A methodology for assessing the effectiveness of serious games and for inferring player learning outcomes (original) (raw)
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Lessons learned applying learning analytics to assess serious games
Computers in Human Behavior, 2019
Lessons learned applying Learning Analytics to assess Serious Games Serious Games have already proved their advantages in different educational environments. Combining them with Game Learning Analytics can further improve the life-cycle of serious games, by informing decisions that shorten development time and reduce development iterations while improving their impact, therefore fostering their adoption. Game Learning Analytics is an evidence-based methodology based on in-game user interaction data, and can provide insight about the game-based educational experience promoting aspects such as a better assessment of the learning process. In this article, we review our experiences and results applying Game Learning Analytics for serious games in three different scenarios: (1) validating and deploying a game to raise awareness about cyberbullying, (2) validating the design of a game to improve independent living of users with intellectual disabilities and (3) improving the evaluation of a game on first aid techniques. These experiences show different uses of game learning analytics in the context of serious games to improve their design, evaluation and deployment processes. Building up from these experiences, we discuss the results obtained and provide lessons learnt from these different applications, to provide an approach that can be generalized to improve the design and application of a wide range of serious games in different educational settings.
2007
What do students really learn as they play videogames for learning? Are they learning the content presented to them, or merely how to play the game? Educators want serious games that “inform, monitor, assess and appraise” students throughout the games and scientific evidence verifying the process. Likewise, policy-makers often require rigorous, large-scale empirical studies to help them determine if new technology, such as the serious games, could be effective in practice.
Systematizing game learning analytics for serious games
– Applying games in education provides multiple benefits clearly visible in entertainment games: their engaging, goal-oriented nature encourages students to improve while they play. Educational games, also known as Serious Games (SGs) are video games designed with a main purpose other than pure entertainment; their main purpose may be to teach, to change an attitude or behavior, or to create awareness of a certain issue. As educators and game developers, the validity and effectiveness of these games towards their defined educational purposes needs to be both measurable and measured. Fortunately, the highly interactive nature of games makes the application of Learning Analytics (LA) perfect to capture students' interaction data with the purpose of better understanding or improving the learning process. However, there is a lack of widely adopted standards to communicate information between games and their tracking modules. Game Learning Analytics (GLA) combines the educational goals of LA with technologies that are commonplace in Game Analytics (GA), and also suffers from a lack of standards adoption that would facilitate its use across different SGs. In this paper, we describe two key steps towards the systematization of GLA: 1), the use of a newly-proposed standard tracking model to exchange information between the SG and the analytics platform, allowing reusable tracker components to be developed for each game engine or development platform; and 2), the use of standardized analysis and visualization assets to provide general but useful information for any SG that sends its data in the aforementioned format. These analysis and visualizations can be further customized and adapted for particular games when needed. We examine the use of this complete standard model in the GLA system currently under development for use in two EU H2020 SG projects.
Game Learning Analytics, Facilitating the Use of Serious Games in the Class
IEEE Revista Iberoamericana de Tecnologias del Aprendizaje
Serious games are still complex to deploy in classrooms for average teachers. Game Learning Analytics can help teachers to apply serious games, using data from students' in-game interactions to provide learning information. Many teachers do not see games as tools to improve their classes, particularly due to perceived loss of control when using games; so it is essential to retain their benefits while avoiding most of the deployment complexity. In this paper, we describe our experience using Game Learning Analytics to encourage the application and deployment of Serious Games in class as learning tools.
Applications Of Learning Analytics To Assess Serious Games (Extended Abstract)
2018
We summarize our experiences regarding three applications of Learning Analytics (LA) for Serious<br> Games (SGs) with different purposes: Validate and deploy games in schools. The SG Conectado has been designed to address social problems (bullying and cyberbullying). Validate game design when information cannot be directly gathered from users. The SG Downtown was designed for improving independent life of users with Intellectual Disabilities (ID) who struggle with communication issues. Improve evaluation and deployment of games. The SG First Aid Game was already validated and data mining models were applied to predict knowledge after playing. All three games have been tested with target users in actual classrooms, as described in the following<br> section. Results and implications of the use of analytics in those three scenarios are later explained.
Analysis of Serious Games based Learning Requirements using Feedback and Traces of Users
Proceedings of the 10th International Conference on Computer Supported Education
Identify the games that best meet the needs and expectations of teachers and objectives of their courses remains a necessity about the integration of serious games among active teaching methods. Indeed, several serious games have developed in recent years, and it is often difficult for a teacher, not a computer scientist in particular, to find a game that meets these specific needs. Our aim is to develop models and tools enabling the teacher to find serious games adapted to his needs, considering user feedback and their traces of interaction with the game. To this end, we have explored the evaluation methods of serious games as well as methods of extracting knowledge from traces and texts. In this paper, we present our method of knowledge extraction of educational objectives. Thus, our proposal is assisting and supporting teachers/trainers to choose serious games and easily integrate them into their learning processes and devices.
Implications of Learning Analytics for Serious Game Design
2014 IEEE 14th International Conference on Advanced Learning Technologies, 2014
This paper addresses the implications of combining learning analytics and serious games for improving game quality, monitoring and assessment of player behavior, gaming performance, game progression, learning goals achievement, and user's appreciation. We introduce two modes of serious games analytics: in-game (real time) analytics, and post-game (off-line) analytics. We also explain the GLEANER framework for in-game analytics and describe a practical example for offline analytics. We conclude with a brief outlook on future work, highlighting opportunities and challenges towards a solid uptake of SGs in authentic educational and training settings.
2017
The engaging and goal-oriented nature of serious games has been proven to increase student motivation. Games also allow learning assessment in a non-intrusive fashion. To increase adoption of serious games, their full lifecycle, including design, development, validation, deployment and iterative refinement must be made as simple and transparent as possible. Currently serious games impact analysis and validation is done on a case-by-case basis. In this paper, we describe a generic architecture that integrates a game authoring tool, uAdventure, with a standards-based Game Learning Analytics framework, providing a holistic approach to bring together development, validation, and analytics, that allows a systematic analysis and validation of serious games impact. This architecture allows game developers, teachers and students access to different analyses with minimal setup; and improves game development and evaluation by supporting an evidence-based approach to assess both games and lear...
Multi-Level Game Learning Analytics for Serious Games
2018 10th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games), 2018
Serious games are usually used or deployed in an educational setting as an isolated or individual activity, disconnected from other curricular activities. However, to really increase the adoption of serious games in different educational scenarios, the combination and integration of games into the educational flow should be simplified. We envision Serious Games as new type of educational activity that can be combined as parts of other games (e.g. minigames integrated in larger games), integrated into other online activities, or even mixed with both game and non-game activities. In addition, if we want to make the most from serious games, a learning analytics system must be in place to harvest and analyze interactions, providing metrics and insights to instructors regarding the gameplay sessions. Moreover, if a course-level learning analytics strategy is designed, it must be aligned with the game learning analytics. This approach requires communication between games and educational activities used during the educational experience. From a game learning analytics standpoint, gaining insights from these integrated experiences introduces new requirements within potentially complex multi-level or hierarchical activities. Moreover, the analysis required to generate these metrics should be both efficient and provide insight in an understandable way and for different stakeholders. This paper describes an approach to multilevel game learning analytics from the perspectives of data model, implementation architecture, and result visualization in teacheroriented dashboards.