Dynamic Bayesian Network Modeling of Game-Based Diagnostic Assessments

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. Drawing from and advancing methods in dynamic Bayesian networks, cognitive diagnostic modeling, and analysis of process data, a Bayesian approach to model construction, calibration, and use in facilitating inferences about students on the fly is described, and implemented in the context of an educational video game.

数字游戏提供了一个极具吸引力的环境，用于评估学生的专业技能，包括在诊断性环境中对技能和误区的掌握。本文提出了一种动态贝叶斯网络建模方法，用于从教育视频游戏中观察学生的学习表现。借鉴并发展动态贝叶斯网络、认知诊断建模以及过程数据分析的方法，本文描述了一种贝叶斯方法，用于模型构建、校准以及在促进对学生在实时中进行推断中的应用，并已在教育视频游戏环境中得到实施。