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Using multimodal learning analytics to study collaboration on discussion groups

Published: 01 August 2019 Publication History

Abstract

Nowadays, companies and organizations require highly competitive professionals that have the necessary skills to confront new challenges. However, current evaluation techniques do not allow detection of skills that are valuable in the work environment, such as collaboration, teamwork, and effective communication. Multimodal learning analytics is a prominent discipline related to the analysis of several modalities of natural communication (e.g., speech, writing, gestures, sight) during educational processes. The main aim of this work is to develop a computational environment to both analyze and visualize student discussion groups working in a collaborative way to accomplish a task. ReSpeaker devices were used to collect speech data from students, and the collected data were modeled by using influence graphs. Three centrality measures were defined, namely permanence, persistence, and prompting, to measure the activity of each student and the influence exerted between them. As a proof of concept, we carried out a case study made up of 11 groups of undergraduate students that had to solve an engineering problem with everyday materials. Thus, we show that our system allows to find and visualize nontrivial information regarding interrelations between subjects in collaborative working groups; moreover, this information can help to support complex decision-making processes.

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  • (2024)Visualization of Hotspots and Frontiers in Learning Analytics under Big Data Environment — Based on Citespace Knowledge Map AnalysisProceedings of the 2024 8th International Conference on Digital Technology in Education (ICDTE)10.1145/3696230.3696267(221-231)Online publication date: 7-Aug-2024
  • (2023)Rethinking MMLA: Design Considerations for Multimodal Learning Analytics SystemsProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3596186(354-359)Online publication date: 20-Jul-2023
  • (2022)Scalability, Sustainability, and Ethicality of Multimodal Learning AnalyticsLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506862(13-23)Online publication date: 21-Mar-2022
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Published In

cover image Universal Access in the Information Society
Universal Access in the Information Society  Volume 18, Issue 3
Aug 2019
282 pages
ISSN:1615-5289
EISSN:1615-5297
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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 August 2019

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  1. Multimodal learning analytics
  2. Influence graphs
  3. Social networks
  4. Collaboration

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View all
  • (2024)Visualization of Hotspots and Frontiers in Learning Analytics under Big Data Environment — Based on Citespace Knowledge Map AnalysisProceedings of the 2024 8th International Conference on Digital Technology in Education (ICDTE)10.1145/3696230.3696267(221-231)Online publication date: 7-Aug-2024
  • (2023)Rethinking MMLA: Design Considerations for Multimodal Learning Analytics SystemsProceedings of the Tenth ACM Conference on Learning @ Scale10.1145/3573051.3596186(354-359)Online publication date: 20-Jul-2023
  • (2022)Scalability, Sustainability, and Ethicality of Multimodal Learning AnalyticsLAK22: 12th International Learning Analytics and Knowledge Conference10.1145/3506860.3506862(13-23)Online publication date: 21-Mar-2022
  • (2021)Can Analytics of Speaking Time Serve as Indicators of Effective Team Communication and Collaboration?Proceedings of the X Latin American Conference on Human Computer Interaction10.1145/3488392.3488404(1-4)Online publication date: 22-Nov-2021
  • (2020)Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature reviewEducation and Information Technologies10.1007/s10639-020-10229-w25:6(5499-5547)Online publication date: 30-May-2020

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