A Learning Analytics Approach Using Social Network Analysis and Binary Classifiers on Virtual Resource Interactions for Learner Performance Prediction

Authors

DOI:

https://doi.org/10.19173/irrodl.v23i4.6445

Keywords:

social network analysis, machine learning, binary classifiers, supervised and ensemble learning algorithms, virtual resources interactions, learners’ academic performance

Abstract

The COVID-19 pandemic induced a digital transformation of education and inspired both instructors and learners to adopt and leverage technology for learning. This led to online learning becoming an important component of the new normal, with home-based virtual learning an essential aspect for learners on various levels. This, in turn, has caused learners of varying levels to interact more frequently with virtual resources to supplement their learning. Even though virtual learning environments provide basic resources to help monitor the learners’ online behaviour, there is room for more insights to be derived concerning individual learner performance. In this study, we propose a framework for visualising learners’ online behaviour and use the data obtained to predict whether the learners would clear a course. We explored a variety of binary classifiers from which we achieved an overall accuracy of 80%–85%, thereby indicating the effectiveness of our approach and that learners’ online behaviour had a significant effect on their academic performance. Further analysis showed that common patterns of behaviour among learners and/or anomalies in online behaviour could cause incorrect interpretations of a learner’s performance, which gave us a better understanding of how our approach could be modified in the future.

References

Agudo-Peregrina, Á. F., Hernández-García, Á., & Iglesias-Pradas, S. (2012, October). Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications. In F. José García, L. Vicent, M. Ribó, A. Climent, J. L. Sierra, & A. Sarasa (Eds.), 2012 International Symposium on Computers in Education (SIIE) (pp. 1–6). Institute of Electrical and Electronics Engineers. https://ieeexplore.ieee.org/document/6403184

Al-Azawei, A., & Al-Masoudy, M. (2020). Predicting learners’ performance in virtual learning environment (VLE) based on demographic, behavioral and engagement antecedents. International Journal of Emerging Technologies in Learning (IJET), 15(9), 60–75. https://doi.org/10.3991/ijet.v15i09.12691

Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552

Chung, K. S. K., & Paredes, W. C. (2015). Towards a social networks model for online learning & performance. Educational Technology & Society, 18(3), 240–253. http://www.jstor.org/stable/jeductechsoci.18.3.240

de Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218–231. https://doi.org/10.1111/jcal.12130

Dragulescu, B., Bucos, M., & Vasiu, R. (2015). Social network analysis on educational data set in RDF format. Journal of Computing and Information Technology, 23(3), 269–281. https://doi.org/10.2498/cit.1002645

Golbeck, J. (2013). Network structure and measures. Analyzing the social Web (pp. 25–44). Morgan Kaufmann. https://doi.org/10.1016/C2012-0-00171-8

Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE—Life Sciences Education, 13(2), 167–178. https://doi.org/10.1187/cbe.13-08-0162

Khor, E.T. & Looi, C.K. (2019) A learning analytics approach to model and predict learners’ success in digital learning. In Y. W. Chew, K. M. Chan, and A. Alphonso (Eds.), Personalised Learning. Diverse Goals. One Heart. ASCILITE 2019 Singapore (pp. 476-480). https://repository.nie.edu.sg/bitstream/10497/22077/1/ASCILITE-2019-476.pdf

Koza, J. R., Bennett, F. H., Andre, D., & Keane, M. A. (1996). Automated design of both the topology and sizing of analog electrical circuits using genetic programming. In J. S. Gero & F. Sudweeks (Eds.), Artificial Intelligence in Design ’96 (pp. 151–170). Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0279-4_9

Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University learning analytics dataset. Scientific Data, 4(1), Article 170171. https://doi.org/10.1038/sdata.2017.171

Mastoory, Y., Harandi, S. R., & Abdolvand, N. (2016). The effects of communication networks on students’ academic performance: The synthetic approach of social network analysis and data mining for education. International Journal on Integrating Technology in Education, 5(4), 23–34. https://doi.org/10.5121/ijite.2016.5403

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

Mariame, O., Khouljian, S. & Kerbeb, M.L. (2021). Feature engineering, mining for predicting student success based on interaction with the virtual learning environment using artificial neural network. Annals of the Romanian Society for Cell Biology, 25(6), 12734–12746. https://www.annalsofrscb.ro/index.php/journal/article/view/8002/5907

Otte, E., & Rousseau, R. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441–453. https://doi.org/10.1177/016555150202800601

Rabbany, R., Takaffoli, M., & Zaiane, O. R. (2012). Social network analysis and mining to support the assessment of on-line student participation. ACM SIGKDD Explorations Newsletter, 13(2), 20–29. https://doi.org/10.1145/2207243.2207247

Rakic, S., Pavlovic, M., Softic, S., Lalic, B., & Marjanovic, U. (2019, November). An evaluation of student performance at e-learning platform. In 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) (pp. 681–686). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICETA48886.2019.9040066

Rakic, S., Softic, S., Vilkas, M., Lalic, B., & Marjanovic, U. (2018, November). Key indicators for student performance at the e-learning platform: An SNA approach. In 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA) (pp. 463–468). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICETA.2018.8572236

Rakic, S., Tasic, N., Marjanovic, U., Softic, S., Lüftenegger, E., & Turcin, I. (2020). Student performance on an e-learning platform: Mixed method approach. International Journal of Emerging Technologies in Learning (iJET), 15(2), 187–203. https://doi.org/10.3991/ijet.v15i02.11646

Rivas, A., Gonzalez-Briones, A., Hernandez, G., Prieto, J., & Chamoso, P. (2021). Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing, 423, 713–720. https://doi.org/10.1016/j.neucom.2020.02.125

Saqr, M., Fors, U., & Nouri, J. (2018). Using social network analysis to understand online problem-based learning and predict performance. PloS ONE, 13(9), Article e0203590. https://doi.org/10.1371/journal.pone.0203590

Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019, March). Student performance prediction and classification using machine learning algorithms. In ICEIT 2019: Proceedings of the 2019 8th International Conference on Educational and Information Technology (pp. 7–11). Association for Computing Machinery. https://doi.org/10.1145/3318396.3318419

Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., Alelyani, S., & Nawaz, R. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, Article 106189. https://doi.org/10.1016/j.chb.2019.106189

Wolff, A., Zdrahal, Z., Nikolov, A., & Pantucek, M. (2013, April). Improving retention: Predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In D. Suthers, K. Verbert, E. Duval, & X. Ochoa (Eds.), LAK ’13: Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 145–149). Association for Computing Machinery. https://doi.org/10.1145/2460296.2460324

Published

2022-11-01

How to Cite

Khor, E. T., & Darshan, D. (2022). A Learning Analytics Approach Using Social Network Analysis and Binary Classifiers on Virtual Resource Interactions for Learner Performance Prediction. The International Review of Research in Open and Distributed Learning, 23(4), 123–146. https://doi.org/10.19173/irrodl.v23i4.6445

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