An emotional student model for game-based learning

Students’ performance and motivation are influenced by their emotions. Game-based learning (GBL) environments comprise elements that facilitate learning and the creation of an emotional connection with students. GBL environments include Intelligent Tutoring Systems (ITSs) to ensure personalized learning. ITSs reason about students’ needs and characteristics (student modeling) to provide suitable instruction (tutor modeling). The authors’ research is focused on the design and implementation of an emotional student model for GBL environments based on the Control-Value Theory of achievement emotions by Pekrun et al. (2007). The model reasons about answers to questions in game dialogues and contextual variables related to student behavior acquired through students’ interaction with PlayPhysics. The authors’ model is implemented using Dynamic Bayesian Networks (DBNs), which are derived using Probabilistic Relational Models (PRMs), machine learning techniques, and statistical methods. This work compares an earlier approach that uses Multinomial Logistic Regression (MLR) and cross-tabulation for learning the structure and conditional probability tables with an approach that employs Necessary Path Condition and Expectation Maximization algorithms. Results showed that the latter approach is more effective at classifying the control of outcome-prospective emotions. Future work will focus on applying this approach to classification of activity and outcome-retrospective emotions.