Scalability, sustainability, and ethicality of Multimodal Learning Analytics

Lixiang Yan, Linxuan Zhao, Dragan Gasevic, Roberto Martinez-Maldonado

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

7 Citations (Scopus)

Abstract

Multimodal Learning Analytics (MMLA) innovations are commonly aimed at supporting learners in physical learning spaces through state-of-the-art sensing technologies and analysis techniques. Although a growing body of MMLA research has demonstrated the potential benefits of sensor-based technologies in education, whether their use can be scalable, sustainable, and ethical remains questionable. Such uncertainty can limit future research and the potential adoption of MMLA by educational stakeholders in authentic learning situations. To address this, we systematically reviewed the methodological, operational, and ethical challenges faced by current MMLA works that can affect the scalability and sustainability of future MMLA innovations. A total of 96 peer-reviewed articles published after 2010 were included. The findings were summarised into three recommendations, including i) improving reporting standards by including sufficient details about sensors, analysis techniques, and the full disclosure of evaluation metrics, ii) fostering interdisciplinary collaborations among experts in learning analytics, software, and hardware engineering to develop affordable sensors and upgrade MMLA innovations that used discontinued technologies, and iii) developing ethical guidelines to address the potential risks of bias, privacy, and equality concerns with using MMLA innovations. Through these future research directions, MMLA can remain relevant and eventually have actual impacts on educational practices.

Original languageEnglish
Title of host publicationLAK 2022 Conference Proceedings
EditorsHassan Khosravi, Abhinava Barthakur
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages13-23
Number of pages11
ISBN (Electronic)9781450395731
DOIs
Publication statusPublished - 2022
EventInternational Conference on Learning Analytics and Knowledge 2022 - Online, United States of America
Duration: 21 Mar 202225 Mar 2022
Conference number: 12th
https://dl.acm.org/doi/proceedings/10.1145/3506860 (Proceedings)
https://www.solaresearch.org/events/lak/lak22/ (Website)

Conference

ConferenceInternational Conference on Learning Analytics and Knowledge 2022
Abbreviated titleLAK 2022
Country/TerritoryUnited States of America
Period21/03/2225/03/22
Internet address

Keywords

  • ethics
  • learning analytics
  • multimodal
  • scalability
  • sensors
  • sustainability

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