Version 1
: Received: 16 November 2023 / Approved: 16 November 2023 / Online: 16 November 2023 (11:24:43 CET)
Version 2
: Received: 20 December 2023 / Approved: 20 December 2023 / Online: 20 December 2023 (10:19:25 CET)
He, G., Huang, C., Yang, S., Lwin, K., Ouh, E.L., Ju, R., & Zhu, X. (2023). Predicting Students' Progress in Intelligent Tutoring Systems. Preprints. https://doi.org/10.20944/preprints202311.1073.v2
Chicago/Turabian Style
He, G., Ran Ju and Xiaoming Zhu. 2023 "Predicting Students' Progress in Intelligent Tutoring Systems" Preprints. https://doi.org/10.20944/preprints202311.1073.v2
Abstract
Intelligent Tutoring Systems (ITS) are increasingly popular for online learning. These systems use adaptive algorithms to recommend relevant content based on students' profiles. However, instructors need to periodically assess students' performance to ensure learning outcomes and adjust strategies accordingly. Our objective is to predict students' progress in advance, enabling teachers to make quicker decisions and facilitating the iterative process of adaptive algorithms. For this study, we collected a dataset from ALIN, an online learning platform, consisting of over 5,000 students' learning records and test results. Using this dataset, we conducted experiments employing various machine learning algorithms. The results indicate that learning behavior contributes to improving forecast performance, while students' progress strongly correlates with their previous test results. Additionally, we discovered that students' progress can be indirectly predicted by forecasting their scores. Furthermore, by breaking down overall scores into several distinct components and predicting individual scores for each component, the accuracy of the forecasts can be improved.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
20 December 2023
Commenter:
Chengwei Huang
Commenter's Conflict of Interests:
Author
Comment:
Change of author-ship. Original author list:Guijia He, Chengwei Huang, Steven Yang, Kelvin Lwin, Eng Lieh Ouh, Ran Ju, Yuanmi Chen, Xiaoming Zhu. Updated List: Guijia He, Chengwei Huang, Steven Yang, Kelvin Lwin, Eng Lieh Ouh, Ran Ju, Xiaoming Zhu. As Dr. Yuanmi Chen requested, and other co-authors agreed, to change the author list. Reason: Dr. Chen indicated that the initial work she undertook is no longer significant in the final version of the paper.
Commenter: Chengwei Huang
Commenter's Conflict of Interests: Author
As Dr. Yuanmi Chen requested, and other co-authors agreed, to change the author list. Reason: Dr. Chen indicated that the initial work she undertook is no longer significant in the final version of the paper.