Research Gaps for Adaptive and Predictive Computer-Based Tutoring Systems

Researchers continue to enhance individual computerbased training capabilities to support self-directed learning and account for individual differences (e.g., personality or domain competence). Student-centric tutoring approaches recognize that each student’s unique affect, motivation, skills, knowledge, preferences and experiences should influence the content, flow and challenge level of computer-based instruction. In other words, these individual differences should be the basis for adapting instruction to promote learning and predicting the future learning states of the student. This article explores current trends in adaptive and predictive computer-based tutoring methods, identifies gaps and discusses opportunities for future research. The intent of this paper is to introduce concepts discussed in the “adaptive and predictive computer-based tutoring” track of the the Defense and Homeland Security Simulation (DHSS) Workshop 2011.