Game Learning Analytics, Facilitating the Use of Serious Games in the Class

Calvo-Morata, Antonio; Alonso‐Fernández, Cristina; Martínez‐Ortiz, Iván; Fernández‐Manjón, Baltasar

doi:10.1109/rita.2019.2952296

Cited by 9 publications

(8 citation statements)

References 21 publications

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“…GLA has shown positive impacts on learning, skill enhancement and engagements in knowledge acquisition/content understanding, and affective and motivational outcomes (Boyle et al, 2012(Boyle et al, , 2016Connolly et al, 2012). GLA facilitates serious game usage in the classroom setting (Calvo-Morata et al, 2019).…”

Section: Game Learning Analytics Frameworkmentioning

confidence: 99%

Personalised Learning through Context-Based Adaptation in the Serious Games with Gating Mechanism

et al. 2023

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When the traditional "one size fits all" approach is used in designing educational games, the game context is usually arranged in a fixed sequence. However, the designated content may not effectively support the diversity of players. The player's ability and characteristics should be considered and supported with an appropriate learning context embedded in the game to facilitate personalised experiences. Adapting game scenarios to a player's characteristics can boost motivation and ultimately improve learning outcomes. This research applies a context-aware design approach and the Learner-Centered Design approach to establish a personalised adaptation framework for designing educational serious games and enhancing personalised knowledge delivery. The proposed framework decouples the game logic implementation and adaptation mechanism. It dynamically adapts the designed game objects and activities to personal learning objectives, learning levels and learning progress to achieve a non-linear learning sequence. Through synchronous real-time xAPI message exchange mechanisms, system components and learning content adaptation are enabled. The adaptation aims to fit personal learning objectives and provide a non-linear learning sequence in a game environment. The framework provides students with personalised learning experiences. A game named GhostCoder is implemented and used to evaluate the framework. Based on the externalised adaptive mechanism, the game content is adapted to the player's performance by adjusting the difficulty of the learning content within the game. Testing of the game in the lab environment has been performed. At the next stage, an evaluation will be conducted with the target groups of students.

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Section: Game Learning Analytics Frameworkmentioning

confidence: 99%

Personalised Learning through Context-Based Adaptation in the Serious Games with Gating Mechanism

et al. 2023

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“…Los cuestionarios fueron validados de modo previo. En caso de que se careciera de un instrumento de validación para el cuestionario, este se elaboró ad-hoc (Alonso-Fernández et al, 2020;Alonso-Fernández et al, 2020c;Calvo-Morata et al, 2019;Melo et al, 2019;Rotaru et al, 2018).…”

Section: Tendencias De Validaciónunclassified

“…En determinadas situaciones, la recolección de los datos de interacción demostró ser indispensable cuando el aprendizaje obtenido de la interacción del jugador con el juego serio no pudo medirse con pre-y postest (Alonso-Fernández et al, 2019). Si bien es cierto que el uso de pre-y postest es una herramienta efectiva para la recolección de datos, la aplicación de xAPI-SG representa una alternativa para recolectar datos, ya que recurre a estadísticas agregadas de alto nivel sobre el rendimiento del jugador y el equipo (Alonso-Fernández et al, 2019, 2020a, 2020bCalvo-Morata et al, 2019;Cano, Fernández-Manjón & García-Tejedor, 2018;Charleer et al, 2018).…”

Section: Tendencias De Validaciónunclassified

“…Respecto a la interacción de los estudiantes con el juego, esta sucedió de forma individual (Alonso-Fernández et al, 2019, 2020a, 2020bCalvo-Morata et al, 2019Cano, Fernández-Manjón & García-Tejedor, 2018;Cloude et al, 2020;Niemelä et al, 2020;Peddycord-Liu et al, 2017;Rotaru et al, 2018) o colaborativa. Los resultados son diferentes, no necesariamente superiores unos frente a otros; mientras que la interacción individual permitió un mayor avance en la realización de los juegos y en la participación, la interacción en binas demostró mayor exploración del juego (Ruipérez-Valiente & Kim, 2020).…”

Section: Tendencias De Implementaciónunclassified

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Tendencias de game learning analytics: una revisión sistemática de la literatura especializada

2022

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Este artículo presenta una revisión sistemática de la literatura especializada con el método de metasíntesis para conocer las tendencias teóricas y empíricas que pueden encontrarse en la literatura científica acerca de game learning analytics. La búsqueda se realizó en 17 bases de datos y se obtuvieron 153 resultados. Tras la aplicación de ciertos criterios de exclusión, se admitieron 17 artículos de investigación científica para el análisis. La información fue clasificada en tendencias de diseño, validación e implementación. Los hallazgos en diseño sugieren una tendencia a simular ambientes reales con el objetivo de validar, no solo el juego serio, sino también el aprendizaje obtenido aplicando mediciones pre- y postest. Se observó una implementación variada entre fines educativos, de capacitación o apoyo a personas con discapacidad. Asimismo, se emplearon juegos prediseñados y juegos de autor con interacciones individuales.

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“…Visual analyzes that aggregate the learning analysis data during the game session, based on control dashboards allow an overview of the participating student groups (Calvo-Morata et al, 2019). The use of an interactive dashboard, which allows to obtain filtering by student, chosen paths, progress, actions in the game, answers, scores, times for completion, among others, is of great relevance, so that educators can also view detailed information on learning during a session of a serious game (Calvo-Morata et al, 2019). It can be observed (Figure 4) the evolution in the studies of learning analytics interactive dashboards in serious games in 2016 and a significant growth until 2019.…”

Section: Data Visualizationmentioning

confidence: 99%

Use of Learning Analytics Interactive Dashboards in Serious Games

Scheneider

Lemos

2020

Int J Innov Educ Res

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The learning analytics in serious games, corresponds to a subject in increasing demand in the educational field. In this context, there is a need to study how data visualizations found in the literature are adopted in learning analytics in serious games. This paper presents a Systematic Literature Review (SLR) on how the evolution of studies associated with the use of learning analytics interactive dashboards in serious games is processed, seeking to investigate the characteristics of using dashboards for viewing educational data. A bibliometric analysis was carried out in which 75 relevant studies were selected from the Scopus, Web of Science, and IEEExplore databases. From the data analysis, it was observed that in the current literature there is a reduced number of studies containing the main actors in the learning process, as follows: teachers/instructors, students/participants, game developers/designers, and managers/researchers. In the vast majority of investigated studies, data visualization algorithms are used, where the main focus takes into account only actors, such as teachers/instructors and students/participants.

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Game Learning Analytics, Facilitating the Use of Serious Games in the Class

Cited by 9 publications

References 21 publications

Personalised Learning through Context-Based Adaptation in the Serious Games with Gating Mechanism

Personalised Learning through Context-Based Adaptation in the Serious Games with Gating Mechanism

Tendencias de game learning analytics: una revisión sistemática de la literatura especializada

Use of Learning Analytics Interactive Dashboards in Serious Games

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