Graph-based data mining and educational data analysis based on graphical models have become emerging disciplines in EDM. Large-scale graph data, such as social network data, complex user-system interaction logs, student-produced graphical representations, and conceptual hierarchies, carries multiple levels of pedagogical information. Exploring such data can help to answer a range of critical questions such as:
For social network data from MOOCs, online forums, and user-system interaction logs:
For computer-aided learning (writing, programming, etc.)
Graphical model techniques, such as Bayesian Network, Markov Random Field, and Conditional Random Field, have been widely used in EDM for student modeling, decision making, and knowledge tracing. Utilizing these approaches can help to :
Researches related to questions can help us to better understand students’ learning status, and improve the teaching effectiveness and student learning. Our goal in this workshop is to bring together researchers with special interest in graph-based data analysis to 1) discuss state of the art tools and technologies, 2) identify common problems and challenges, and 3) foster a community of researchers for further collaboration. We will consider the submission of full and short papers as well as posters and demonstrations covering a range of graphics topics that include, but are not limited to:
We particularly welcome submissions of in-progress work both from students and researchers with problems who are seeking appropriate analytical tools, and developers of graph analysis tools who are seeking new challenges.
Sincerely: Dr. Collin Lynch, Dr. Tiffany Barnes, Linting Xue & Niki Gitinabard
We will invite submissions of full papers which describe mature work. We will also accept short papers describing in-progress work or student projects, and poster/demo submissions for those presenting available data, tools, and methods. This last category is particularly targeted at researchers who have data or methods available and are seeking to identify potential collaborators.
Long papers should be from describe mature work and be from 7-8 pages long.
Short papers should be 5 pages long and describe planned research; existing datasets that would be amenable to graph analysis; or mining techniques that are available for wider use.
Poster and demo submissions should be 3 pages and should present an overview of the planned content. Posters and demos should be focused on available datasets and tools and be designed to foster discussion.
Papers should be submitted in the EDM 2017 latex and word formats: http://educationaldatamining.org/EDM2017/submission/
Submissions should anonymize the author's names for review and may be made via EasyChair at: https://easychair.org/conferences/?conf=gedm2017
We will organize this workshop as a half-day mini-conference with time set aside for paper presentations, large-group discussion, and individual networking. We will open the workshop with a summary of prior meetings. We will spend the workshop on presentations with a short discussion session.
Dr. Collin Lynch
Assistant Professor of Computer Science NC State University
Email: cflynch@ncsu.edu
Dr. Tiffany Barnes
Associate Professor of Computer Science at NC State University
Email: tmbarnes@ncsu.edu
Linting Xue
Ph.D student of Computer Science at NC State University
Email: lxue3@ncsu.edu
Niki Gitinabard
Ph.D student of Computer Science at NC State University
Email: ngitina@ncsu.edu