دورية أكاديمية
Identifying At-Risk Online Learners by Psychological Variables Using Machine Learning Techniques
| العنوان: | Identifying At-Risk Online Learners by Psychological Variables Using Machine Learning Techniques |
|---|---|
| المؤلفون: | Hsiang-yu Chien, Oi-Man Kwok, Yu-Chen Yeh, Noelle Wall Sweany, Eunkyeng Baek, William Alex McIntosh |
| المصدر: | Online Learning, Vol 24, Iss 4 (2020) |
| بيانات النشر: | Online Learning Consortium, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Theory and practice of education |
| مصطلحات موضوعية: | machine learning, random forest, online learning, at-risk online learners, stepwise regression, logistic regression, Theory and practice of education, LB5-3640 |
| الوصف: | The purpose of this study was to investigate a predictive model of online learners’ learning outcomes through machine learning. To create a model, we observed students’ motivation, learning tendencies, online learning-motivated attention, and supportive learning behaviors along with final test scores. A total of 225 college students who were taking online courses participated. Longitudinal data were collected over three semesters (T1, T2, and T3). T3 was used as training data given that it contained the largest sample size across all three data waves. To analyze the data, two approaches were applied: (a) stepwise logistic regression and (b) random forest (RF). Results showed that RF used fewer items and predicted final grades more accurately in a small sample. Furthermore, it selected four items that might potentially be used to identify at-risk learners even before they enroll in an online course. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2472-5749 2472-5730 |
| Relation: | https://olj.onlinelearningconsortium.org/index.php/olj/article/view/2320; https://doaj.org/toc/2472-5749; https://doaj.org/toc/2472-5730 |
| DOI: | 10.24059/olj.v24i4.2320 |
| URL الوصول: | https://doaj.org/article/81f4a5dce58344cc8c41ad962ec658b7 |
| رقم الأكسشن: | edsdoj.81f4a5dce58344cc8c41ad962ec658b7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24725749 24725730 |
|---|---|
| DOI: | 10.24059/olj.v24i4.2320 |