Literaturnachweis - Detailanzeige
| Autor/inn/en | Zhou, Guojing; Moulder, Robert G.; Sun, Chen; D'Mello, Sidney K. |
|---|---|
| Titel | Investigating Temporal Dynamics Underlying Successful Collaborative Problem Solving Behaviors with Multilevel Vector Autoregression [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022). |
| Quelle | (2022), (12 Seiten)
PDF als Volltext kostenfreie Datei |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Monographie |
| Schlagwörter | Cooperative Learning; Problem Solving; College Students; Physics; Videoconferencing; Educational Games; Science Instruction; Comparative Analysis; Peer Relationship; Guidelines; Models; Coding; Interaction Process Analysis |
| Abstract | In collaborative problem solving (CPS), people's actions are interactive, interdependent, and temporal. However, it is unclear how actions temporally relate to each other and what are the temporal similarities and differences between successful vs. unsuccessful CPS processes. As such, we apply a temporal analysis approach, Multilevel Vector Autoregression (mlVAR) to investigate CPS processes. Our data were collected from college students who collaborated in triads via a video-conferencing tool (Zoom) to collaborately engage a physics learning game. Video recordings of their verbal interactions were transcribed, coded using a validated CPS framework, and organized into sequences of 10-second windows. Then, mlVAR was applied to the successful vs. unsuccessful CPS sequences to build temporal models for each. A comparison of the models together with a qualitative analysis of the transcripts revealed six temporal relationships common to both, six unique to successful level attempts, and another eight unique to unsuccessful level attempts only. Generally, for successful outcomes, people were likely to answer clarification questions with reasons and to ask for suggestions according to the current game situation, while for unsuccessful CPS level attempts, people were more likely to struggle with unclear instructions and to respond to inappropriate ideas. Overall, our results suggest that mlVAR is an effective approach for temporal analyses of CPS processes by identifying relationships that go beyond a coding and counting approach. [For the full proceedings, see ED623995.] (As Provided). |
| Anmerkungen | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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