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Tue, 4 Feb 2014 00:04:26 -0500
Chris Quintana <[log in to unmask]>
"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Chris Quintana <[log in to unmask]>
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[Apologies for cross-posting.....]

Reminder - Deadline to Apply for the Doctoral Consortium at LAK'14 is this
Friday (Feb 7)

The Doctoral Consortium will be held on March 25, 2014 in Indianapolis, IN
immediately prior to the LAK'14 conference.
Deadline to Apply: ***February 7th, 2014*** through the online system ( )

CFP: Doctoral Consortium at the Fourth Conference on Learning Analytics and
Knowledge (LAK'14)
The LAK Doctoral Consortium is a day-long workshop designed to support
emerging scholars in learning analytics by helping them develop productive
approaches to studying the intersection of theory, big data, and practice.
The event will bring together advanced Ph.D. students from a variety of
disciplines working on topics related to Learning Analytics who are
grappling with their dissertation research and a mentor panel of prominent
researchers in the field (particularly across the disciplinary areas of
learning sciences, data sciences, and human-computer interaction) help to
support participation in the analytics community by a cohort of young
scholars. Doctoral Consortium participants will be given the opportunity to
present, discuss, and receive feedback on their research in an
interdisciplinary and international atmosphere as well as be exposed to a
wide range of different analytic approaches, methods, and tools for
acquiring data about learners and their learning activities. Special
attention will be paid to considering the "big picture" of the field and in
understanding how individual work, often driven by a specific disciplinary
outlook, may impact and intersect with larger learning analytics issues.

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
- Develop a supportive, multidisciplinary community of learning analytics
- Foster a spirit of collaborative research across countries, institutions
and disciplinary background
- Contribute to the conference experience of participating students through
interaction with other doctoral consortium participants and faculty mentors

The intention of this doctoral consortium is to support and inspire
advanced Ph.D. students during their ongoing research efforts. Therefore,
applicants should already be working on a specific research project but
should not have completed their degree nor officially submitted their
thesis prior to the doctoral consortium (March 2014).

Costs / Funding
Students attending the Doctoral Consortium should be registered for the
general LAK conference, but there is no additional registration fee the
Consortium itself. All meals on the workshop day will be provided and a
stipend will be given to accepted students that covers the cost conference
registration plus an additional amount to defray the cost of travel.

We thank the Society for Learning Analytics Research (SoLAR) for providing
financial support for this event.

In order to apply to the Doctoral Consortium, student applicants will need
to submit the following information:

1. A 2-4 page summary of your research that includes the following:
-- A 150 word abstract
-- Brief background of 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
-- An outline of the current knowledge of the problem domain and state of
existing solutions
-- A discussion of how the Ph.D. project's suggested solution is different,
new, or better as compared to existing approaches to the problem
-- A sketch of the research methodology and identification of core methods
/ techniques used
-- Current status of the work and any preliminary results achieved so far
(Note that the 4 page limit includes all tables, figures, references etc.)

2. In a separate sheet include:
-- A statement of the particular issues/problems in your dissertation,
research areas that you want to discuss, types of feedback that might be
particularly useful
-- A list of members of the LAK'14 Program Committee who you think would be
able to provide a particularly useful perspective or feedback on your work
-- If you have financial support from your supervisor or external sources
to attend the conference and wish to decline the consortium stipend to
provide greater support for students without external funds

3. A letter of recommendation from your dissertation supervisor/advisor. It
should include an assessment of the current status of your work, and an
expected date for dissertation completion.

Upload the 2-4 page summary as the primary conference submission document
("Paper") and the recommendation and issues document together (as a zip or
merged PDF file) for the additional supporting file ("Attachment"). Please
also provide keywords in the indicated field so that we can assign
appropriate reviewers to your submission.

Reviewing Process
Applications will be reviewed with the goal of selecting participants who
reflect individual excellence, interesting projects, and strong motivation
while at the same time contributing to the workshop objectives and the
aggregate diversity of the cohort. Proposals will be reviewed by the
Doctoral Consortium co-chairs and additional Program Committee members as
needed. Participants will be selected on the basis of the academic quality
of their proposal; relevance and potential contribution to the Learning
Analytics field; recommendation from the dissertation supervisor/advisor;
and their anticipated contribution to the workshop goals. We specifically
seek applicants whose work is conceptually motivated and uses analytics to
provide direction for understanding and supporting learning processes and

Submission deadline: February 7, 2014
Notification of acceptance: February 21, 2014
Doctoral Consortium: March 25, 2014

At the Conference
Prior to the workshop, participating students and mentors will be asked to
provide a written formative review of a small number of the student
projects. These will both provide formal feedback and serve as a grounding
for discussions during the workshop.

At the workshop, the format will be centered around research presentations
and feedback sessions from the mentors and other workshop participants.
Each participant will be allocated a timeslot to make a presentation (in
whole or small group format) summarizing their current research foci and
challenges, including theoretical frameworks, discipline, goals,
methodologies, actual or potential studies, and the research plan. The
presentation will be followed by a Q&A/discussion period with the mentors
and other student participants. These sessions will be interspersed with
break-out group discussions on special topics and career-related issues
depending on the group's interests.

There will also be a group luncheon during the workshop and an informal
evening event following the workshop. These will provide the students,
mentors, and other invited researchers an opportunity to network in a more
informal setting and engage in additional discussion about professional
issues with regards to the students' future academic and professional

In order to foster networking between the student participants themselves
and also with leading learning analytics researchers, participating
students will also present their work in a poster session during the

Accepted students should prepare:

1. A very brief (60 sec max) introduction of themselves and the PhD project
for the opening session
2. A 10-15 minute presentation of the PhD project to be used for
introducing discussion about the project and its main issues
3. A poster about their in-progress Ph.D. work for one of the LAK 2014
poster sessions

Organizing Committee
- Chris Quintana, University of Michigan, MI, USA (Contact:
[log in to unmask])
- Alyssa Friend Wise, Simon Fraser University, Surrey, Canada (Contact:
[log in to unmask])
- Katherine Maillet, Institut Mines-TÚlÚcom, TÚlÚcom Ecole de Management,
France (Contact:[log in to unmask])
- Stephanie Teasley, University of Michigan, MI, USA (Contact:
[log in to unmask])

Chris Quintana, Ph.D.
Associate Professor, Learning Technologies
School of Education
University of Michigan
610 E. University, 4122 SEB
Ann Arbor, MI, 48109
[log in to unmask]
Ph.: (734) 615-0287

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