Early Prediction of Student Knowledge in Game-Based Learning with Distributed Representations of Assessment Questions

Autor/inn/en	Emerson, Andrew; Min, Wookhee; Azevedo, Roger; Lester, James
Titel	Early Prediction of Student Knowledge in Game-Based Learning with Distributed Representations of Assessment Questions
Quelle	In: British Journal of Educational Technology, 54 (2023) 1, S.40-57 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-1013
DOI	10.1111/bjet.13281
Schlagwörter	Game Based Learning; Natural Language Processing; Prediction; Student Evaluation; Individualized Instruction; Scaffolding (Teaching Technique); Multiple Choice Tests; Student Characteristics; Models; Undergraduate Students; Microbiology + Suchen Sie Ihr Suchwort? Natürliche Sprache; Vorhersage; Schulnote; Studentische Bewertung; Individualisierender Unterricht; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Analogiemodell; Mikrobiologie
Abstract	Game-based learning environments hold significant promise for facilitating learning experiences that are both effective and engaging. To support individualised learning and support proactive scaffolding when students are struggling, game-based learning environments should be able to accurately predict student knowledge at early points in students' gameplay. Student knowledge is traditionally assessed prior to and after each student interacts with the learning environment with conventional methods, such as multiple choice content knowledge assessments. While previous student modelling approaches have leveraged machine learning to automatically infer students' knowledge, there is limited work that incorporates the fine-grained content from each question in these types of tests into student models that predict student performance at early junctures in gameplay episodes. This work investigates a predictive student modelling approach that leverages the natural language text of the post-gameplay content knowledge questions and the text of the possible answer choices for early prediction of fine-grained individual student performance in game-based learning environments. With data from a study involving 66 undergraduate students from a large public university interacting with a game-based learning environment for microbiology, Crystal Island, we investigate the accuracy and early prediction capacity of student models that use a combination of gameplay features extracted from student log files as well as distributed representations of post-test content assessment questions. The results demonstrate that by incorporating knowledge about assessment questions, early prediction models are able to outperform competing baselines that only use student game trace data with no question-related information. Furthermore, this approach achieves high generalisation, including predicting the performance of students on unseen questions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01