Applications of data science to game learning analytics data: A systematic literature review

Data science techniques, nowadays widespread across all fields, can also be applied to the wealth of information derived from student interactions with serious games. Use of data science techniques can greatly improve the evaluation of games, and allow both teachers and institutions to make evidence...

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Veröffentlicht in:Computers and education Jg. 141; S. 103612
Hauptverfasser: Alonso-Fernández, Cristina, Calvo-Morata, Antonio, Freire, Manuel, Martínez-Ortiz, Iván, Fernández-Manjón, Baltasar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.11.2019
Schlagworte:
Data science applications in education
Evaluation methodologies
Games
Teaching/learning strategies
Data science applications in education
Evaluation methodologies
Teaching/learning strategies
Games
ISSN:0360-1315, 1873-782X
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Zusammenfassung:Data science techniques, nowadays widespread across all fields, can also be applied to the wealth of information derived from student interactions with serious games. Use of data science techniques can greatly improve the evaluation of games, and allow both teachers and institutions to make evidence-based decisions. This can increase both teacher and institutional confidence regarding the use of serious games in formal education, greatly raising their attractiveness. This paper presents a systematic literature review on how authors have applied data science techniques on game analytics data and learning analytics data from serious games to determine: (1) the purposes for which data science has been applied to game learning analytics data, (2) which algorithms or analysis techniques are commonly used, (3) which stakeholders have been chosen to benefit from this information and (4) which results and conclusions have been drawn from these applications. Based on the categories established after the mapping and the findings of the review, we discuss the limitations of the studies analyzed and propose recommendations for future research in this field. •Applications of data science to game learning analytics data from serious games.•Categorization of purposes, data science techniques, stakeholders and results.•Most studies focus on assessment and behaviors, applying classical techniques.•Larger samples should be considered and more complex techniques.•Need of specific Game Learning Analytics with standards and open data sets.
ISSN:0360-1315
1873-782X
DOI:10.1016/j.compedu.2019.103612