You can consult here the full call for papers for the Learning Analytics track at TEEM 2021 conference. Deadline: 4th July.

Last editions of the learning analytics track, one of the conclusions of the track was that variety (of topics, techniques, data sources, contexts, users) was necessary for the field to expand and advance, at the peril of a fragmentation that now seems inevitable.

The fragmented landscape of learning analytics has its own perks, though. Nearly eight years after the first Learning Analytics and Knowledge Conference, we have now a clearer view of all the different aspects, technologies, algorithms and scope of application of learning analytics. In a sense, we are now witnessing the end of the beginning of learning analytics.

This eight edition of the track aims to embrace variety and invites researchers across the world to original submissions that evaluate the role of learning analytics from educational, social and technical perspectives, with a critical view. As in previous years, we look forward to receiving valuable contributions that reconcile educational and technical approaches, but we welcome both quantitative and qualitative work that reflect a broad range of applications of learning analytics across different instructional contexts.

Submissions should present advances in theory and/or apply novel techniques for data collection, processing and visualization in learning analytics. Submissions covering ethical aspects and policy-making in learning analytics are also welcome. All submissions will be subject to peer-review.


  • Educational research and learning analytics.
  • Learning analytics and instructional design.
  • Competence-based learning analytics.
  • Learning analytics and self-regulated learning.
  • Interoperability and standards for learning analytics.
  • Learning analytics in team-based education.
  • Mobile learning analytics. Multimodal learning analytics.
  • Learning analytics in virtual worlds.
  • Discourse and sentiment analytics.
  • Academic analytics.
  • Data sources for learning analytics.
  • New approaches and methods in learning analytics.
  • Learning analytics for personal learning environments (PLEs)
  • Success stories and case studies.
  • Theoretical advances in learning analytics.
  • Replication and cross-validation of existing research.
  • Ethical aspects of learning analytics.
  • Learning analytics and policy-making.