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An intelligent tool for early drop-out prediction of distance learning students

  • Application of soft computing
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Abstract

Early identification of vulnerable students who are prone to drop-out is critical for devising effective educational retention strategies. Based on the Activity Theory, we undertake this challenge by considering students’ online activities as a useful predictor of their academic performance. Specifically, six artificial intelligence and related prediction models in individual and ensemble structures for tackling classification and multi-objective optimization tasks pertaining to early prediction of students’ performance are presented. A real database comprising online learning activities of 2544 students over 2 years in 84 science, engineering, and technology courses from an open distance education institution is used for evaluation. Comparing with other studies in the literature, the huge numbers of students and courses involved in this study pose a great challenge, due to increase in complexity of the problem and data dimensionality. The empirical results reveal statistically significant improvements of the ensemble-based models as compared with individual models in prediction of students’ performance. Implications of the results are analyzed and discussed from the Activity Theory perspective.

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Authors and Affiliations

  1. School of Science and Technology, Wawasan Open University, Penang, Malaysia

    Choo Jun Tan

  2. School of Technology, Peninsula College George Town, Penang, Malaysia

    Ting Yee Lim

  3. Deputy Vice Chancellor Office, HELP University, Kuala Lumpur, Malaysia

    Teik Kooi Liew

  4. Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, Australia

    Chee Peng Lim

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  1. Choo Jun Tan
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Correspondence to Choo Jun Tan.

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Tan, C.J., Lim, T.Y., Liew, T.K. et al. An intelligent tool for early drop-out prediction of distance learning students. Soft Comput 26, 5901–5917 (2022). https://doi.org/10.1007/s00500-021-06604-5

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