文章基本信息

标题：Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks
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作者：Ying Cui ; Man-Wai Chu ; Fu Chen 等
期刊名称：Journal of Educational Data Mining
电子版ISSN：2157-2100
出版年度：2019
卷号：11
期号：1
页码：80-100
出版社：International EDM Society
摘要：Digital game-based assessments generate student process data that is much more difficult to analyze than traditional assessments. The formative nature of game-based assessments permits students, through applying and practicing the targeted knowledge and skills during gameplay, to gain experiences, receive immediate feedback, and as a result, improve their skill mastery. Both Bayesian Knowledge Tracing and Dynamic Bayesian Networks are capable of updating students’ mastery levels based on their observed responses during the assessment. This paper investigates the use of these two models for analyzing student response process data from an interactive game-based assessment, Raging Skies. The game measures a set of knowledge and skill-based learner outcomes listed in a Canadian Provincial Grade 5 science program-of-study under the Weather Watch unit. To evaluate and compare the performance of Bayesian Knowledge Tracing and Dynamic Bayesian Networks, the classification consistency and accuracy are examined.
关键词：game-based assessment;Evidence-Centered Design;Bayesian Knowledge Tracing;Dynamic Bayesian Networks;formative feedback;process data analysis
其他关键词：game-based assessment;Evidence-Centered Design;Bayesian Knowledge Tracing;Dynamic Bayesian Networks;formative feedback;process data analysis