Incorporating Latent Factors Into Knowledge Tracing To Predict Individual Differences In Learning

An effective tutor—human or electronic—must determine what a student does and does not know. Inferring a student’s knowledge state is challenging because behavioral observations (e.g., correct vs. incorrect problem solution) provide only weak evidence. Two classes of models have been proposed to address the challenge. Latent-factor models employ a collaborative filtering approach in which data from a population of students solving a population of problems is used to predict the performance of an individual student on a specific problem. Knowledgetracing models exploit a student’s sequence of problem-solving attempts to determine the point at which a skill is mastered. Although these two approaches are complementary, only preliminary, informal steps have been taken to integrate them. We propose a principled synthesis of the two approaches that predicts student performance based on a theory of individual differences among students and among problems, as well as a theory of the temporal dynamics of learning. We present promising results using data from two intelligent tutoring systems.