educational data mining a literature review

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Information Discovery and Delivery

ISSN : 2398-6247

Article publication date: 19 May 2020

Issue publication date: 10 October 2020

Educational data mining (EDM) and learning analytics, which are highly related subjects but have different definitions and focuses, have enabled instructors to obtain a holistic view of student progress and trigger corresponding decision-making. Furthermore, the automation part of EDM is closer to the concept of artificial intelligence. Due to the wide applications of artificial intelligence in assorted fields, the authors are curious about the state-of-art of related applications in Education.

Design/methodology/approach

This study focused on systematically reviewing 1,219 EDM studies that were searched from five digital databases based on a strict search procedure. Although 33 reviews were attempted to synthesize research literature, several research gaps were identified. A comprehensive and systematic review report is needed to show us: what research trends can be revealed and what major research topics and open issues are existed in EDM research.

Results show that the EDM research has moved toward the early majority stage; EDM publications are mainly contributed by “actual analysis” category; machine learning or even deep learning algorithms have been widely adopted, but collecting actual larger data sets for EDM research is rare, especially in K-12. Four major research topics, including prediction of performance, decision support for teachers and learners, detection of behaviors and learner modeling and comparison or optimization of algorithms, have been identified. Some open issues and future research directions in EDM field are also put forward.

Research limitations/implications

Limitations for this search method include the likelihood of missing EDM research that was not captured through these portals.

Originality/value

This systematic review has not only reported the research trends of EDM but also discussed open issues to direct future research. Finally, it is concluded that the state-of-art of EDM research is far from the ideal of artificial intelligence and the automatic support part for teaching and learning in EDM may need improvement in the future work.

• Educational data mining
• Learning analytics
• Systematic review
• Prediction of performance
• Decision support
• Artificial intelligence

Acknowledgements

Conflict of interest: The authors have declared no conflicts of interest for this article.

This study was supported by National Natural Science Foundation of China Under Grant No. 61877027.

Du, X. , Yang, J. , Hung, J.-L. and Shelton, B. (2020), "Educational data mining: a systematic review of research and emerging trends", Information Discovery and Delivery , Vol. 48 No. 4, pp. 225-236. https://doi.org/10.1108/IDD-09-2019-0070

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A Systematic Review on Educational Data Mining

Research output : Contribution to journal › Review Article › Research › peer-review

Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

• clustering methods
• Data mining
• educational technology
• systematic review

Access to Document

• 10.1109/ACCESS.2017.2654247

Other files and links

• Link to publication in Scopus

T1 - A Systematic Review on Educational Data Mining

AU - Dutt, Ashish

AU - Ismail, Maizatul Akmar

AU - Herawan, Tutut

N1 - Funding Information: This work was supported by the University of Malaya research under Grant RP028B-14AET. Publisher Copyright: © 2013 IEEE.

N2 - Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

AB - Presently, educational institutions compile and store huge volumes of data, such as student enrolment and attendance records, as well as their examination results. Mining such data yields stimulating information that serves its handlers well. Rapid growth in educational data points to the fact that distilling massive amounts of data requires a more sophisticated set of algorithms. This issue led to the emergence of the field of educational data mining (EDM). Traditional data mining algorithms cannot be directly applied to educational problems, as they may have a specific objective and function. This implies that a preprocessing algorithm has to be enforced first and only then some specific data mining methods can be applied to the problems. One such preprocessing algorithm in EDM is clustering. Many studies on EDM have focused on the application of various data mining algorithms to educational attributes. Therefore, this paper provides over three decades long (1983-2016) systematic literature review on clustering algorithm and its applicability and usability in the context of EDM. Future insights are outlined based on the literature reviewed, and avenues for further research are identified.

KW - clustering methods

KW - Data mining

KW - educational technology

KW - systematic review

UR - http://www.scopus.com/inward/record.url?scp=85028347409&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2017.2654247

DO - 10.1109/ACCESS.2017.2654247

M3 - Review Article

AN - SCOPUS:85028347409

SN - 2169-3536

JO - IEEE Access

JF - IEEE Access

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Educational data mining to predict students' academic performance: A survey study

• Published: 09 July 2022
• Volume 28 , pages 905–971, ( 2023 )

Cite this article

• Saba Batool 1 ,
• Junaid Rashid   ORCID: orcid.org/0000-0002-1485-0757 2 ,
• Muhammad Wasif Nisar 1 ,
• Jungeun Kim 3 ,
• Hyuk-Yoon Kwon 4 &
• Amir Hussain 5  

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Educational data mining is an emerging interdisciplinary research area involving both education and informatics. It has become an imperative research area due to many advantages that educational institutions can achieve. Along these lines, various data mining techniques have been used to improve learning outcomes by exploring large-scale data that come from educational settings. One of the main problems is predicting the future achievements of students before taking final exams, so we can proactively help students achieve better performance and prevent dropouts. Therefore, many efforts have been made to solve the problem of student performance prediction in the context of educational data mining. In this paper, we provide readers with a comprehensive understanding of student performance prediction and compare approximately 260 studies in the last 20 years with respect to i) major factors highly affecting student performance prediction, ii) kinds of data mining techniques including prediction and feature selection algorithms, and iii) frequently used data mining tools. The findings of the comprehensive analysis show that ANN and Random Forest are mostly used data mining algorithms, while WEKA is found as a trending tool for students’ performance prediction. Students’ academic records and demographic factors are the best attributes to predict performance. The study proves that irrelevant features in the dataset reduce the prediction results and increase model processing time. Therefore, almost half of the studies used feature selection techniques before building prediction models. This study attempts to provide useful and valuable information to researchers interested in advancing educational data mining. The study directs future researchers to achieve highly accurate prediction results in different scenarios using different available inputs or techniques. The study also helps institutions apply data mining techniques to predict and improve student outcomes by providing additional assistance on time.

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Students Performance Prediction Using Educational Data Mining

An Overview of Data Mining Techniques for Student Performance Prediction

Predicting Students Performance Using Educational Data Mining and Learning Analytics: A Systematic Literature Review

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Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

Saba Batool & Muhammad Wasif Nisar

Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, South Korea

Junaid Rashid

Department of Software, Department of Computer Science and Engineering, Kongju National University, Cheonan, 31080, South Korea

Jungeun Kim

Department of Industrial Engineering, Seoul National University of Science and Technology, Seoul, South Korea

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Data Science and Cyber Analytics Research Group, Edinburgh Napier University, Edinburgh, EH11 4DY, UK

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Batool, S., Rashid, J., Nisar, M.W. et al. Educational data mining to predict students' academic performance: A survey study. Educ Inf Technol 28 , 905–971 (2023). https://doi.org/10.1007/s10639-022-11152-y

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Received : 27 November 2021

Accepted : 02 June 2022

Published : 09 July 2022

Issue Date : January 2023

DOI : https://doi.org/10.1007/s10639-022-11152-y

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