Systematic Research on Predictive Models on Students Academic Performance in Higher Education

Zulkifley Mohamed

QR Code Link :
Type :	article
Subject :	L Education (General)
Main Author :	Zulkifley Mohamed
Additional Authors :	Nor Afzalina Azmee Faiz Zulkifli
Title :	Systematic Research on Predictive Models on Students Academic Performance in Higher Education

Place of Production :	Tanjong Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2019
Corporate Name :	Universiti Pendidikan Sultan Idris

Abstract : Universiti Pendidikan Sultan Idris

In Malaysia, the students’ academic assessment has changed over time as it is now made to be more holistic. Predictive models used for educational data in Malaysia are still inadequate in giving a true picture of the students’ academic performance. The reason behind this is due to the lack of research in Malaysian context on predictive models and factors affecting students' academic achievement. Therefore, this study will conduct a systematic review on the literature, in order to identify the predictive methods for students’ academic performance in higher education. Through three major bases: WoS, Science Direct and IEEE Xplore, an extensive search was conducted to find any related articles from 2014 to 2018 that use "predict", "forecast", "academic performance", "student" and "higher education" keywords in their text. Initially, 195 articles were selected to be used in this review. However, through titles and abstracts’ filtering and screen processing, only 69 articles found to discuss on the predictive model for student's academic performance at higher education level. Based on extensive reading, the most widely used attributes in the predictive models is the academic process. While predictive models can be categorized into three, namely classification, cluster and regression, there are nine methods used by previous researchers in all categories. The most widely used category from previous studies was the classification, with 33 articles. This study has listed the advantages and disadvantages of each method based on the previous studies. This study also has identified the challenges and gaps faced by previous researchers in improving the existing models in the future. Among the challenges faced by the predictive models are the amount of data and assumptions that need to be followed before analysis can be made. In the future, the predictive models used for students’ academic performance should consider the latest assessments’ valuation method based on a modern educational system that emphasizes on soft skills, interpersonal skills and high-level thinking capabilities

References

1. S. Taruna, and M. Pandey, “An empirical analysis of classification techniques for predicting academic performance,” IEEE Int. Adv. Comput. Conf., 2014, pp. 523–528.

2. A. Sharabiani, F. Karim, A. Sharabiani, M. Atanasov, and H. Darabi, “An enhanced Bayesian network model for prediction of students’ academic performance in engineering programs,” IEEE Glob. Eng. Educ. Conf., 2014, pp. 832–837.

3. M. Mayilvaganan, and D. Kalpanadevi, “Comparison of classification techniques for predicting the cognitive skill of students in education environment,” IEEE Int. Conf. Comput. Intell. Comput. Res., 2014, pp. 1–4.

4. J. Xu, K. H. Moon, and M. van der Schaar, “A machine learning approach for tracking and predicting student performance in degree programs,” IEEE J. Sel. Top. Signal Process., 11(5), 2017, pp. 742–753.

5. J. Gamulin, O. Gamulin, and D. Kermek, “Comparing classification models in the final exam performance prediction,” IEEE 37th International Convention on Information and Communication Technology, Electronics and Microelectronics, 2014, pp. 663–668.

6. F. Marbouti, H. A. Diefes-Dux, and K. Madhavan, “Models for early prediction of at-risk students in a course using standards-based grading,” Comput. Educ., 103, 2016, pp. 1–15.

7. J. L. Arco-Tirado, F. Fernández-Martín, A. M. RamosGarcía, L. Littvay, and J. Villoria, “A counterfactual impact evaluation of a bilingual program on students’ grade point average at a Spanish university,” Eval. Program Plann., 68, 2018, pp. 81–89.

8. A. Krasilnikov, and A. Smirnova, “Online social adaptation of first-year students and their academic performance,” Comput. Educ., 113, 2017, pp. 327–338.

9. S. E. Sorour, T. Mine, K. Godaz, and S. Hirokawax, “Comments data mining for evaluating student’s performance,” IEEE 3rd Int. Conf. Adv. Appl. Informatics, 2014, pp. 25–30.

10. T. M. Christian, and M. Ayub, “Exploration of classification using NBTree for predicting students’ performance,” IEEE Int. Conf. Data Softw. Eng., 2014, pp. 1–6.

11. S. M. M. Rubiano, and J. A. D. Garcia, “Formulation of a predictive model for academic performance based on students’ academic and demographic data,” IEEE Front. Educ. Conf., 2015, pp. 1–7.

12. S. C. Kumar, E. D. Chowdary, S. Venkatramaphanikumar, and K. V. K. Kishore, “M5P model tree in predicting student performance: A case study,” IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol., 2016, pp. 1103–1107.

13. B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, and W. F. Punch, “Predicting student performance: An application of data mining methods with an educational web-based system,” 33rd ASEE/IEEE Annu. Front. Educ., 2003, pp. 1-6.

14. A. Pardo, F. Han, and R. A. Ellis, “Combining university student self-regulated learning indicators and engagement with online learning events to predict academic performance,” IEEE Trans. Learn. Technol., 10(1), 2017, pp. 82–92.

15. J. Jamesmanoharan, “Discovering students’ academic performance based on GPA using K-Means clustering algorithm,” IEEE World Congress on Computing and Communication Technologies, 2014, pp. 200–202.

16. Y. Olufadi, “A configurational approach to the investigation of the multiple paths to success of students through mobile phone use behaviors,” Comput. Educ., 86, 2015, pp. 84–104.

17. T. Bonsaksen, T. Brown, H. B. Lim, and K. Fong, “Approaches to studying predict academic performance in undergraduate occupational therapy students: A crosscultural study,” BMC Med. Educ., 17(1), 2017, pp. 1–9.

