Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques
Source abstract
Abstract
Technology and innovation empower higher educational institutions (HEI) to use different types of learning systems—video learning is one such system. Analyzing the footprints left behind from these online interactions is useful for understanding the effectiveness of this kind of learning. Video-based learning with flipped teaching can help improve student’s academic performance. This study was carried out with 772 examples of students registered in e-commerce and e-commerce technologies modules at an HEI. The study aimed to predict student’s overall performance at the end of the semester using video learning analytics and data mining techniques. Data from the student information system, learning management system and mobile applications were analyzed using eight different classification algorithms. Furthermore, data transformation and preprocessing techniques were carried out to reduce the features. Moreover, genetic search and principle component analysis were carried out to further reduce the features. Additionally, the CN2 Rule Inducer and multivariate projection can be used to assist faculty in interpreting the rules to gain insights into student interactions. The results showed that Random Forest accurately predicted successful students at the end of the class with an accuracy of 88.3% with an equal width and information gain ratio.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Online and Blended Learning · Advanced Technologies in Various Fields
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, Petaling Jaya 47810, Selangor, Malaysia
Department of Computing, Middle East College, Knowledge Oasis Muscat, P.B. No. 79, Al Rusayl 124, Oman
Faculty of Ocean Engineering Technology and Informatics (FTKKI), University Malaysia Terengganu, Kuala Terengganu 21030, Terengganu, Malaysia
References (50)
Show source-provided references
1. Blended learning in higher education: Trends and capabilities10.1007/s10639-019-09886-3 ↗
2. Ali, M.F., Joyes, G., and Ellison, L. (2013, January 4–6). Using blended learning to enhance students’ cognitive presence. Proceedings of the 2013 International Conference on Informatics and Creative Multimedia, ICICM 2013, Kuala Lumpur.10.1109/ICICM.2013.42 ↗
3. Cleveland-Innes, M., and Wilton, D. (2018). Guide to Blended Learning, Commonwealth of Learning.10.56059/11599/3095 ↗
4. Medina, L.C. (2018). Blended learning: Deficits and prospects in higher education. Australas. J. Educ. Technol., 34.10.14742/ajet.3100 ↗
5. Schmidt, S.M.P., and Ralph, D.L. (2016). The flipped classroom: A twist on teaching. Contemp. Issues Educ. Res., 9.10.19030/cier.v9i1.9544 ↗
6. A framework for institutional adoption and implementation of blended learning in higher education10.1016/j.iheduc.2012.09.003 ↗
7. Hasan, R., Palaniappan, S., Mahmood, S., Shah, B., Abbas, A., and Sarker, K.U. (2019). Enhancing the teaching and learning process using video streaming servers and forecasting techniques. Sustainability, 11.10.3390/su11072049 ↗
8. Educational data mining and learning analytics: An updated survey10.1002/widm.1355 ↗
9. Naidu, V.R., Singh, B., Hasan, R., and Al Hadrami, G. (2017). Learning analytics for smart classrooms in higher education. IJAEDU- Int. E-J. Adv. Educ., 440–446.10.18768/ijaedu.338514 ↗
10. Learning analytics in higher education10.1002/aehe.20121 ↗
11. Atif, A., Richards, D., Bilgin, A., and Marrone, M. (2013, January 1–4). Learning analytics in higher education: A summary of tools and approaches. Proceedings of the 30th Annual conference on Australian Society for Computers in Learning in Tertiary Education, ASCILITE 2013, Sydney, Australia.10.14742/apubs.2013.1524 ↗
12. Behavior-based grade prediction for MOOCs via time series neural networks
13. Using learning analytics to evaluate a video-based lecture series10.1080/0142159X.2017.1395001 ↗
14. A review: Emerging trends of big data in higher educational institutions10.1007/978-981-15-2329-8_29 ↗
15. An empirical study on attribute selection of student performance prediction model10.1504/IJLT.2017.088407 ↗
16. Data mining in education10.1002/widm.1075 ↗
17. Predicting students’ final performance from participation in on-line discussion forums10.1016/j.compedu.2013.06.009 ↗
18. Hasan, R., Palaniappan, S., Raziff, A.R.A., Mahmood, S., and Sarker, K.U. (2018, January 13–14). Student academic performance prediction by using decision tree algorithm. Proceedings of the 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018—Proceedings, IEEE, Kuala Lumpur, Malaysia.10.1109/ICCOINS.2018.8510600 ↗
19. Shana, J., and Venkatachalam, T. (2011). Identifying key performance indicators and predicting the result from student data. Int. J. Comput. Appl., 25.10.5120/3057-4169 ↗
20. Big data in educational data mining and learning analytics10.15680/IJIRCCE.2014.0212044 ↗
21. Guest editorial - learning and knowledge analytics
22. Using learning analytics to develop early-warning system for at-risk students
23. Arnold, K.E., and Pistilli, M.D. (–2, January 29). Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the ACM International Conference Proceeding Series, Vancouver, BC, Canada.
