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Epistemic Network Analysis and Topic Modeling for Chat Data from Collaborative Learning Environment / Zhiqiang Cai, James W. Pennebaker and Brendan Eagan.

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This study investigates a possible way to analyze chat data from collaborative learning environments using epistemic network analysis and topic modeling. A 300-topic general topic model built from TASA (Touchstone Applied Science Associates) corpus was used in this study. 300 topic scores for each o...

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Bibliographic Details
Online Access: Full Text (via ERIC)
Main Authors: Cai, Zhiqiang, Pennebaker, James W. (Author), Eagan, Brendan (Author), Shaffer, David W. (Author), Dowell, Nia M. (Author), Graesser, Arthur C. (Author)
Format: eBook
Language:English
Published: [Place of publication not identified] : Distributed by ERIC Clearinghouse, 2017.
Subjects:
Network Analysis.
Computer Mediated Communication.
Cooperative Learning.
Scores.
Prediction.
Data Analysis.
Epistemology.
Learning Processes.
Academic Achievement.
Introductory Courses.
Psychology.
Undergraduate Students.
Personality Problems.
Tests.
Achievement Gains.
Course Descriptions.
Description
Summary:This study investigates a possible way to analyze chat data from collaborative learning environments using epistemic network analysis and topic modeling. A 300-topic general topic model built from TASA (Touchstone Applied Science Associates) corpus was used in this study. 300 topic scores for each of the 15,670 utterances in our chat data were computed. Seven relevant topics were selected based on the total document scores. While the aggregated topic scores had some power in predicting students' learning, using epistemic network analysis enables assessing the data from a different angle. The results showed that the topic score based epistemic networks between low gain students and high gain students were significantly different (t = 2.00). Overall, the results suggest these two analytical approaches provide complementary information and afford new insights into the processes related to successful collaborative interactions. [This paper was published in: X. Hu, T. Barnes, A. Hershkovitz, L. Paquette (Eds), "Proceedings of the 10th International Conference on Educational Data Mining" (pp. 104-111). Wuhan, China: EDM Society.]
Item Description:Sponsoring Agency: National Science Foundation (NSF).
Sponsoring Agency: Institute of Education Sciences (ED).
Sponsoring Agency: US Army Research Laboratory (ARL).
Sponsoring Agency: Office of Naval Research (ONR).
Contract Number: DRK120918409.
Contract Number: DRK121418288.
Contract Number: R305C120001.
Contract Number: W911INF1220030.
Contract Number: N0001412C0643.
Contract Number: N0001416C3027.
Abstractor: As Provided.
Educational level discussed: Higher Education.
Physical Description:1 online resource (1 online resource (8 pages))
Type of Computer File or Data Note:Text (Reports, Research)
Text (Speeches/Meeting Papers)
Preferred Citation of Described Materials Note:Grantee Submission, Paper presented at the International Conference on Educational Data Mining (10th, Wuhan, China, Jun 25-28, 2017).

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