Relevance of learning analytics to measure and support students' learning in adaptive educational technologies

Maria Bannert, Inge Molenar, Roger Azevedo, Sanna Järvelä, Dragan Gašević

Research output: Contribution to conferencePoster

16 Citations (Scopus)


In this poster, we describe the aim and current activities of the EARLI-Centre for Innovative Research (E-CIR) "Measuring and Supporting Student's Self-Regulated Learning in Adaptive Educational Technologies" which is funded by the European Association for Research on Learning and Instruction (EARLI) from 2015 to 2019. The aim is to develop our understanding of multimodal data that unobtrusively capture cognitive, meta-cognitive, affective and motivational states of learners over time. This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation, and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods including learning analytics to develop more powerful adaptive educational technologies.

Original languageEnglish
Number of pages2
Publication statusPublished - 13 Mar 2017
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2017 - Morris J Wosk Centre for Dialogue, Simon Fraser University, Vancouver, Canada
Duration: 13 Mar 201717 Mar 2017
Conference number: 7th


ConferenceInternational Learning Analytics & Knowledge Conference 2017
Abbreviated titleLAK 2017
Internet address


  • Adaptive Educational Technologies
  • Educational data mining
  • Learning analytics
  • Multimodal data
  • Self-regulated learning

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