Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks

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, r...

Full description

Saved in:
Bibliographic Details
Published inJournal of educational data mining Vol. 11; no. 1; pp. 80 - 100
Main Authors Cui, Yang, Chu, Man-Wai, Chen, Fu
Format Journal Article
LanguageEnglish
Published International Educational Data Mining 01.06.2019
Subjects
Accuracy
Bayesian Statistics
Classification
Computer Games
Data Analysis
Educational Games
Elementary School Students
Feedback (Response)
Formative Evaluation
Grade 5
Mastery Learning
Reliability
Science Education
Science Process Skills
Scoring
Student Evaluation
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2157-2100
2157-2100
DOI:10.5281/zenodo.3554751