Learning analytics in massively multi‐user virtual environments and courses

Lee, M.J.W.; Kirschner, Paul A.; Kester, Liesbeth

doi:10.1111/jcal.12139

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been a growing interest in exploiting digital traces that learners leave behind while interacting with educational systems through artificial intelligence (AI), big data (Daniel, 2015), and learning analytics (Siemens, 2013) innovations. These innovations have been designed for multiple educational purposes such as adapting instruction, personalizing feedback, provoking reflection, or generating deeper understanding of learning processes (Lee et al, 2016; Viberg et al, 2018). While important achievements have been obtained in the last decades by mining data collected through online learning activities (e.g., clickstreams and keystrokes; Li et al, 2016; Mousavinasab et al, 2018), learning is ultimately a complex, multimodal process that involves linguistic, gestural, visual, and physical interaction of learners with educational systems, learning artefacts, learning space, peers, and educators (Kress, 2001; Oviatt et al, 2017; Ritella & Hakkarainen, 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Mangaroska

Martínez-Maldonado

Vesin

et al. 2021

Computer Assisted Learning

View full text Add to dashboard Cite

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.

show abstract