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Modelling Collaborative Problem-solving Competence with Transparent Learning Analytics: Is Video Data Enough?
University College London, UK.ORCID-id: 0000-0001-5843-4854
University College London, UK.
Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0001-9454-0793
Scuola Superiore Sant'Anna, Italy.
2020 (Engelska)Ingår i: LAK20: THE TENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE, Association for Computing Machinery (ACM), 2020, s. 270-275Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2020. s. 270-275
Nyckelord [en]
Multimodal learning analytics, physical learning analytics, collaborative problem-solving, decision trees, video analytics
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:mau:diva-18667DOI: 10.1145/3375462.3375484ISI: 000558753800036Scopus ID: 2-s2.0-85082397681OAI: oai:DiVA.org:mau-18667DiVA, id: diva2:1476732
Konferens
Tenth International Conference on Learning Analytics & Knowledge, March 2020
Tillgänglig från: 2020-10-15 Skapad: 2020-10-15 Senast uppdaterad: 2024-02-05Bibliografiskt granskad

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Spikol, Daniel

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Totalt: 83 träffar
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