Applications of data science to game learning analytics data: A systematic literature review. (November 2019)

• Record Type: Journal Article
• Authors: Alonso-Fernández, Cristina; Calvo-Morata, Antonio; Freire, Manuel; Martínez-Ortiz, Iván; Fernández-Manjón, Baltasar
• Is Part Of: Computers & education. Volume 141(2019)
• Publication Date: 2019-11
• Subjects: Data science applications in education -- Evaluation methodologies -- Games -- Teaching/learning strategies; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2019.103612
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: 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. Highlights: 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.