Validating game-based learning assessment of students’ computational thinking practices using Bayesian networks and machine-learning based detectors

Bibliographic Data

ID21743904
AuthorsElizabeth Rowe (0000-0002-3185-2308, Terumo (Germany)), Russell G Almond (0000-0002-8876-9337, Florida State University, corresponding author), Ma Victoria Almeda (0000-0001-5172-6467, Terumo (Germany))
Year2026
Volume58
Issue1
Pages14-31
Publication date2026-01-02
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Research on Technology in Education (JOURNAL)
Journal identifiersISSN: 1539-1523 • E-ISSN: 1945-0818
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15391523.2025.2590018
OpenAlexW7130698270
LanguageEN
Citations received1
References cited20

Zoombinis is an award-winning digital game aligned with four computational thinking practices—problem decomposition, pattern recognition, abstraction, and algorithm design. The study manipulates two levels of scoring: Evidence identification (EI)—identifying outcomes from a single puzzle-level,—and evidence accumulation (EA)—summarizing observed outcomes across puzzles and levels. For three puzzles, machine learning detectors provide information about players’ computational thinking practices. For the remaining 45 puzzle-levels, only basic information (e.g. degree of player success) is available. These are combined with two EA algorithms: the average of detector outputs, and a Bayesian network. This study uses data from 716 students complete pre- and post-assessments. The correlation among the measures of computational thinking was positive yet modest, suggesting that the measures focus on different aspects of computational thinking

Bayesian network · Bayesian probability · Bayesian statistics · Computational model · Computational Thinking · Detector · Evaluation methods · Educational Games and Gamification · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics

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