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 -