24. Corrigan, O., Smeaton, A.F., Glynn, M., and Smyth, S. (2015, January 15–18). Using educational analytics to improve test performance. Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Toledo, Spain.10.1007/978-3-319-24258-3_4 ↗
25. How learning analytics can early predict under-achieving students in a blended medical education course
26. LaaN: Convergence of knowledge management and technology-enhanced learning10.1109/TLT.2011.33 ↗
27. Al learning analytics dashboard to improve students’ performance and success
28. Giannakos, M.N., Chorianopoulos, K., and Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. Int. Rev. Res. Open Distance Learn., 16.10.19173/irrodl.v16i1.1976 ↗
29. Hasnine, M.N., Akcapinar, G., Flanagan, B., Majumdar, R., Mouri, K., and Ogata, H. (2018, January 26–30). Towards final scores prediction over clickstream using machine learning methods. Proceedings of the ICCE 2018—26th International Conference on Computers in Education, Workshop Proceedings, Manila, Philippines.
30. Modelling and predicting student’s academic performance using classification data mining techniques
31. Educational data mining: A survey from 1995 to 200510.1016/j.eswa.2006.04.005 ↗
32. Knowledge discovery with genetic programming for providing feedback to courseware authors10.1007/s11257-004-7961-2 ↗
33. Supervised data mining approach for predicting student performance
34. Student performance prediction
35. Abu Zohair, L.M. (2019). Prediction of Student’s performance by modelling small dataset size. Int. J. Educ. Technol. High. Educ., 16.10.1186/s41239-019-0160-3 ↗
36. Daud, A., Aljohani, N.R., Abbasi, R.A., Lytras, M.D., Abbas, F., and Alowibdi, J.S. (2017, January 7). Predicting student performance using advanced learning analytics. Proceedings of the 26th International Conference on World Wide Web Companion—WWW ’17 Companion, Perth, Australia.10.1145/3041021.3054164 ↗
37. An overview and comparison of supervised data mining techniques for student exam performance prediction10.1016/j.compedu.2019.103676 ↗
38. Saa, A.A., Al-Emran, M., and Shaalan, K. (2020). Mining student information system records to predict students’ academic performance. Advances in Intelligent Systems and Computing, Springer.10.1007/978-3-030-14118-9_23 ↗
39. López, M.I., Luna, J.M., Romero, C., and Ventura, S. (2012, January 19–21). Classification via clustering for predicting final marks based on student participation in forums. Proceedings of the 5th International Conference on Educational Data Mining, EDM 2012, Chania, Greece.
40. Predicting student academic performance in KSA using data mining techniques
41. Analysis of student academic performance using clustering techniques
42. Analysis of student result using clustering techniques
43. Association Rule Discovery for Student Performance Prediction Using Metaheuristic Algorithms
44. Predicting examination results using association rule mining
45. Setting learning analytics in context: Overcoming the barriers to large-scale adoption10.18608/jla.2014.13.7 ↗
46. Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data10.1007/s10489-012-0374-8 ↗
47. Early dropout prediction using data mining: A case study with high school students10.1111/exsy.12135 ↗
48. Interpretable multiview early warning system adapted to underrepresented student populations10.1109/TLT.2019.2911079 ↗
49. Orange: Data mining toolbox in python
50. FreeViz-An intelligent multivariate visualization approach to explorative analysis of biomedical data10.1016/j.jbi.2007.03.010 ↗