Learning Analytics in Serious Games as Predictors of Linguistic Competence in Students at Risk

• Published in: Technology, knowledge and learning Vol. 29; no. 3; pp. 1551 - 1577
• Main Authors: Sánchez Castro, Susana, Pascual Sevillano, Mª Ángeles, Fombona Cadavieco, Javier
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-09-2024; Springer Nature B.V
• Subjects: At risk populations; At risk students; Creativity and Arts Education; Design optimization; Education; Educational Technology; Elementary education; Games; Impact analysis; Learning; Learning analytics; Learning and Instruction; Linguistic competence; Linguistics; Original Research; Performance evaluation; Performance prediction; Primary Education; Science Education; Students; Verbal learning; Disadvantaged children; Serious games; Educational games; Educational technology
• ISSN: 2211-1662; 2211-1670
• DOI: 10.1007/s10758-024-09726-5

The planned systematized design of the use of serious games in the classroom is presented as a strategy to optimize learning. In this framework, Learning Analytics represents stealth assessment and follow-up method, and a way to personalize such games by simplifying their application for teachers. The aim of this research was to analyze the impact of the use of serious games on improving linguistic competence in socio-educationally disadvantaged students, with a proposal for a planned systematized intervention. We use two specific games to improve linguistic competence and its learning analytics to achieve the proposed goal. This study carried out was pre-experimental, with pretest and posttest, and the sample consisted of 75 students at 4 primary education centers in Spain (36 boys, 39 girls) aged 9–12 (M = 10.6; SD = 0.7) at risk due to socioeconomic conditions in Primary Education. The results show that (a) the serious games integrated into the curriculum and adjusted to the learning objectives can facilitate the development and acquisition of linguistic competence in students with socio-educational disadvantages; (b) these students can match their peers in performance and competencies with appropriate systematic intervention; (c) the level acquired in a key competence can be evaluated and specific needs identified in students with academic difficulties using learning analytics; (d) learning analytics can contribute to predicting student performance in academic subjects through the scores collected in the analysis of learning integrated into serious games. These findings contribute to filling research gaps in these four aspects.