Graph-based Educational Data Mining

With the growing popularity of MOOCs and computer-aided learning systems, as well as the growth of social networks in education, we have begun to collect increasingly large amounts of educational graph data. This graph data includes complex user-system interaction logs, student-produced graphical representations, and conceptual hierarchies that large amounts of graph data have. There is abundant pedagogical information beneath these graph datasets. As a result, graph data mining techniques such as graph grammar induction, path analysis, and prerequisite relationship prediction has become increasingly important. Also, graphical model techniques (e.g. Hidden Markov Models or probabilistic graphical models) has become more and more important to analyze educational data. While educational graph data and data analysis based on graphical models has grown increasingly common, it’s necessary to build a strong community for educational graph researchers. This workshop will provide such a forum for interested researchers to discuss ongoing work, share common graph mining problems, and identify technique challenges. Researchers are encouraged to discuss prior analyses of graph data and educational data analyses based on graphical models. We also welcome discussions of in-progress work from researchers seeking to identify suitable sources of data or appropriate analytical tools. 1. PRIOR WORKSHOPS So far, we have successfully held two international workshops on Graph-based Educational Data-Mining. The first one was held in London, co-located with EDM 2014. It featured 12 publications of which 6 were full-papers, the remainder short papers. Having roughly 25 full-day attendees and additional drop-ins, it led to a number of individual connections between researchers and the formation of an e-mail list for group discussion. The second one was co-located with EDM 2015 in Spain. 10 authors presented their published work including 4 full papers and 6 short papers there. 2. OVERVIEW AND RELEVANCE 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 studentproduced 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.