As a game design educator diving deep into my innovation plan for implementing game-based learning strategies, I've been exploring a wealth of resources on measuring technology initiatives in education. I've curated a list of five key resources that I've found particularly valuable for my literature review and measurement strategy.
One comprehensive review by Plass et al. (2015) provides a theoretical framework for game-based learning, discussing how games can be designed to support learning and engagement. It offers insights into cognitive, motivational, affective, and sociocultural foundations of game-based learning. I'll use this to establish a solid theoretical grounding for my innovation plan and to identify potential areas for measurement, such as cognitive load, motivation, and social interaction in learning games.
Another valuable resource is the work of Arnab et al. (2015), which introduces the Learning Mechanics-Game Mechanics (LM-GM) model, a framework for analyzing the relationship between learning and game mechanics in serious games. I plan to use this model to design and evaluate the educational games in my innovation plan, ensuring a strong connection between learning objectives and gameplay elements.
For best practices in measuring the effectiveness of digital game-based learning, I've found the meta-analysis by All et al. (2016) particularly insightful. It highlights the importance of using multiple assessment methods and considering various outcome measures. I'll incorporate these best practices into my measurement strategy, ensuring a comprehensive evaluation of my game-based learning initiative.
To gain a comprehensive overview of the empirical evidence on the impacts and outcomes of serious games and computer games used for learning, I've turned to the updated systematic review by Boyle et al. (2016). This will help me identify established measurement tools and methodologies that have been successful in evaluating game-based learning initiatives.
Lastly, for insights into advanced measurement strategies using big data and machine learning, I've found the systematic review by Alonso-Fernández et al. (2019) extremely valuable. This exploration of how data science techniques can be applied to game learning analytics will inform the development of more sophisticated measurement tools for my innovation plan, potentially incorporating learning analytics into the games themselves.
These resources will form the backbone of my literature review, providing a solid foundation for the measurement of my game-based learning innovation plan. They offer a mix of theoretical frameworks, practical measurement tools, and cutting-edge analytics techniques that will help ensure a comprehensive and effective evaluation strategy.
Comparing my approach to my peers, I notice that while many are focusing on traditional educational metrics, my game-based learning focus allows for more innovative measurement strategies. For instance, the use of in-game analytics and the LM-GM model provides unique insights into the learning process that traditional assessments might miss.
Looking ahead, I see potential for future research in several areas:
1. Long-term impact studies of game-based learning initiatives
2. Cross-cultural comparisons of game-based learning effectiveness
3. Integration of VR/AR technologies into game-based learning measurement
4. Ethical considerations in game learning analytics
As we continue to level up our educational strategies, it's crucial that our measurement techniques evolve alongside our teaching methods. Game-based learning offers exciting new possibilities, not just for engagement and learning, but also for how we understand and evaluate the learning process itself.
What measurement strategies are you considering for your innovation plans? How might game-based approaches inform your evaluation methods, even if you're not directly using games in your teaching? Let's continue this discussion in the comments!
References:
All, A., Nuñez Castellar, E. P., & Van Looy, J. (2016). Assessing the effectiveness of digital game-based learning: Best practices. Computers & Education, 92-93, 90-103. https://doi.org/10.1016/j.compedu.2015.10.007
Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers & Education, 141, Article 103612. https://doi.org/10.1016/j.compedu.2019.103612
Arnab, S., Lim, T., Carvalho, M. B., Bellotti, F., de Freitas, S., Louchart, S., Suttie, N., Berta, R., & De Gloria, A. (2015). Mapping learning and game mechanics for serious games analysis. British Journal of Educational Technology, 46(2), 391-411. https://doi.org/10.1111/bjet.12113
Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Lim, T., Ninaus, M., Ribeiro, C., & Pereira, J. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, 94, 178-192. https://doi.org/10.1016/j.compedu.2015.11.003
Plass, J. L., Homer, B. D., & Kinzer, C. K. (2015). Foundations of game-based learning. Educational Psychologist, 50(4), 258-283. https://doi.org/10.1080/00461520.2015.1122533
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