripts for this program. You will receive marks on each assessment to test your understanding and marks on each module to determine your eligibility for the certificate.

Upon successful completion of the program, i.e., after completing all the modules as per the eligibility of the certificate, you are issued a certificate from MIT Professional Education.

Upon successfully completing this program, learners will secure a professional certificate in Applied Data Science from MIT Professional Education.

These live sessions will be recorded and posted on the LMS (Learning Management System) so that learners who couldn’t make it to a session or wish to attend it later can do so by watching the uploaded recordings.

This program is taught by renowned MIT faculty who possess several years of experience and come highly recommended. Along with the teaching staff, the course also has highly qualified industry mentors who will direct you through live, personalized mentoring sessions as you work on hands-on projects.

During this program, learners will gain proficiency in the most in-demand programming languages and tools, including Python, NumPy, Keras, TensorFlow, Matplotlib, and Scikit-Learn, among others.

This course syllabus is designed by considering the following aspects:

Renowned MIT faculty carefully crafted the curriculum to provide learners with industry-relevant tools and techniques and apply them to real-world problems.

The curriculum of this course covers essential Data Science techniques to deal with complex problems and prepare data-driven decision-makers for the future.

Learners will explore critical concepts of Data Analysis and Data Visualization, Machine Learning, Deep Learning, and Neural Networks.

The theory behind recommendation systems and their application to various sectors are also covered in the course material.

The MIT Applied Data Science Program lasts 12 weeks and is structured as follows:  

• 2 Weeks: Foundational courses on data science with Python and Statistical Science
• 6 Weeks: A core curriculum that includes hands-on applications and problem-solving, involving 58 hours of live virtual sessions by MIT faculty and industry experts
• 1 Week: Project submissions
• 3 Weeks : Final, integrative MIT Professional Education Applied Data Science capstone project

Note: The live virtual classes with MIT professors will occur on Mondays, Wednesdays, and Fridays at 9:30 AM EST .

This course is an excellent choice for those seeking knowledge and skills in Applied Data Science. The benefits of choosing this course from MIT Professional Education are as follows: 

• Learn from distinguished MIT faculty through live online classes in the comfort of your home.
• Boost your career transition with 1-on-1 career counseling, a review of your resume and LinkedIn profile, and an online portfolio that includes six hands-on projects and a 3-week capstone project.
• Earn a Certificate of Completion from MIT Professional Education.
• Take advantage of live mentorship from industry professionals on the application of faculty members' concepts.
• Earn 3.0 Continuing Education Units (CEUs) on successful program completion.

MIT is ranked #1 university globally by QS World University Rankings 2023 and #2 in the best global universities in the U.S. News & World Report 2022-2023.

The MIT Professional Education Applied Data Science Program is an all-encompassing course tailored to meet the learning needs of professionals seeking to advance their careers, tackle complex problems with innovative solutions, and contribute to a better future.

The program combines state-of-the-art online technology with traditional classroom instruction, fostering participation and teamwork and improving learning outcomes. Over 12 weeks, participants can enhance their data analytics skills by profoundly understanding the theories and practical applications of cutting-edge techniques, including supervised and unsupervised learning, regression, time-series analysis, neural networks, recommendation engines, and computer vision.

For 5 weeks of MIT Faculty live lectures, each week involves:

6 hours of live virtual sessions by MIT Faculty (Monday, Wednesday, and Friday)

4 hours of mentored learning sessions (2 sessions every weekend)

5 to 8 hours of self-study and practice (based on your background)

This amounts to an average time commitment of 15-18 hours per week.

For the remaining 7 weeks, an average time commitment of 12-16 hours per week is expected from the learners, which includes foundation/conceptual sessions, mentor learning sessions, capstone project work, self-study, and practice.

The live virtual sessions with MIT faculty will be held on Mondays, Wednesdays, and Fridays at 9:30 AM EST. The mentorship sessions with industry experts will be held in small groups of learners on weekends. The exact timings will be determined based on the time zones of the learners in a particular mentorship group.

MIT Professional Education is a distinguished platform that provides specialized and advanced applied data science programs, offering access to MIT's world-renowned research, knowledge, and expertise to working professionals in the fields of science and technology. As a critical component of MIT's vision, MIT Professional Education fulfills the mission of connecting practitioner-oriented education with industry and integrating industry feedback and knowledge into MIT's education and research.

You should possess a working knowledge of computer programming and statistics.

The prerequisites of the program include working knowledge of programming and statistics. Suppose you do not possess either (or both) of them. In that case, you will have to put in extra effort to learn them before the program's commencement in order to cope with the curriculum designed by MIT Professional Education.

We, from Great Learning, will provide you with content that can be useful in understanding the fundamentals of programming (Python) and statistics. However, you would be required to put in extra effort and hours to complete the programming assignments.

The applications go through a rolling process that closes when the required number of seats in the cohort is filled. Please submit your application as soon as possible to boost your chances of getting a seat.

Candidates must fulfill the eligibility requirements listed above to enroll in this course. The following is the typical application procedure for those candidates who qualify:

• Step-1: Application Form

Candidates must fill out their online application form.  

• Step-2: Application Screening

Upon receiving the application, the program team will review it to determine your fit with the program.  

• Step-3: Program Enrollment

If chosen, candidates will be given an offer for the upcoming cohort. By paying the fee, they can reserve their seats.

