Predicting and analyzing the performance of students through data mining techniques to improve academic performance

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GHODOOSI M.; Mirsaeedi F.; Koosha H.R.

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Journal Paper

Paper Information

Journal: Technology of Education Journal Year:2020 | Volume:14 | Issue:4 (56) Page(s): 821-834

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Title

Predicting and analyzing the performance of students through data mining techniques to improve academic performance

Author(s)

GHODOOSI M. | Mirsaeedi F. | Koosha H.R. | Issue Writer Certificate

Keywords

Educational Data MiningQ1

Unit SelectionQ1

Academic PerformanceQ1

Logit BoostQ1

Abstract

Background and Objectives: Nowadays, significant advancements in information technology and communication field in different societies are seen. Given that universities as a leading institution in the field of science have moved towards electronic processes in the management of education and educational environments, there are databases with a large amount of information. By analyzing this massive data of educational systems, methods can be provided to improve the educational status of students. Educational Data Mining has sought to discover the knowledge contained in the data of the educational system. One of the applications of Educational Data Mining is to predict students' Academic Performance. Predicting students' Academic Performance and providing useful solutions is of particular importance in the success of educational systems and can help managers make right decisions to increase the efficiency of the educational system and better student performance. The purpose of this paper is to identify effective indicators on Academic Performance, predict students' academic status using data mining techniques, and finally present a new trend for modifying Unit Selection and educational strategies to increase the efficiency of the education system. Methods: Steps of this research are determined according to CRISP model. In current research, databases containing 9 datasets of specialized courses in industrial engineering were used. The students had bachelor degrees. Indicators affecting students’ performance have been identified based on previous research and expert opinions. Demographic data and academic records of undergraduate students were entered in database. After data preprocessing, 13 attributes were selected and different models were proposed to predict student's academic status in the next semester. Then, a comparison between the results of 4 different algorithms has been done. Findings: All 13 attributes were identified to be effective according to information gain and gain ratio. These 13 attributes are as follow: GPA, total passed units, number of conditional terms, type of admission, marital status, gender, university admission year, living place, age, current semester, prerequisite course score, instructor of the course, repetition of the course. Among the 4 considered models, the Logit Boost algorithm is known as the best model for categorizing two class and multi-class according to the accuracy rate and ROC. Conclusion: Because of acceptable performance of data mining algorithms, the use of these algorithms in predicting student performance is appropriate and the proposed model can be used as a support tool for decision making in educational systems. According to the obtained results and the opinion of academic experts, the Unit Selection process was redesigned. The proposed model can be used as a decision support tool in educational systems. The presented process uses the available data in educational systems and data mining science, provides useful knowledge to decision-makers to make the right and appropriate decision. Decision makers can make appropriate decisions by examining the predictions made by the data mining algorithm and obtaining useful information, in order to make the educational system more efficient.

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APA: Copy

GHODOOSI, M., Mirsaeedi, F., & Koosha, H.R.. (2020). Predicting and analyzing the performance of students through data mining techniques to improve academic performance. JOURNAL OF TECHNOLOGY OF EDUCATION (JOURNAL OF TECHNOLOGY AND EDUCATION), 14(4 (56) ), 821-834. SID. https://sid.ir/paper/402372/en

Vancouver: Copy

GHODOOSI M., Mirsaeedi F., Koosha H.R.. Predicting and analyzing the performance of students through data mining techniques to improve academic performance. JOURNAL OF TECHNOLOGY OF EDUCATION (JOURNAL OF TECHNOLOGY AND EDUCATION)[Internet]. 2020;14(4 (56) ):821-834. Available from: https://sid.ir/paper/402372/en

IEEE: Copy

M. GHODOOSI, F. Mirsaeedi, and H.R. Koosha, “Predicting and analyzing the performance of students through data mining techniques to improve academic performance,” JOURNAL OF TECHNOLOGY OF EDUCATION (JOURNAL OF TECHNOLOGY AND EDUCATION), vol. 14, no. 4 (56) , pp. 821–834, 2020, [Online]. Available: https://sid.ir/paper/402372/en

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