Discovering Individual and Collaborative Problem-Solving Modes with Hidden Markov Models

Supporting students during learning tasks is the main goal of intelligent tutoring systems, and the most effective systems can adapt to students based on a model of their current state of knowledge or their problem-solving actions. Most tutoring systems focus on individual students, but there is growing interest in supporting student pairs. However, modeling student pairs involves considerations that may differ from individual students. This paper reports on hidden Markov models (HMMs) of student interactions within a visual programming environment. We compare HMMs for individual students to those of student pairs and examine the different approaches the students take. The resulting models suggest that there are some important differences across both conditions. There is potential for using these models to predict problem-solving modes and support adaptive tutoring for collaboration in problem-solving domains.

• Type: Journal
• Year: 2015
• Where: AIED
• Authors: Fernando J. Rodríguez, Kristy Elizabeth Boyer
• Keywords: AIED 2015, Artificial Intelligence