Upon the successful completion of this program, learners become a part of MIT Professional Education's alumni community group and can access alumni benefits, that include a 15% discount towards any short programs offered by MIT Professional Education.

No, Data Science and Applied Data Science are different.

Data Science is a broad field that involves techniques and processes for gathering and analyzing data to generate insights, predictions, and strategies. It includes topics such as machine learning, artificial intelligence, and statistics.

Applied Data Science is the practice of using Data Science principles in different areas, such as e-commerce, healthcare, finance, and marketing. It focuses on utilizing data-driven approaches to design, develops, and deploy solutions to complex business problems. It focuses on the practical application of Data Science principles to derive insights and add value to different sectors of the economy.

The demand for Applied Data Scientists has seen massive growth over the past few years and is most likely to increase the graph in the upcoming years. Glassdoor’s research says that the Data Scientist role is the #3 job in the United States in 2022. According to a study by the U.S. Bureau of Labor Statistics , the demand for Data Scientists is expected to rise 36% by 2031, which is much quicker than the average for all the other occupations. Data Scientists are one of the fastest-growing jobs in the world.

Yes, Applied Data Science is absolutely worth it! Applied Data Science involves the application of Data Science principles and practices to solve real-world problems. With Applied Data Science, you can use data to inform business decision-making, optimize complex systems, and make products and services more effective. 

Applied Data Science is an essential skill that can help you stand out in the job market and give you the knowledge and skills to help your organization stay ahead of the competition. It can open the door to more job opportunities, more efficient systems, and better decision-making.

Numerous trending applications in the industry use Data Science. Some of the essential Data Science applications include:

• Healthcare Services: Data Science can be used in Medical Image Analysis like tumor detection, etc., using a Machine Learning Method, Support Vector Machine (SVM).  
• Banking and Finance Sectors: Data Science can be used for fraud detection, risk modeling, customer data management, real-time predictive analytics, etc.  
• Transport: Data Science is used in several cars, like optimizing vehicle performance, fuel consumption patterns, etc. It can also be used in self-driving cars for vehicle monitoring. For example, Uber uses Data Science and Machine Learning to predict the weather, availability of customers and transportation, etc.  
• Manufacturing Industries: Data Science plays a vital role in the manufacturing industries, such as optimizing production, reducing costs, increasing profits, etc.  
• E-commerce: Data Science can be used to identify customer base, predictive analytics for estimating goods and services, identify the latest trends of each product, optimize pricing of the products for customers, and many more.  
• Image and Facial Recognition: Using Data Science and Machine Learning, you can identify a person in an image using a facial recognition algorithm. For example, when you upload a photo with your friends on Facebook, you get suggestions for tagging your friends in your picture. This automatic tag suggestion is an example of Image and Facial Recognition.  
• Airline Sectors: With the help of Data Science, airline sectors can now predict flight delays, they can choose which class of airplanes they can buy to suit their specific needs, plan airline routes whether to take a halt in any place or put out a direct flight and many more.  
• Gaming Sectors: In games, computers (opponents) collect data from your previous games and improve themselves in the upcoming games. For example, Chess.

There are several other industries that use Data Science for their applications.

Applied Data Science is a high/deep technical knowledge of how Data Science and its methodologies work. Applied Data Science involves modelling complicated problems, discovering insights, building highly advanced and high-risk algorithms, identifying opportunities through statistical and machine learning models, and visualization techniques for improving operational efficiency.

You can become an Applied Data Scientist by:

Earning a bachelor’s degree in computer science, IT, mathematics/statistics, or any other Data Science related fields

Gaining professional experience in Data Science by working at any organization

Enrolling in an Applied Data Science Program from top universities, such as MIT, UC Berkeley, etc.

According to the research by Glassdoor , the average salary earned by an Applied Data Scientist in the United States is $125,784 per annum. The pay scale ranges from $83K per annum to $194K per annum.

We welcome corporate sponsorships and can help you through the process. 

[For more information, please write to us at [email protected] or +1 617 468 7899]

No. Through the Learning Management System (LMS), learners can access all the necessary learning materials online. There will be a list of recommended books and other resources for your in-depth reading pleasure because these fields are broad and constantly changing, so there is always more you can learn.

This professional course costs USD 3900, which candidates can pay through Credit/Debit Cards and Bank transfers. For further details, please get in touch with the Great Learning team.

Candidates can pay the course fee through Bank Transfer and Credit/Debit Cards. They can also avail PayPal payment options.

For further details, please get in touch with us at [email protected] .

Please note that submitting the registration fee does constitute enrolling in the program, and the below cancellation penalties will be applied. If you are unable to attend your program, please review our dropout and refund policies below:

Dropout requests received within 7 days of enrollment and more than 42 days prior to the commencement of the program will incur no fee. Any payment received will be refunded in full.

Dropout requests received more than 42 days prior to the program but more than 7 days after the acceptance are subject to a cancellation fee of USD 250.

Dropout requests received 22-41 days prior to the commencement of the program are subject to a cancellation fee equal to 50% of the program fee.

Any dropout requests received fewer than 22 days prior to the commencement of the program are subject to a cancellation fee equal to 100% of the program fee.

No refund will be made to those who do not engage in the program or leave before completing a program for which they have been registered.

Still have queries? Contact Us

Please fill in the form and a Program Advisor from Great Learning will reach out to you. You can also reach out to us at [email protected] or +1 617 468 7899

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Check out the program and fee details in our brochure

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