Literaturnachweis - Detailanzeige
| Autor/inn/en | Li, Yuntao; Fu, Chengzhen; Zhang, Yan |
|---|---|
| Titel | When and Who at Risk? Call Back at These Critical Points [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017). |
| Quelle | (2017), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Online Courses; Mass Instruction; Student Behavior; Learning Activities; Video Technology; Lecture Method; Group Discussion; Assignments; Accuracy; Prediction; Intervention; Dropout Rate; Models; Scores; Computer Software; Data Analysis; Classification; Comparative Analysis; Mathematics |
| Abstract | Since MOOC is suffering high dropout rate, researchers try to explore the reasons and mitigate it. Focusing on this task, we employ a composite model to infer behaviors of learners in the coming weeks based on his/her history log of learning activities, including interaction with video lectures, participation in discussion forum, and performance of assignments, etc. The prediction accuracy of our proposed model outperforms related methods. Besides, we try combining the model with suggested interventions, such as sending reminder emails to at-risk learners. Future work, which is currently underway, will evaluate its influence on mitigating dropout rate. [For the full proceedings, see ED596512.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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