Doctoral Consortium

14 March 2017, Simon Fraser University, Vancouver, BC, Canada

Invitation from the Chairs

The LAK Doctoral Consortium is a one-day workshop to support emerging scholars in learning analytics by helping them develop productive approaches to studying the intersection of theory, data, and practice in the learning sciences, data sciences, and human-centered computing.

The event will bring together doctoral students from a variety of disciplines working on topics related to learning analytics who are grappling with their dissertation research. The consortium chairs serve as a mentor panel to provide feedback. Doctoral Consortium participants will be given opportunities to present, discuss, and receive feedback on their research in an interdisciplinary and supportive atmosphere. They will also be exposed to a wide range of different analytic approaches, methods, and tools for acquiring data about learners and their learning activities.


The specific objectives of the Doctoral Consortium are 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

The intention of this Doctoral Consortium is to support and inspire doctoral students during their ongoing research efforts. Ideally, participants will have developed and even defended a proposal, but their work will be at a sufficiently early stage that they can still make adjustments based on feedback received at the consortium (they should not have completed their doctorate, nor officially submitted their thesis prior to the doctoral consortium).

Important Dates

  • 10 November 2016: Deadline for submissions (no extensions).
  • 3 January 2017: Notification of acceptance.
  • 14 March 2017: Doctoral consortium event.

Proposal Format & Submission Process

To apply to the Doctoral Consortium, student applicants should submit the following documents as one single PDF file to

  1. Up to 5-page (including all tables, figures and references, except 1 additional page for appendices) summary of your research, in the ACM conference format that includes the following:
  • A 150-word abstract
  • Brief background to the project and identification of the significant problem(s) in the field the project addresses
  • Goals of the research and a clear formulation of the research question(s)
  • An outline of the current knowledge of the problem domain and state of existing solutions
  • A discussion of how the doctoral project’s suggested solution is different, new, or better than existing approaches to the problem
  • A sketch of the research methodology and identification of core methods/techniques
  • Current status of the work and results achieved so far (e.g literature review, submitted or accepted papers, prototypes designed or built, experiments carried out, etc.)
  1. A supplementary page with additional information addressed to the Doctoral Consortium committee to help us to help you. This should include:
  • A statement of the particular issues/problems that you want to discuss, and/or types of feedback that might be particularly useful
  • Names of LAK researchers from whom you would like to receive feedback on your work. We are happy to contact them and ask if they would be willing to drop into the consortium for your session, visit your poster, or just meet up at LAK.
  1. A letter of recommendation from your supervisor/advisor with an assessment of the current status of your work, a brief summary of how attending the Doctoral Consortium is like to benefit your work, and an expected date for dissertation completion.

Reviewing Process

Doctoral Consortium co-chairs will review applications, with the help of additional Program Committee members where necessary. Participants will be selected on the basis of:

  • Academic quality of their proposal
  • Relevance and potential contribution to the learning analytics field
  • Potential for the student and the dissertation work to benefit from participation in the doctoral consortium
  • Support of the dissertation advisor for participation and its potential benefit.

Students who have not participated in a LAK Doctoral Consortium previously will have preference during allocation. Our aim is to bring together a diverse cohort of emerging scholars of learning analytics.

During & After the Conference

The Doctoral Consortium will take place the day before the main conference. As in previous years, the format interleaves research presentations and small-group discussion, so students have opportunities for in-depth conversation about their work. A plenary discussion on career development concludes the day. All participants are also encouraged to join the Doctoral Consortium dinner with the mentor team to continue the conversation.

To help disseminate their work to all conference attendees, students will also present their work in the main LAK Poster session, and will be entered for the LAK Best Poster Award. If you are accepted as a Doctoral Consortium participant, you will submit your poster in advance to us for comments.

Financial Support

As in previous years, for this DC event we will strive to provide financial support to cover costs related to the LAK conference registration, accommodation for two nights, and a potential contribution towards other travel related expenses. Some funds are already confirmed for SoLAR student members to cover a flat amount of their expenses.


Ani Aghababyan, McGraw-Hill Education, USA

Bodong Chen, University of Minnesota, USA

Rebecca Ferguson, The Open University, UK

Joris Klerkx, University of Leuven, Belgium