Principles for designing educational games that integrate content and play and create learning experiences connecting to many areas of learners' lives. Too often educational videogames are narrowly focused on specific learning outcomes dictated by school curricula and fail to engage young learners. This book suggests another approach, offering a guide to designing games that integrates content and play and creates learning experiences that connect to many areas of learners' lives. These games are not gamified workbooks but are embedded in a long-form experience of exploration, discovery, and collaboration that takes into consideration the learning environment. Resonant Games describes twenty essential principles for designing games that offer this kind of deeper learning experience, presenting them in connection with five games or collections of games developed at MIT's educational game research lab, the Education Arcade. Each of the games—which range from Vanished, an alternate reality game for middle schoolers promoting STEM careers, to Ubiquitous Bio, a series of casual mobile games for high school biology students—has a different story, but all spring from these fundamental assumptions: honor the whole learner, as a full human being, not an empty vessel awaiting a fill-up; honor the sociality of learning and play; honor a deep connection between the content and the game; and honor the learning context—most often the public school classroom, but also beyond the classroom. The open access edition of this book was made possible by generous funding from the MIT Libraries and Klopfer's lab.
The purpose of this study is to explore some of the ways in which gameplay data can be analyzed to yield results that feed back into the learning ecosystem. There is a solid research base showing the positive impact that games can have on learning, and useful methods in educational data mining. However, there is still much to be explored in terms of what the results of gameplay data analysis can tell stakeholders and how those results can be used to improve learning. As one step toward addressing this, researchers in this study collected back-end data from high school students as they played an MMOG called The Radix Endeavor.Data from a specific genetics quest in the game were analyzed by using data mining techniques including the classification tree method. These techniques were used to examine the relationship between tool use and quest completion, how use of certain tools may influence content-related game choices, and the multiple pathways available to players in the game. The study identified that in this quest use of the trait examiner tool was most likely to lead to success, though a greater number of trait decoder tool uses could also lead to success, perhaps because in those cases players solving problems about genetic traits at an earlier point. These results also demonstrate the multiple strategies available to Radix players that provide different pathways to quest completion. Given these methods of analysis and quest-specific results, the study applies the findings to suggest ways to validate and refine the game design, and to provide useful feedback to students and teachers. The study suggests ways that analysis of gameplay data can be part of a feedback loop to improve a digital learning experience.
Learning games have great potential to become an integral part of new classrooms of the future. One of the key reported benefits is the capacity to keep students deeply engaged during their learning process. Therefore, it is necessary to develop models that can measure quantitatively how learners are engaging with learning games to inform game designers and educators, and to find ways to maximize learner engagement. In this work, we present our proposal to multidimensionally measure engagement in a learning game over four dimensions: general activity, social, exploration, and quests. We apply metrics from these dimensions to data from The Radix Endeavor, an inquiry-based online game for STEM learning that has been tested in K-12 classrooms as part of a pilot study across numerous schools. Based on these dimensions, we apply clustering and report four different engagement profiles that we define as: "integrally engaged," "lone achiever," "social explorer," and "non-engaged." We also use three variables (account type, class grade, and gender) to perform a cross-sectional analysis finding interesting, statistically significant differences in engagement. For example, in-school students and accounts registered to males engaged socially much more than out-of-school learners or accounts registered to females, and that older students have better performance metrics than younger ones.
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