期刊
COMPLEXITY
卷 2021, 期 -, 页码 -出版社
WILEY-HINDAWI
DOI: 10.1155/2021/7463631
关键词
-
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2021R1C1C2004868]
- University of Tartu ASTRA Project PER ASPERA
- European Regional Development Fund
- National Research Foundation of Korea [2021R1C1C2004868] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Modeling students' online learning behavior to predict course achievement is crucial. Decision Tree and AdaBoost classification methods outperformed others on different datasets. The study demonstrates feasibility of accurately predicting students' course achievement by modeling their online learning behavior.
While modelling students' learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students' online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students' learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students' feature vectors and behavior model constitute a comprehensive students' learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students' achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students' course achievement with a high accuracy through modelling their learning behavior during online courses.
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