关键词：概率模型;游戏设计;虚拟环境
摘 要：Computer-based learning environments offer the potential for innovative assessments of student knowledge and personalized instruction for learners. However, there are a number of challenges to realizing this potential. Many psychological models are not specific enough to directly deploy in instructional systems, and computational challenges can arise when considering the implications of a particular theory of learning. While learners’ interactions with virtual environments encode significant information about their understanding, existing statistical tools are insufficient for interpreting these interactions. This research develops computational models of teaching and learning and combines these models with machine learning algorithms to interpret learners’ actions and customize instruction based on these interpretations. This approach results in frameworks that can be adapted to a variety of educational domains, with the frameworks clearly separating components that can be shared across tasks and components that are customized based on the educational content. Using this approach, this dissertation addresses three major questions: (1) How can one diagnose learners’ knowledge from their behavior in games and virtual laboratories? (2) How can one predict whether a game will be diagnostic of learners’ knowledge? and (3) How can one customize instruction in a computer-based tutor based on a model of learning in a domain?