Posted on

[edm-announce] CfP: EDM 2021 Workshop – Big Data, Research Challenges, Science Convergence in Educational Data Science: The 2nd Workshop of the Learner Data Institute

  • From: Stephen Fancsali <sfancsali@xxxxxxxxx>
  • To: edm-announce@xxxxxxxxxxxxx
  • Date: Mon, 12 Apr 2021 16:34:08 -0400

[on behalf of myself & Vasile Rus]

Call for Papers:

*Big Data, Research Challenges, & Science Convergence in Educational Data
Science: The Second Workshop of the Learner Data Institute*

A Half-Day Virtual Workshop @ EDM 2021

June 29, 2021 — online (time TBD)

Workshop Information URL:
https://sites.google.com/view/learnerdatainstitute/ldiedm

SUBMISSION LINK: https://easychair.org/conferences/?conf=ldiedm2021

*Workshop Summary*
The Second Workshop of the Learner Data Institute (LDI) builds on the
success of last year’s virtual workshop and seeks to bring together
researchers working across disciplines on data-intensive research of
interest to the educational data science and educational data mining
communities. In addition to welcoming work describing mature,
data-intensive or “big data” research and emerging work-in-progress that
spans traditional academic disciplines, the workshop organizers welcome
case studies of interdisciplinary research programs and projects, including
case studies of learning engineering efforts pursued by universities,
learning technology providers, and others (both successful and as lessons
learned), as well as position papers on important challenges for
researchers harnessing “big data” and crossing disciplinary boundaries as
they do so.

We convene researchers and developers from diverse fields who seek to
“harness the data revolution” in educational data science and “grow
convergence research,” aligning with (at least) two of the U.S. National
Science Foundation’s “10 Big Ideas” for emerging research and development
opportunities. “Convergence builds and supports creative partnerships and
the creative thinking needed to address complex problems” (“NSF’s 10 Big
Ideas: Growing Convergence Research”), and we expect that bringing together
highly experienced researchers, as well as students and early-career
researchers, will stimulate substantial growth and interest in
state-of-the-art, data-intensive, transdisciplinary or “convergent”
approaches to solving vexing societal problems related to education. We
also seek to explore the big data and learning engineering frameworks that
will enable convergent solutions.

Please see the workshop website for more information:
https://sites.google.com/view/learnerdatainstitute/ldiedm

Reference
U.S. National Science Foundation. “NSF’s 10 Big Ideas: Growing Convergence
Research” https://www.nsf.gov/news/special_reports/big_ideas/convergent.jsp

*Questions & Areas of Interest*
– How can we use massive and diverse datasets generated by adaptive
instructional systems (AISs) to address core questions and challenges in
learning science and engineering?
– Are learners, teachers, and learning science researchers successfully
interacting with cyber-learning technologies?
– How can we engage end-users of AISs and related stakeholders (e.g., K-12
teachers) to participate in educational data science research and
development in a way that builds local capacity to meaningfully leverage
learning data?
– What are some critical challenges with respect to scaling the development
of AISs across many domains and millions of learners?
– What are the current limitations of AISs and adaptive components of
instructional systems?
– Which aspects of learning are best handled by humans and which ones by
cyber-learning technologies (and how do we enhance the interaction of the
two)?
– How can data from student and teacher interactions with cyber-learning
technologies, in and outside the classroom, be collected in ways consistent
with best practices—e.g., with respect to data fidelity, security,
reliability, privacy, human subject research protocols, school policies,
parental consent, HIPAA, FERPA?
– Methodology, infrastructure, and workflows for “big data” and
data-intensive educational research
– Inter/multi/trans-disciplinary approaches to data-intensive educational
research
– Case studies of successful & unsuccessful efforts to practically harness
insights from large datasets in settings where learning takes place (e.g.,
case studies of “learning engineering” efforts)
– Emerging challenges for researchers working across disciplines with large
datasets
– Use-cases, workflows, and case studies (illustrating the need) for
(possibilities of extensions to existing) data infrastructure for research
leveraging learner data, including data repositories, (open source)
software and statistical libraries, innovative use of cloud computing
resources, etc.

*Submission Types*
The Workshop Committee solicits three types of submissions:

– Full papers (up to 8 pages): describing mature research, extensive
descriptions of data-intensive workflows, and learning engineering efforts
suitable for a 20 minute presentation.
– Short papers (4-6 pages): suitable for a 10 minute presentation,
especially appropriate for work-in-progress and shorter case studies.
– Position papers (up to 3 pages) describing approaches to convergence
research (see below), emerging challenges (e.g., that the LDI might take on
collaboratively with authors with future funding), “wishlist(s)” for
transformative learning applications, resources like data repositories, and
other infrastructure that would fuel innovative work (e.g., that LDI could
collaboratively develop with future funding): suitable for a 5 minute
presentation.

We hope that all papers, but especially position papers, will spark
conversations and interactions to drive future collaborations between LDI
researchers and workshop participants. The Workshop Committee also intends
to invite 1-2 speakers to deliver talks during the workshop.

*Important Dates*
– Submission Deadline: June 4, 2021
– Acceptance Notification: June 15, 2021
– Workshop: June 29, 2021

*Submission Logistics*
Use the EDM paper templates.

– Microsoft Word template:
https://educationaldatamining.org/edm2020/wp-content/uploads/sites/4/2019/09/edm_word_template2020.doc

– LaTeX template:
https://educationaldatamining.org/edm2020/wp-content/uploads/sites/4/2019/09/edm_submission2020.zip

– Submission System (EasyChair):
https://easychair.org/conferences/?conf=ldiedm2021

For more information about the workshop and LDI, see the workshop website:
https://sites.google.com/view/learnerdatainstitute/ldiedm

*Workshop & Review Committee*
Vasile Rus, Ph.D., University of Memphis (Co-Chair)
Stephen E. Fancsali, Ph.D., Carnegie Learning, Inc. (Co-Chair)
Dale Bowman, Ph.D., University of Memphis
Jody Cockroft, AA, BS, CCRP, University of Memphis
Art Graesser, Ph.D., University of Memphis
Andrew Hampton, Ph.D., University of Memphis
Philip I. Pavlik Jr., Ph.D., University of Memphis
Chip Morrison, Ed.D., University of Memphis
Steven Ritter, Ph.D., Carnegie Learning, Inc.
Deepak Venugopal, Ph.D., University of Memphis
[Ad-hoc reviewers will be drawn from the group of LDI contributors and
broader community as necessary.]

Ezoicreport this ad

Other related posts:

  • » [edm-announce] CfP: EDM 2021 Workshop – Big Data, Research Challenges, & Science Convergence in Educational Data Science: The 2nd Workshop of the Learner Data Institute – Stephen Fancsali