Malmö University Publications
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  • 1.
    Cukurova, Mutlu
    et al.
    University College London, UK.
    Zhou, Qi
    University College London, UK.
    Spikol, Daniel
    Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Landolfi, Lorenzo
    Scuola Superiore Sant'Anna, Italy.
    Modelling Collaborative Problem-solving Competence with Transparent Learning Analytics: Is Video Data Enough?2020In: LAK20: THE TENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, Association for Computing Machinery (ACM), 2020, p. 270-275Conference paper (Refereed)
    Abstract [en]

    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 similar to 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.

  • 2.
    Spikol, Daniel
    et al.
    Malmö högskola, Faculty of Technology and Society (TS).
    Prieto, Luis P.
    Tallinn University, Tallinn, Estonia.
    Rodriguez-Triana, M. J.
    REACT Group, EPFL, Lausanne, Switzerland.
    Worsley, Marcelo
    Northwestern University, Evanston, IL, United States.
    Ochoa, Xavier
    ESPOL, Guayaquil, Ecuador.
    Cukurova, Mutlu
    UCL Knowledge Lab, London, United Kingdom.
    Vogel, Bahtijar
    Malmö högskola, Faculty of Technology and Society (TS).
    Ruffaldi, Emanuele
    Scuola Superiore Sant'Anna, Italy.
    Ringtved, Ulla Lunde
    University College Nordjylland.
    Current and Future Multimodal Learning Analytics Data Challenges2017In: Seventh International Learning Analytics & Knowledge Conference (LAK'17), ACM Digital Library, 2017, p. 518-519Conference paper (Refereed)
    Abstract [en]

    Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, high-frequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.

1 - 2 of 2
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