3554752
doi
10.5281/zenodo.3554752
oai:zenodo.org:3554752
Chu, Man-Wai
University of Calgary
Chen, Fu
University of Alberta
Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks
Cui, Ying
University of Alberta
url:https://jedm.educationaldatamining.org/index.php/JEDM/article/view/397
info:eu-repo/semantics/openAccess
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
game-based assessment
Evidence-Centered Design
Bayesian Knowledge Tracing
Dynamic Bayesian Networks
formative feedback
process data analysis
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.
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Zenodo
2019-06-24
info:eu-repo/semantics/article
3554751
1.0.0
1579539091.653178
886523
md5:ef73638605c301067fcc7f784684e36e
https://zenodo.org/records/3554752/files/55437827
public
https://jedm.educationaldatamining.org/index.php/JEDM/article/view/397
Is cited by
url
10.5281/zenodo.3554751
isVersionOf
doi
Journal of Educational Data Mining
11
1
80-100
2019-06-24