Literaturnachweis - Detailanzeige
| Autor/inn/en | Mbouzao, Boniface; Desmarais, Michel C.; Shrier, Ian |
|---|---|
| Titel | Methodology of Measure of Similarity in Student Video Sequence of Interactions [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020). |
| Quelle | (2020), (6 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Comparative Analysis; Video Technology; Interaction; Measurement Techniques; Markov Processes; Task Analysis; Online Courses; Mass Instruction; Learner Engagement; Accuracy; Prediction; Data Analysis |
| Abstract | Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov Chain and Edit Distance. However, these methods have caveats as we show with prototypical examples. This paper proposes a new methodology of comparing video sequences of interaction based both on time spent in each state and the succession of states by computing the distance between the transition matrices of the video interaction sequences. Results show the proposed methodology can better characterize video interaction in a task to discriminate which student is interacting with a video, or which video a student is interacting with. [For the full proceedings, see ED607784.] (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 | 2024/1/01 |
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