The goal of this meeting is to promote exchange of high-quality research that uses insights from psychology, data science, cognitive science, education science, and other scientific disciplines to answer educational research questions.
The theme of this year’s conference is: Using data to understand how and why learning happens.
Submission deadline: January 14
However, abstracts are accepted on a rolling basis and we expect slots to fill up quickly!
We solicit abstract submissions reporting on research in the broadly defined topic of learning analytics. This includes technical work that applies data science or other quantitative methods to improve education, as well as interventions, methodologies, tools or technology that are intended to improve learning outcomes.
Abstracts will be assessed by the program committee on the relevance to the field of Learning Analytics, and will be accepted on an ongoing basis. We aim to provide a platform for discussion on how to gain insight of best educational practices; and this includes interventions that are well designed, but showed no positive effect on learning. Accepted work will be presented as one of:
Short research talk. 15 minutes (including Q&A)
Can address on-going work, which may include a briefly described theoretical underpinning, an initial proposal or rationale for a technical solution, and preliminary results in an experience.
Lightning talk. 5 minute talk
We welcome thought-provoking work that has not yet reached a level of completion that would warrant a longer presentation.
A live demonstration is a great opportunity to communicate ideas and concepts in a powerful way that a regular presentation cannot. Show aspects of learning analytics in an interactive hands-on form. Feel free to include a link to a video on your abstract.
Research in progress, ideas for projects, etc.
Topics that warrant further conversations within small-groups. These topics will be used to guide lunchtime discussions.
List of Topics
Theoretical topics: Cognitive science models about education, data science methods applied to learning, novel theories about learning
Lessons learned: After going through the learning analytics implementation process, share insights that have surfaced that affect the completion of the project
Innovative new tools/techniques: Share newly developed tools or approaches to learning analytics that have been implemented at an institution.
Application of standards: A project making use of data/analytics standards and illustrating the benefits of such an approach.
Collaboration and sharing: How are groups of institutions/practitioners partnering to solve shared problems in the learning analytics space?
While submissions from for profit companies are welcomed, reviewers will not accept sales pitches.
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