LAK17: doctoral consortium

Screen Shot 2017-03-31 at 09.19.17A very busy week in Vancouver at the LAK17 (learning analytics and knowledge) conference kicked off with the all-day doctoral consortium on 14 March (funded by SoLAR and the NSF). I joined Bodong Chen and Ani Aghababyan as an organiser this year and we enjoyed working with the ten talented doctoral students from across the world who gained a place in the consortium.

  1. Alexander Whitelock-Wainwright: Students’ intentions to use technology in their learning: The effects of internal and external conditions
  2. Alisa Acosta: The design of learning analytics to support a knowledge community and inquiry approach to secondary science
  3. Daniele Di Mitri: Digital learning shadow: digital projection, state estimation and cognitive inference for the learning self
  4. Danielle Hagood: Learning analytics in non-cognitive domains
  5. Justian Knobbout: Designing a learning analytics capabilities model
  6. Leif Nelson: The purpose of higher education in the discourse of learning analytics
  7. Quan Nguyen: Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics
  8. Stijn Van Laer: Design guidelines for blended learning environments to support self-regulation: event sequence analysis for investigating learners’ self-regulatory behavior
  9. Tracie Farrell Frey: Seeking relevance: affordances of learning analytics for self-regulated learning
  10. Ye Xiong: Write-and-learn: promoting meaningful learning through concept map-based formative feedback on writing assignments

The intention of the doctoral consortium was to support and inspire doctoral students in their ongoing research efforts. The objectives were to:

  • Provide a setting for mutual feedback on participants’ current research and guidance on future research directions from a mentor panel
  • Create a forum for engaging in dialogue aimed at building capacity in the field with respect to current issues in learning analytics ranging from methods of gathering analytics, interpreting analytics with respect to learning issues, considering ethical issues, relaying the meaning of analytics to impact teaching and learning, etc.
  • Develop a supportive, multidisciplinary community of learning analytics scholars
  • Foster a spirit of collaborative research across countries, institutions and disciplinary background
  • Enhance participating students’ conference experience by connecting participants to other LAK attendees
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