Abstract
Online learning platforms have made knowledge easily and readily accessible for people, yet the ratio of students withdrawing or failing a course is relatively high compared to in-class learning as students do not get enough attention from the instructors. We propose an ensemble learning framework for the early identification of students who are at risk of dropping or failing a course. The framework fuses student demographics, assessment results, and daily activities as the total learning statistics and considers the slicing of data with regard to timestamp. A stacking ensemble classifier is then built upon eight base machine learning classification algorithms. Results show that the proposed model outperforms the base classifiers. The framework enables the early identification of possible failures at the half of a course with 85% accuracy; with full data incorporated, an accuracy of 94.5% is achieved. The framework shows great promise for instructors and online platforms to design interventions before it is too late to help students to pass their courses.
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References
R. Alshabandar, A. Hussain, R. Keight, A. Laws, T. Baker, The application of gaussian mixture models for the identification of at-risk learners in massive open online courses, in 2018 IEEE Congress on Evolutionary Computation (CEC) (IEEE, Piscataway, 2018), pp. 1–8
L. Haiyang, Z. Wang, P. Benachour, P. Tubman, A time series classification method for behaviour-based dropout prediction, in 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT) (IEEE, Piscataway, 2018), pp. 191–195
S.-U. Hassan, H. Waheed, N.R. Aljohani, M. Ali, S. Ventura, F. Herrera, Virtual learning environment to predict withdrawal by leveraging deep learning. Int. J. Intell. Syst. 34(8), 1935–1952 (2019)
H. Heuer, A. Breiter, Student success prediction and the trade-off between big data and data minimization, in DeLFI 2018-Die 16.E-Learning Fachtagung Informatik (2018)
M. Hlosta, Z. Zdrahal, J. Zendulka, Ouroboros: early identification of at-risk students without models based on legacy data, in Proceedings of the Seventh International Learning Analytics & Knowledge Conference (ACM, New York, 2017), pp. 6–15
J. Kuzilek, M. Hlosta, Z. Zdrahal, Open university learning analytics dataset. Sci. Data 4, 170171 (2017)
J. Kuzilek, J. Vaclavek, V. Fuglik, Z. Zdrahal, Student drop-out modelling using virtual learning environment behaviour data, in European Conference on Technology Enhanced Learning (Springer, Berlin, 2018), pp. 166–171
J. Kuzilek, J. Vaclavek, Z. Zdrahal, V. Fuglik, Analysing student vle behaviour intensity and performance, in European Conference on Technology Enhanced Learning (Springer, Berlin, 2019), pp. 587–590
W. Li, M. Gao, H. Li, Q. Xiong, J. Wen, Z. Wu, Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning, in 2016 International Joint Conference on Neural Networks (IJCNN) (IEEE, Piscataway, 2016), pp. 3130–3137
Z. Liu, J. He, Y. Xue, Z. Huang, M. Li, Z. Du, Modeling the learning behaviors of massive open online courses, in 2015 IEEE International Conference on Big Data (Big Data) (IEEE, Piscataway, 2015), pp. 2883–2885
T.J. Mock, T.L. Estrin, M.A. Vasarhelyi, Learning patterns, decision approach, and value of information. J. Account. Res. 10(1), 129 (1972)
R.L. Peach, S.N. Yaliraki, D. Lefevre, M. Barahona, Data-driven unsupervised clustering of online learner behaviour (2019). arXiv preprint arXiv:1902.04047
S. Rizvi, B. Rienties, S.A. Khoja, The role of demographics in online learning; a decision tree based approach. Comput. Educ. 137, 32–47 (2019)
R.W. Taylor, Pros and cons of online learning—a faculty perspective. J. Eur. Ind. Train. 26(1), 24–37 (2002)
M. Teruel, L.A. Alemany, Co-embeddings for student modeling in virtual learning environments, in Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (ACM, New York, 2018), pp. 73–80
D.H. Wolpert, Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
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Yu, L., Cai, T. (2021). Ensemble Learning for Early Identification of Students at Risk from Online Learning Platforms. In: Stahlbock, R., Weiss, G.M., Abou-Nasr, M., Yang, CY., Arabnia, H.R., Deligiannidis, L. (eds) Advances in Data Science and Information Engineering. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71704-9_35
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DOI: https://doi.org/10.1007/978-3-030-71704-9_35
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