GEDM 2017 - Workshop on Graph-based Educational Data Mining (GEDM)

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:
What social networks can foster or hinder learning?
Do users of online learning tools behave as we expect them to?
How does the interaction graph evolve over time?
What data we can use to define relationship graphs?
What path(s) do high-performing students take through online materials?
What is the impact of teacher-interaction on students’ observed behavior?
Can we identify students who are particularly helpful in a course?
For computer-aided learning (writing, programming, etc.)
What substructures are commonly found in student-produced diagrams?
Can we use prior student data to identify students’ solution plan, if any?
Can we automatically induce empirically-valid graph rules from prior student data and use induced graph rules to support automated grading systems?
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 :
Learn students’ behavioral patterns.
Predict students’ behaviors and learning outcomes.
Induce pedagogical strategies for computer-aided learning systems.
Identify the difficult level of knowledge components in the intelligent tutoring systems.
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:
Social network data
Graphical solution representations
Graphical behavior models
Graph-based log analysis
Large network datasets
Novel graph-based machine learning methods
Novel graph analysis techniques
Relevant analytical tools and standard problems
Issues with graph models
Tools and technologies for graph grammar (pattern) recognition
Tools and technologies for automatic concept hierarchy extraction
Computer-aided learning system development involved with graphical representations
Use of graphical models in educational data
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