Abstract
With the development of computer technology, “Internet + education" has become a hot topic, which has promoted the development of online education. The impact of the epidemic has brought new challenges to online education, and the effect of online teaching has sparked extensive research. Why online learning has not received satisfying learning effect? And how to improve the quality of online education have become the focus of research. To address these challenges in online learning, this paper studies the important factors affect the students’ learning achievements based on the explicit and implicit features extracted from the online learning behavior data. And a wealth of machine learning models are explored to predict the learner’s achievement, including the original linear models, the ensemble learning models and the advanced deep learning models. With extensive experimental results achieved, we gain reasonable explanation on the influence factors hander the learning achievement, and predict the learning achievements with the explainable machine learning models. Finally, some suggestions are put forward to improve students’ learning behaviors, aiming to promote the quality of online learning in the long run.
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Acknowledgments
This work is supported by Shandong Provincial Project of Graduate Education Quality Improvement (No. SDYJG21104, No. SDYJG19171), the Key R &D Program of Shandong Province, China (NO. 2021SFGC0104, NO. 2021CXGC010506), the Natural Science Foundation of Shandong Province, China (No. ZR2020LZH008, ZR2021MF118, ZR2022LZH003), the National Natural Science Foundation of China under Grant (NO. 62101311, No. 62072290), the Postgraduate Quality Education and Teaching Resources Project of Shandong Province (SDYKC2022053, SDYAL2022060), the Shandong Normal University Research Project of Education and Teaching(No. 2019XM48), and Industry-University Cooperation and Education Project of Ministry of Education (No. 2206026 95231855).
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Peng, H., Yu, X., Jiao, X., Yin, Q., Zhao, L. (2024). Online Learning Behavior Analysis and Achievement Prediction with Explainable Machine Learning. In: Vaidya, J., Gabbouj, M., Li, J. (eds) Artificial Intelligence Security and Privacy. AIS&P 2023. Lecture Notes in Computer Science, vol 14510. Springer, Singapore. https://doi.org/10.1007/978-981-99-9788-6_3
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DOI: https://doi.org/10.1007/978-981-99-9788-6_3
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