The Predictive Learning Analytics for Student Dropout Using Data Mining Technique: A Systematic Literature Review-Reference-Cited by-同舟云学术

The Predictive Learning Analytics for Student Dropout Using Data Mining Technique: A Systematic Literature Review

Published:2023 Issue: Volume: Page:9-17
ISSN:2191-530X
Container-title:Advances in Technology Transfer Through IoT and IT Solutions
language:
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Author:

Nurmalitasari ,Awang Long Zalizah,Mohd Noor Mohammad Faizuddin

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-25178-8_2

Reference49 articles.

1. Y. Chen, A. Johri, H. Rangwala, Running out of STEM: a comparative study across STEM majors of college students at-risk of dropping out early, in ACM International Conference Proceeding Series (2018), pp. 270–279

2. F. Jiménez, A. Paoletti, G. Sánchez, G. Sciavicco, Predicting the risk of academic dropout with temporal multi-objective optimization. IEEE Trans. Learn. Technol. 12, 225–236 (2019). https://doi.org/10.1109/TLT.2019.2911070

3. S. Sultana, S. Khan, M.A. Abbas, Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts. Int. J. Electr. Eng. Educ. 54, 105–118 (2017). https://doi.org/10.1177/0020720916688484

4. S. Aydin, A. Öztürk, G.T. Büyükköse et al., An investigation of drop-out in open and distance education. Educ. Sci. Theory Pract. 19, 40–57 (2019). https://doi.org/10.12738/estp.2019.2.003

5. M.S. Larsen, K.P. Kornbeck, R.M. Kristensen et al., Dropout phenomena at universities: what is dropout? Why does dropout occur? What can be done by the universities to prevent or reduce it? A systematic review (2013)

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