Educational Data Mining Applications and Techniques

Educational data mining (EDM) uses data mining techniques to analyze huge amounts of student data in the educa-tional environments. The main purpose of EDM is to analyze and solve educational issues and, consequently, improve educational processes. With the emergence of EDM applications in the educational environments, several techniques have been identified to implement these applications. This paper reviews the relevant studies in EDM including datasets and techniques used in those studies and identifies the most effective techniques. The most prevalent applications include predicting student performance, detecting undesirable student behaviors, grouping students and student modeling. These applications aim to help decision makers in the educational institutions to understand student situations, improve students’ performance, identify learning priorities for different groups of students and develop learning process. The prediction accuracy is selected as the evaluation criteria for the effectiveness of educational data mining techniques. The results show that Bayesian Network and Random Forest are the most effective techniques for predicting student performance, Social Network Analysis is the best technique for detecting undesirable student behaviors, Clustering and Social Network Analysis are the most effective techniques for grouping students and student modeling, respectively. This study recommends conducting more comprehensive and extended studies to evaluate the effectiveness of EDM techniques with an extended evaluation criteria.

[1]  Mohammed Saeed,et al.  Applications of Educational Data Mining and Learning Analytics Tools in Handling Big Data in Higher Education , 2018, Applications of Big Data Analytics.

[2]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Marta E. Zorrilla,et al.  Data Mining and Social Network Analysis in the Educational Field: An Application for Non-Expert Users , 2014 .

[4]  Fadhilah Ahmad,et al.  The Prediction of Students' Academic Performance Using Classification Data Mining Techniques , 2015 .

[5]  Ashraf Y. A. Maghari,et al.  Students performance prediction using KNN and Naïve Bayesian , 2017, 2017 8th International Conference on Information Technology (ICIT).

[6]  Subitha Sivakumar,et al.  Predictive Modeling of Student Dropout Indicators in Educational Data Mining using Improved Decision Tree , 2016 .

[7]  Sebastián Ventura,et al.  Data mining in education , 2013, WIREs Data Mining Knowl. Discov..

[8]  R Sathyaraj,et al.  Comparative Study on Marks Prediction using Data Mining and Classification Algorithms , 2017 .

[9]  Hilal Almarabeh,et al.  Analysis of Students' Performance by Using Different Data Mining Classifiers , 2017 .

[10]  Lumbini P. Khobragade,et al.  Students’ Academic Failure Prediction Using Data Mining , 2015 .

[11]  A. S. M. Badrudduza,et al.  Educational Performance Analytics of Undergraduate Business Students , 2019, International Journal of Modern Education and Computer Science.

[12]  Alejandro Peña Ayala,et al.  Educational data mining: A survey and a data mining-based analysis of recent works , 2014, Expert Syst. Appl..

[13]  Mohammed Saqr,et al.  The role of social network analysis as a learning analytics tool in online problem based learning , 2019, BMC Medical Education.

[14]  SYADIAH NOR WAN SHAMSUDDIN,et al.  ANALYSIS ON STUDENTS PERFORMANCE USING NAÏVE , 2017 .

[15]  B. Umamaheswari,et al.  A Survey on Educational Data Mining in Field of Education , 2016 .

[16]  Harwati,et al.  A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques , 2017 .

[17]  Slavomir Stankov,et al.  Educational data mining for grouping students in e-learning system , 2012, Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces.

[18]  Mykola Pechenizkiy,et al.  Predicting Students Drop Out: A Case Study , 2009, EDM.

[19]  Etinosa Noma-Osaghae,et al.  Data mining approach to predicting the performance of first year student in a university using the admission requirements , 2018, Education and Information Technologies.

[20]  Vivek Narayanan,et al.  Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model , 2013, IDEAL.

[21]  Tahani Alsubait,et al.  Data Mining for Student Advising , 2020 .

[22]  Andrés Villanueva Manjarres,et al.  Data mining techniques applied in educational environments: Literature Review , 2018 .

[23]  Carlos R. Minussi,et al.  Prediction of school dropout risk group using Neural Network , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[24]  Tutut Herawan,et al.  A Systematic Review on Educational Data Mining , 2017, IEEE Access.

[25]  Y. K. Salal,et al.  Educational Data Mining : Student Performance Prediction in Academic , 2019 .

[26]  Manjusha Pandey,et al.  Analyzing Student Performance in Engineering Placement Using Data Mining , 2019 .

[27]  N. Ananthi,et al.  Performance Analysis of Undergraduate Students Placement Selection using Decision Tree Algorithms , 2014 .

[28]  Shane Dawson,et al.  Mining LMS data to develop an "early warning system" for educators: A proof of concept , 2010, Comput. Educ..

[29]  Fabio A. González,et al.  A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining , 2015, IEEE Revista Iberoamericana de Tecnologias del Aprendizaje.

[30]  Ganesan Kavitha,et al.  Educational Data Mining and Learning Analytics - Educational Assistance for Teaching and Learning , 2017, ArXiv.

[31]  Lubos Popelínský,et al.  Predicting drop-out from social behaviour of students , 2012, EDM.

[32]  Hafez Mousa,et al.  School Students' Performance Predication Using Data Mining Classification , 2017 .

[33]  Sadiq Hussain,et al.  Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA , 2018 .

[34]  Aqeel Majeed Humadi,et al.  Students’ Success Prediction based on Bayes Algorithms , 2017 .

[35]  Ritika Saxena.  Educational Data Mining: Performance Evaluation of Decision Tree and Clustering Techniques Using WEKA Platform , 2015 .

[36]  Jason M. Harley,et al.  Clustering and Profiling Students According to their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning , 2013, EDM 2013.

[37]  Odunayo Salau,et al.  The impact of engineering students' performance in the first three years on their graduation result using educational data mining , 2019, Heliyon.

[38]  Umar Manzoor,et al.  Modeling and Predicting Students' Academic Performance Using Data Mining Techniques , 2016 .

[39]  P. G. Sunitha Hiremath,et al.  Student academic performance and social behavior predictor using data mining techniques , 2017, 2017 International Conference on Computing, Communication and Automation (ICCCA).

[40]  Manpreet Singh,et al.  Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector , 2015 .

[41]  Shane Dawson,et al.  Measuring creative potential: Using social network analysis to monitor a learners' creative capacity , 2011 .

[42]  Qasim Ali Arain,et al.  Analyzing Students’ Academic Performance through Educational Data Mining , 2019 .

[43]  Osmar R. Zaïane,et al.  Educational data mining applications and tasks: A survey of the last 10 years , 2017, Education and Information Technologies.

[44]  P. Kavipriya,et al.  On Improving Student Performance Prediction in Education Systems using Enhanced Data Mining Techniques , 2017 .

[45]  K. Sathesh Kumar,et al.  Review on Prediction Algorithms in Educational Data Mining , 2018 .

[46]  Jarutas Pattanaphanchai,et al.  The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity , 2019, JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences.