CrossMMLA in practice: Collecting, annotating and analyzing multimodal data across spaces

Michail Giannakos, Daniel Spikol, Inge Molenaar, Daniele Di Mitri, Kshitij Sharma, Xavier Ochoa, Rawad Hammad

Research output: Contribution to journalConference articlepeer-review

Abstract

Learning is a complex process that is associated with many aspects of interaction and cognition (e.g., hard mental operations, cognitive friction etc.) and that can take across diverse contexts (online, classrooms, labs, maker spaces, etc.). The complexity of this process and its environments means that it is likely that no single data modality can paint a complete picture of the learning experience, requiring multiple data streams from different sources and times to complement each other. The need to understand and improve learning that occurs in ever increasingly open, distributed, subject-specific and ubiquitous scenarios, require the development of multimodal and multisystem learning analytics. Following the tradition of CrossMMLA workshop series, the proposed workshop aims to serve as a place to learn about the latest advances in the design, implementation and adoption of systems that take into account the different modalities of human learning and the diverse settings in which it takes place. Apart from the necessary interchange of ideas, it is also the objective of this workshop to develop critical discussion, debate and co-development of ideas for advancing the state-of-the-art in CrossMMLA.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume2610
StatePublished - 2020
Event2020 CrossMMLA in Practice: Collecting, Annotating and Analyzing Multimodal Data Across Spaces, CrossMMLA 2020 - Virtual, Online
Duration: Mar 24 2020 → …

Keywords

  • Learning spaces
  • Multimodal learning analytics
  • Sensor data

ASJC Scopus subject areas

  • Computer Science(all)

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