Modelling collaborative problem-solving competence with transparent learning analytics: Is video data enough?
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
Modelling collaborative problem-solving competence with transparent learning analytics : Is video data enough? / Cukurova, Mutlu; Zhou, Qi; Spikol, Daniel; Landolfi, Lorenzo.
LAK 2020 Conference Proceedings - Celebrating 10 years of LAK: Shaping the Future of the Field - 10th International Conference on Learning Analytics and Knowledge. ACM Association for Computing Machinery, 2020. p. 270-275 (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - Modelling collaborative problem-solving competence with transparent learning analytics
T2 - 10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020
AU - Cukurova, Mutlu
AU - Zhou, Qi
AU - Spikol, Daniel
AU - Landolfi, Lorenzo
PY - 2020/3/23
Y1 - 2020/3/23
N2 - In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected ~500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach.
AB - In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected ~500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach.
KW - Collaborative problem-solving
KW - Decision trees
KW - Multimodal learning analytics
KW - Physical learning analytics
KW - Video analytics
UR - http://www.scopus.com/inward/record.url?scp=85082397681&partnerID=8YFLogxK
U2 - 10.1145/3375462.3375484
DO - 10.1145/3375462.3375484
M3 - Article in proceedings
AN - SCOPUS:85082397681
T3 - ACM International Conference Proceeding Series
SP - 270
EP - 275
BT - LAK 2020 Conference Proceedings - Celebrating 10 years of LAK
PB - ACM Association for Computing Machinery
Y2 - 23 March 2020 through 27 March 2020
ER -
ID: 256266108