Exploring Missing Behaviors with Region-Level Interaction Network Coverage
We have used a complex network model of student-tutor interactions to derive high-level approaches to problem solving. We also have used interaction networks to evaluate between-group differences in student approaches, as well as for automatically producing both next-step and high-level hints. Students do not visit vertices within the networks uniformly; students from different experimental groups are expected to have different patterns of network exploration. In this work we explore the possibility of using frequency estimation to uncover locations in the network with differing amounts of student-saturation. Identification of these regions can be used to locate specific problem approaches and strategies that would be most improved by additional student-data, as well as provide a measure of confidence when comparing across networks or between groups.
Unable to display preview. Download preview PDF.
- 1.Bellman, R.: A markovian decision process. Technical report, DTIC Document (1957)Google Scholar
- 2.Eagle, M., Barnes, T.: Exploring differences in problem solving with data-driven approach maps. In: Proceedings of the Seventh International Conference on Educational Data Mining (2014)Google Scholar
- 3.Eagle, M., Hicks, D., III, P., Barnes, T.: Exploring networks of problem-solving interactions. In: Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (LAK 15) (2015)Google Scholar
- 5.Murray, T.: Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education (IJAIED) 10, 98–129 (1999)Google Scholar
- 6.Stamper, J., Eagle, M., Barnes, T., Croy, M.: Experimental evaluation of automatic hint generation for a logic tutor. International Journal of Artificial Intelligence in Education (IJAIED) 22(1), 3–18 (2013)Google Scholar