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.
Weitere Kapitel dieses Buchs durch Wischen aufrufen
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
- Exploring Missing Behaviors with Region-Level Interaction Network Coverage