An Approximation of Label Distribution-Based Ensemble Learning Method for Online Educational Prediction


  • Long Zhang South China University of Technology
  • Shu Kai
  • Huang Keyu
  • Zhang Ruiqiu


ensemble learning, light gradient boosting machine, channel attention network, CrossEntropy, label distribution approximation


Online education becomes increasingly important since traditional learning is shocked heavily by COVID-19. To better develop personalized learning plans for students, it is necessary to build a model that can automatically evaluate students’ performance in online education. For this purpose, in this study we propose an ensemble learning method named light gradient boosting channel attention network (LGBCAN), which is based on label distribution estimation. First, the light gradient boosting machine (LightGBM) is used to predict the performance in online learning tasks. Then The Channel Attention Network (CAN) model further improves the function of LightGBM by focusing on better results in the K-fold CrossEntropy of LightGBM. The results are converted into predicted classes through post-processing methods named approximation of label distribution to complete the classification task. The experiments are employed on two datasets, data science bowl (DSB) and answer correctness prediction (ACP). The experimental results in both datasets suggest that our model has better robustness and generalization ability.



[2] K.D. Rajab, The Effectiveness and Potential of E-Learning in War Zones: An Empirical Comparison of Face-to-Face and Online Education in Saudi Arabia, in IEEE Access, vol. 6, pp. 6783-6794, 2018

[3] N. Todorova, N. Bjorn-Andersen, University learning in times of crisis: The role of IT. Accounting Education, vol. 20, no. 6, pp. 597-599. Dec 2011.

[4] W. Villegas-Ch, M. Román-Cañizares, and X. Palacios-Pacheco. Improvement of an online education model with the integration of machine learning and data analysis in an LMS, Applied Sciences, vol. 10, issue 15, pp. 1-18. August 2020.

[5] T. Chen, L. Peng, X. Yin, J. Rong, J. Yang, and G. Cong, Analysis of user satisfaction with online education platforms in China during the COVID-19 pandemic, Healthcare, vol. 8, no. 3, pp. 200, Jul 2020.

[6] D. Benta, G. Bologa, I. Dzitac. E-learning Platforms in Higher Education. Case Study, Procedia Computer Science, vol. 31, pp. 1170-1176, May. 2014.

[7] R. Al-Shabandar, A. Hussain, A. Laws, R. Keight, J. Lunn and N. Radi, Machine learning approaches to predict learning outcomes in Massive open online courses, 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, 2017, pp. 713-720.

[8] S. Houlden and G. Veletsianos. A post humanist critique of flexible online learning and its 'anytime anyplace' claims. British Journal of Educational Technology. 2019.

[9] H. Xu., Oct. 2018.

[10] C. Xiao and Y. Li, Analysis on the Influence of the Epidemic on the Education in China, 2020 International Conference on Big Data and Informatization Education (ICBDIE), Zhangjiajie, China, pp. 143-147, 2020.

[11] B. McCarthy, L. Li, Tiu, M., Atienza, S. (2013). PBS KIDS mathematics transmedia suites in preschool homes. In Proceedings of the 12th International Conference on Interaction Design and Children (pp. 128-136). ACM.

[12] B. Thorns, E. Eryilmaz. "Introducing a twitter discussion board to support learning in online and blended learning environments", Education and Information Technologies, Vol. 20, No. 2, pp. 265-283. Jun. 2015.

[13] D. Gaševic, C. Rose, G. Siemens, A. Wolff, and Z. Zdrahal, "Learning Analytics and Machine Learning," Proc. Fourth Int. Conf. Learn. Anal. Knowl. LAK, 14, pp. 287-288, 2014.

[14] J. Levy, D. Mussack, M Brunner, U Keller and P Cardoso-Leite, A Fischbach. Contrasting Classical and Machine Learning Approaches in the Estimation of Value-Added Scores in Large-Scale Educational Data. rontiers in Psychology, Aug 2020 2190.

[15] H. Vartiainen, M. Tedre and T Valtonen. Learning machine learning with very young children: Who is teaching whom?, International Journal of Child-Computer Interaction. Vol.9, No. 25, Sep. 2020.

[16] E. Dragan. "Interactive educational game using machine learning", In Proceedings of the 2020 ACM Interaction Design and Children Conference: Extended Abstracts pp. 272-275. June. 2020.