18. F. Williamson, E. Espinosa, M. Quintana, and R. Alarcon, “Do cognitive factors predict student’s academic performance in dental school?” 10th International Technology, Education and Development Conference, pp. 1-7.

19. C. E. Bai, W. Chi, and X. Qian, “Do college entrance examination scores predict undergraduate GPAs? A tale of two universities,” China Econ. Rev., 30, 2014, pp. 632–647.

20. T. Joyce, S. Crockett, D. A. Jaeger, O. Altindag, and S. D. O’Connell, “Does classroom time matter?” Econ. Educ. Rev., 46, 2015, pp. 64–77.

21. A. Slim, G. L. Heileman, J. Kozlick, and C. T. Abdallah, “Employing Markov networks on curriculum graphs to predict student performance,” IEEE 13th Int. Conf. Mach. Learn. Appl., 2014, 2014, pp. 415–418.

22. Z. Li, C. Shang, and Q. Shen, “Fuzzy-clustering embedded regression for predicting student academic performance,” IEEE Int. Conf. Fuzzy Syst., 2016, pp. 344–351.

23. D. Piffer, D. Ponzi, P. Sapienza, L. Zingales, and D. Maestripieri, “Morningness-eveningness and intelligence among high-achieving US students: Night owls have higher GMAT scores than early morning types in a topranked MBA program,” Intelligence, 47, 2014, pp. 107–112.

24. P. M. Arsad, N. Buniyamin, and J. A. Manan, “Neural network and linear regression methods for prediction of students’ academic achievement,” IEEE Glob. Eng. Educ. Conf., 2014, pp. 916–921.

25. T. Kötter, J. Wagner, L. Brüheim, and E. Voltmer, “Perceived medical school stress of undergraduate medical students predicts academic performance: An observational study,” BMC Med. Educ., 17(1), 2017, pp. 1–6.

26. P. Vuttipittayamongkol, “Predicting factors of academic performance,” IEEE 2nd Asian Conf. on Defence Technol., 2016, pp. 161–166.

27. F. K. Kheng, N. Azlan, S. N. D. Ahmad, N. L. H. Leong, and I. Mohamed, “Relationship between cognitive factors and performance in an introductory statistics course: A Malaysian case study introduction,” Malaysian Journal of Mathematical Sciences, 10(3), 2016, pp. 269–282.

28. T. Thiele, D. Pope, A. Singleton, and D. Stanistreet, “Role of students’ context in predicting academic performance at a medical school: A retrospective cohort study,” BMJ Open, 6(3), 2016, pp. 1-11.

29. D. K. Wentworth and J. H. Middleton, “Technology use and academic performance,” Comput. Educ., 78, 2014, pp. 306–311.

30. S. S. Athani, S. A. Kodli, M. N. Banavasi, and P. G. S. Hiremath, “Student performance predictor using multiclass support vector classification algorithm,” IEEE International Conference on Signal Processing and Communication, 2017, pp. 170–174.

31. B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, “Predicting students performance in educational data mining,” IEEE Int. Symp. Educ. Technol., 2015, pp. 125–128.

32. V. Ramesh, P. Parkavi, and K. Ramar, "Predicting student performance: A statistical and data mining approach," International Journal of Computer Applications, 63(8), 2013, pp. 35-39.

33. T. Mishra, D. Kumar, and S. Gupta, “Mining students’ data for prediction performance,” IEEE Int. Conf. Adv. Comput. Commun. Technol., 2014, pp. 255–262.

34. S. Rajeswari, and R. Lawrance, “Classification model to predict the learners’ academic performance using big data,” IEEE Int. Conf. Comput. Technol. Intell. Data Eng., 2016, pp.1–6.

35. S. Rana, and R. Garg, “Application of hierarchical clustering algorithm to evaluate students performance of an institute,” IEEE 2nd Int. Conf. Comput. Intell. Commun. Technol., 2016, pp. 692–697.

36. A. M. Kueider, J. M. Parisi, A. L. Gross, and G. W. Rebok, "Computerized cognitive training with older adults: A systematic review," Plos One, 7(7), 2012, pp. 1-13.

37. K. Macauley, L. Plummer, C. Bemis, G. Brock, C. Larson, and J. Spangler, “Prevalence and predictors of anxiety in healthcare professions students,” Heal. Prof. Educ., 4(3), 2018, pp. 176-185.

38. N. A. Yassein, R. G. M. Helali, and S. B. Mohomad, “Predicting student academic performance in KSA using Gata mining techniques,” J. Inf. Technol. Softw. Eng., 7(5), 2017, pp. 187–191.

39. V. Mhetre, and M. Nagar, “Classification based data mining algorithms to predict slow, average and fast learners in educational system using WEKA,” IEEE Int. Conf. Comput. Methodol. Commun., 2018, pp. 475–479.

40. A. M. Shahiri, and W. Husain, "A review on predicting student's performance using data mining techniques," Procedia Computer Science, 72, 2015, pp. 414-422.

41. P. Guleria, N. Thakur, and M. Sood, “Predicting student performance using decision tree classifiers and information gain,” IEEE Int. Conf. Parallel, Distrib. Grid Comput., 2014, pp. 126–129.

42. F. Zulkifli, R. Z. Abidin, N. F. M. Razi, N. H. Mohammad, R. Ahmad and A. Z. Azmi, “Evaluating quality and reliability of final exam questions for probability and statistics course using Rasch model,” International Journal of Engineering and Technology (UAE), 7(4), 2018, pp. 32-36

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page