A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow MMLA

Marcelo Worsley*, Roberto Martinez-Maldonado, Cynthia D’angelo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12 commitments that we believe are critical for creating effective MMLA innovations. Moreover, as MMLA grows in use, it is important to articulate a set of core commitments that can help guide both MMLA researchers and the broader learning analytics community. The commitments that we describe are deeply rooted in the origins of MMLA and also reflect the ways that MMLA has evolved over the past 10 years. We organize the 12 commitments in terms of (i) data collection, (ii) analysis and inference, and (iii) feedback and data dissemination and argue why these commitments are important for conducting ethical, high-quality MMLA research. Furthermore, in using the language of commitments, we emphasize opportunities for MMLA research to align with established qualitative research methodologies and important concerns from critical studies.

Original languageEnglish (US)
Pages (from-to)10-27
Number of pages18
JournalJournal of Learning Analytics
Issue number3
StatePublished - Dec 15 2021


  • Artificial intelligence
  • Data collection
  • Data dissemination
  • Data mining
  • Ethics
  • Multimodal
  • Sensor data

ASJC Scopus subject areas

  • Education
  • Computer Science Applications


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