Antonio Calvo-Morata

El avance tecnológico ha incrementado la importancia de adquirir habilidades en pensamiento computacional y programación, viéndose reflejado en los currículos escolares. Sin embargo, en muchos casos los profesores no son expertos en la materia y hay pocas herramientas validadas que faciliten su labor. Consideramos que los videojuegos educativos son herramientas versátiles en el aprendizaje de conceptos complejos y pueden aplicarse para aprender programación. En este artículo presentamos Articoding, un juego serio para desarrollar el pensamiento computacional a través de la programación. Junto con el diseño del videojuego, se describe la metodología incremental aplicada basada en experiencias en el aula en diferentes etapas de su desarrollo. En total participaron 469 estudiantes de secundaria y 36 estudiantes universitarios de educación. Estas experiencias han servido para estudiar la aplicabilidad del juego en el aula, y su aceptación por parte de estudiantes y futuros profesores. Los cuestionarios y las analíticas recogidas mientras juegan los estudiantes muestran resultados prometedores sobre su uso en clase. Estos datos también identifican aspectos a mejorar (p.ej., la dificultad de algunos niveles). Con estos resultados estamos elaborando una nueva versión de Articoding, incluyendo analíticas de aprendizaje mejoradas para su uso como herramienta de apoyo docente en la enseñanza de programación.

Serious games are powerful interactive environments that provide more authentic experiences for learning or training different skills. However, developing effective serious games is complex, and a more systematic approach is needed to create better evidence-based games. Learning analytics—based on the analysis of collected in-game user interactions—can support game development and the players’ learning process, providing assessment information to teachers, students, and other stakeholders. However, empirical studies applying and demonstrating the use of learning analytics in the context of serious games in real environments remain scarce. In this paper, we study the application of learning analytics throughout the whole lifecycle of a serious game, in order to assess the game’s design and players’ learning using a serious game that introduces basic programming concepts through a visual programming language. The game was played by N = 134 high school students in two 50-min sessions. During the game sessions, all player interactions were collected, including the time spent solving levels, their programming solutions, and the number of replays. We analyzed these interaction traces to gain insights that can facilitate teachers’ use of serious games in their lessons and assessments, as well as guide developers in making possible improvements to the game. Among these insights, knowing which tasks students struggle with is critical for both teachers and game developers, and can also reveal game design issues. Among the results obtained through analysis of the interaction data, we found differences between boys and girls when playing. Girls play in a more reflexive way and, in terms of acceptance of the game, a higher percentage of girls had neutral opinions. We also found the most repeated errors, the level each player reached, and how long it took them to reach those levels. These data will help to make further improvements to the game’s design, resulting in a more effective educational tool in the future. The process and results of this study can guide other researchers when applying learning analytics to evaluate and improve the educational design of serious games, as well as supporting teachers—both during and after the game activity—in applying an evidence-based assessment of the players based on the collected learning analytics.

Serious games are videogames whose purpose goes beyond mere entertainment. However, serious games use in mainstream education is still limited. The development of serious games is an expensive and complex process that requires the participation of different experts (e.g. domain, educators, graphic artists, and programmers). We consider that new generative AI techniques can help in the prototyping of serious games by reducing and automating some of the processes involved. There is increasing evidence that generative AI techniques such as ChatGPT or GitHub Copilot can increase the productivity of writing or coding tasks respectively. In our case, both prototyping and teaching serious games are complex because of the number and diversity of tasks involved and we are currently investigating whether AI techniques can be used to improve and simplify the process. For example, ChatGPT could support the process of creating the game narrative, and other systems such as Stable Diffusion could ease the creation of some of the graphics resources (e.g., for creating more cohesive and coherent backgrounds). Automating some of the costlier processes of game prototyping can contribute to creating better products by allowing the playtesting of different options for more effective games. As the field of generative AI is in continuous change, this paper presents a working methodology to simplify the development of serious games, that has been instantiated with concrete tools. This working methodology has been piloted effectively by one student from a Master of Design for the development of a serious game and will be tested in a serious games development course. In the article we also explore how these AI techniques can be combined with a game authoring environment such as uAdventure to systematize the development of serious games. The use of Generative AI offers great potential for improving the development of serious games and needs to be further researched alongside its applications for game-based learning education.

Serious games have several advantages for learning over more traditional activities. One of those advantages is that being highly interactive, they provide a firsthand simulated experience to the users. Serious games can also generate interaction data that can be used to assess learning, both for evaluation and to provide timely feedback for learners or even adapt aspects of the game on the fly. However, this potential is currently far from realized because doing so requires significant investment and expertise as compared to more traditional educational activities. Additionally, game learning analytics continue to be a costly and fragile process. We propose a combination of tools that greatly reduces the associated costs of building serious games with meaningful analytics simplifying the validation of those games and their deployment in real settings. We consider that this can be a key step to building predictive assessment avoiding the need for disruptive external assessment thus bootstrapping serious games to recognize learning through analytics.

Game learning analytics (GLA) comprise the collection, analysis, and visualization of player interactions with serious games. The information gathered from these analytics can help us improve serious games and better understand player actions and strategies, as well as improve player assessment. However, the application of analytics is a complex and costly process that is not yet generalized in serious games. Using a standard data format to collect player interactions is essential: the standardization allows us to simplify and systematize every step in developing tools and processes compatible with multiple games. In this paper, we explore a combination of 1) an exploratory visualization tool that analyzes player interactions in the game and provides an overview of their actions, and 2) an assessment approach, based on the collection of interaction data for player assessment. We describe some of the different opportunities offered by analytics in game-based learning, the relevance of systematizing the process by using standards and game-independent analyses and visualizations, and the different techniques (visualizations, data mining models) that can be applied to yield meaningful information to better understand learners’ actions and results in serious games.

Bioelectric signals can improve assessment in videogames by helping to better understand user experience, evaluate attention, or study the cognitive and physical effects of games. Use of signals is therefore relevant to better evaluate and understand the impact and effects of videogames on players, and specially so in the field of serious games, such as educational or training games, to gain insights into the learning processes; or in games for health to better evaluate non-conscious effects on the player’s body. We examine how biological signals (bioelectric + eye-tracking) are being used and collected in the field of video games, including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the results reported from their use. As a result of this systematic literature review, 81 articles have been analyzed, finding that electrocardiograms and encephalograms are the most frequently used signals. The main use of these bioelectrical signals is to evaluate player engagement, level of difficulty, and stress during the gameplay. But there are also examples where signals are used to detect health problems, or as evidence to compare educational games with other learning activities. This review informs researchers interested in better understanding the benefits and limitations of biological signals for video games, providing an overview of studies conducted in recent years and the associated devices described in those studies. Limitations in this field include signal noise issues as well as the amount of time required to calibrate the devices during experiments, adding to the complexity of user testing. It is necessary to work on tools that facilitate experiments with large groups of users in parallel as well as to work on open software and low-cost devices that allow the emergence of a greater number of studies in this field, given for example their potential in the field of educational games to better understand the learning processes of users.