[17] Sano, Mina. Statistical Analysis of Elements of Movement in Musical Expression in Early Childhood Using 3D Motion Capture and Evaluation of Musical Development Degrees through Machine Learning, World Journal of Education, Vol. 8 No. 3, pp. 118-130, 2018.

[18] J. Hodges, S. Mohan. "Machine Learning in Gifted Education: A Demonstration Using Neural Networks", Gifted Child Quarterly, Vol. 63, No. 4, pp. 243-252. Sep. 2019.

[19] C. K Blackwell, A. R. Lauricella, E Wartella. "Factors influencing digital technology use in early childhood education", Computers and Education, Vol. 7, No. 7, pp. 82-90, Aug. 2014.

[20] R. S. M. d. Barros, S. Garrido T. de Carvalho Santos and P. M. Goní§alves Júnior, A Boosting-like Online Learning Ensemble, 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 1871-1878.

[21] H. Chen, S. Lundberg, S. Lee. Checkpoint Ensembles: Ensemble Methods from a Single Training Process,, 2017.

[22] T. Chen and C. Guestrin. Xgboost: A scalable tree boosting system, In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 785-794. Aug. 2016.

[23] G. Ke, Q. Meng, T Finley, T Wang, W Chen and W Ma. Lightgbm: A highly efficient gradient boosting decision tree, In: Advances in Neural Information Processing Systems. pp. 3149-3157. Dec. 2017.

[24] J. Qiu et al., Modeling and Predicting Learning Behavior in MOOCs, Proc. Ninth ACM Int. Conf. Web Search Data Min., pp. 93-102, 2016.

[25] P. Moreno-Ger, D. Burgos, I. Martí­nez-Ortiz, J. L. Sierra and B. Fernández-Manjón. Educational game design for online education. Computers in Human Behavior, Vol. 24, No. 6, pp. 2530-2540. Sep. 2008.

[26] D. Benta, G. Bologa, S. Dzitac, I. Dzitac, University Level Learning and Teaching via E-Learning Platforms, vol. 55, pp. 1366-1373, 2015.

[27] J. Wong, M. Baars, D. Davis, Tim Van Der Zee, Geert-Jan Houben, Fred Paas. Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review, International Journal of Human-Computer Interaction, vol. 35, issue 4-5, 356-373, 2019.

[28] V. Gamper and S. Nothelfer. The Future of Education Trend Report", Center for Digital Technology and Management. Jun. 2015.

[29] S. B. Kotsiantis. Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades, Artificial Intelligence Review, Vol. 37, No. 4, pp.331-344, Apr. 2012.

[30] D. Wang Y. Zhang, and Y. Zhao. LightGBM: an effective miRNA classification method in breast cancer patients, Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics. pp. 7-11, Oct. 2017.

[31] Rong, E. Fonseca, D. Bogdanov, O. Slizovskaia, E. Gomez and Serra. Acoustic scene classification by fusing LightGBM and VGG-net multichannel predictions, roc. IEEE AASP Challenge Detection Classification Acoust. Scenes Events. pp.1-4, Nov. 2017.

[32] J Zhou, G Wang S Yang, J Liu, W Xu, Z Wang and J Ye. Automatic sleep stage classification with single channel EEG signal based on Two-Layer stacked ensemble model, IEEE Access, Vol. 8, pp. 57283-57297, 2020.

[33] Y Ju, G Sun, Q Chen, M Zhang, H Zhu and M U Rehman. A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting, IEEE Access, vol.7: 28309-28318. Feb. 2019.

[34] E. PHUA, N. K. BATCHA. Comparative Analysis of Ensemble Algorithms' Prediction Accracies in Education Data Mining. JCR. Vol.7, Issue 3, pp. 37-40. July 2020.

[35] J. Xu, K. H. Moon and M. van der Schaar, A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs, in IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 5, pp. 742-753, Aug. 2017.

[36] Z. Shi and M. Han, Support Vector Echo-State Machine for Chaotic Time-Series Prediction, in IEEE Transactions on Neural Networks, vol. 18, no. 2, pp. 359-372, March 2007.

[37] Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition, in Proceedings of the IEEE on, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.

[38] Jie Hu, Li Shen, Gang Sun. Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132-7141, 2018.



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