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

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Bibliographic Details
Title: Analyzing Student Process Data in Game-Based Assessments with Bayesian Knowledge Tracing and Dynamic Bayesian Networks
Authors: Cui, Ying, Chu, Man-Wai, Chen, Fu
Publisher Information: Zenodo
Publication Year: 2019
Collection: Zenodo
Subject Terms: game-based assessment, Evidence-Centered Design, Bayesian Knowledge Tracing, Dynamic Bayesian Networks, formative feedback, process data analysis
Description: 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. ; The file is in PDF format. If your computer does not recognize it, simply download the file and then open it with your browser.
Document Type: article in journal/newspaper
Language: English
Relation: https://jedm.educationaldatamining.org/index.php/JEDM/article/view/397; https://zenodo.org/records/3554752; oai:zenodo.org:3554752; https://doi.org/10.5281/zenodo.3554752
DOI: 10.5281/zenodo.3554752
Availability: https://doi.org/10.5281/zenodo.3554752
https://zenodo.org/records/3554752
Rights: Creative Commons Attribution Non Commercial No Derivatives 4.0 International ; cc-by-nc-nd-4.0 ; https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
Accession Number: edsbas.C02510F0
Database: BASE
Description
Abstract: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. ; The file is in PDF format. If your computer does not recognize it, simply download the file and then open it with your browser.
DOI:10.5281/zenodo.3554752