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.

Submission Types

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. 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:

• Long research talk.  30 minutes (including Q&A)
  Must include a clearly explained substantial conceptual, technical or empirical contribution. We welcome thoughts and findings that stem from learning analytics project implementations.
• 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.
• Demo.
  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.
• Poster.
  Research in progress, ideas for projects, etc.

Suggested 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 commercial suppliers are welcomed, reviewers will not accept sales pitches.

Submission Instructions

Submissions should consist of: author names, affiliations, and an abstract of a maximum of 500 words.  Authors should also indicate if they prefer a long talk, short talk, or lightning talk format. Accepted abstracts will be displayed on our website, but are not published, and thus we accept and encourage work that is going to be presented at other venues. 

Submissions are accepted through EasyChair.

Program and Organizing Committee

• Josine Verhagen, Kidaptive
• Leann Pereira, University of San Francisco
• Anita Bowles, Rosetta Stone
• Marti Hearst, University of California Berkeley
• Jose Gonzalez-Brenes, Chegg

Dates

• Submission deadline: December 15th, 2017. We are evaluating submissions on an ongoing basis.
• Event: February 24th, 2018 from 11am-5pm. 
  We will meet at University of California, Berkeley (details TBA). We will host an optional social gathering afterwards. 

Program

TBA

Contact

All questions about submissions should be emailed to baylan2018 @ easychair.org