Challenges and opportunities of multimodal data in human learning: the computer science students' perspective

Katerina Mangaroska, Roberto Martinez-Maldonado, Boban Vesin, Dragan Gašević

Research output: Contribution to journalArticleResearchpeer-review

32 Citations (Scopus)


Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non-technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student-facing MLA innovations.

Original languageEnglish
Pages (from-to)1030-1047
Number of pages18
JournalJournal of Computer Assisted Learning
Issue number4
Publication statusPublished - Aug 2021


  • ethics
  • higher education
  • human-centred analytics
  • multimodal learning analytics
  • pervasive surveillance
  • privacy
  • thematic analysis

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