short-paper

Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?

Authors: Mutlu Cukurova, Qi Zhou, Daniel Spikol, Lorenzo LandolfiAuthors Info & Claims

LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

Pages 270 - 275

https://doi.org/10.1145/3375462.3375484

Published: 23 March 2020 Publication History

Abstract

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.

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Cited By

Zhou QSuraworachet WCukurova M(2024)Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative LearningProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636894(392-403)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636894
Dang BNguyen AJärvelä S(2024)The Unspoken Aspect of Socially Shared Regulation in Collaborative Learning: AI-Driven Learning Analytics Unveiling ‘Silent Pauses’Proceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636874(231-240)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636874
Webb NGiuliani MLemaignan SGrollman DBroadbent EJu WSoh HWilliams T(2024)Measuring Visual Social Engagement from Proxemics and Gaze in the Real WorldCompanion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3640678(1110-1113)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610978.3640678
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Index Terms

Modelling collaborative problem-solving competence with transparent learning analytics: is video data enough?
1. Applied computing
  1. Education
    1. Collaborative learning
    2. Computer-assisted instruction

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LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

March 2020

679 pages

ISBN:9781450377126

DOI:10.1145/3375462

General Chairs:
Christoph Rensing
Technische Universität Darmstadt, Germany
,
Hendrik Drachsler
DIPF Leibniz Institute for Research and Information in Education & University of Frankfurt, Germany
,
Program Chairs:
Vitomir Kovanović
University of South Australia, Australia
,
Niels Pinkwart
Humboldt-Universität zu Berlin, Germany
,
Maren Scheffel
Open Universiteit, Netherlands
,
Katrien Verbert
KU Leuven, Belgium

Copyright © 2020 ACM.

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New York, NY, United States

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Published: 23 March 2020

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LAK '20

LAK '20: 10th International Conference on Learning Analytics and Knowledge

March 23 - 27, 2020

Frankfurt, Germany

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LAK '20 Paper Acceptance Rate 80 of 261 submissions, 31%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Cited By

Zhou QSuraworachet WCukurova M(2024)Harnessing Transparent Learning Analytics for Individualized Support through Auto-detection of Engagement in Face-to-Face Collaborative LearningProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636894(392-403)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636894
Dang BNguyen AJärvelä S(2024)The Unspoken Aspect of Socially Shared Regulation in Collaborative Learning: AI-Driven Learning Analytics Unveiling ‘Silent Pauses’Proceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636874(231-240)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636874
Webb NGiuliani MLemaignan SGrollman DBroadbent EJu WSoh HWilliams T(2024)Measuring Visual Social Engagement from Proxemics and Gaze in the Real WorldCompanion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3610978.3640678(1110-1113)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.1145/3610978.3640678
Salam HCeliktutan OGunes HChetouani M(2024)Automatic Context-Aware Inference of Engagement in HMI: A SurveyIEEE Transactions on Affective Computing10.1109/TAFFC.2023.327870715:2(445-464)Online publication date: Apr-2024
https://doi.org/10.1109/TAFFC.2023.3278707
Alwahaby HCukurova M(2024)Navigating the ethical landscape of multimodal learning analytics: a guiding frameworkEthics in Online AI-based Systems10.1016/B978-0-443-18851-0.00014-7(25-53)Online publication date: 2024
https://doi.org/10.1016/B978-0-443-18851-0.00014-7
D’Mello SDuran NMichaels AStewart A(2024)Improving collaborative problem-solving skills via automated feedback and scaffolding: a quasi-experimental study with CPSCoach 2.0User Modeling and User-Adapted Interaction10.1007/s11257-023-09387-634:4(1087-1125)Online publication date: 14-Feb-2024
https://doi.org/10.1007/s11257-023-09387-6
Zhou QSuraworachet WCukurova M(2024)Detecting non-verbal speech and gaze behaviours with multimodal data and computer vision to interpret effective collaborative learning interactionsEducation and Information Technologies10.1007/s10639-023-12315-129:1(1071-1098)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10639-023-12315-1
Candel Ede-la-Peña CYuste B(2024)Pre-service teachers’ perception of active learning methodologies in history: Flipped classroom and gamification in an e-learning environmentEducation and Information Technologies10.1007/s10639-023-11924-029:3(3365-3387)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1007/s10639-023-11924-0
Li QLuximon YZhang JSong Y(2023)Measuring and classifying students' cognitive load in pen‐based mobile learning using handwriting, touch gestural and eye‐tracking dataBritish Journal of Educational Technology10.1111/bjet.1339455:2(625-653)Online publication date: 30-Sep-2023
https://doi.org/10.1111/bjet.13394
Liu QZheng XLiu YWu LZhang SZhang NWang Q(2023)Exploration of the characteristics of teachers' multimodal behaviours in problem‐oriented teaching activities with different response levelsBritish Journal of Educational Technology10.1111/bjet.1333255:1(181-207)Online publication date: 2-May-2023
https://doi.org/10.1111/bjet.13332
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