We present a new technology-based paradigm to support embodied mathematics educational games, using wearable devices in the form of SmartPhones and SmartWatches for math learning, for full classes of students in formal in- school education settings. The Wearable Learning Games Engine is web based infrastructure that enables students to carry one mobile device per child, as they embark on math team-based activities that require physical engagement with the environment. These Wearable Tutors serve as guides and assistants while students manipulate, measure, estimate, discern, discard and find mathematical objects that satisfy specified constraints. Multi-player math games that use this infrastructure have yielded both cognitive and affective benefits. Beyond math game play, the Wearable Games Engine Authoring Tool enables students to create games themselves for other students to play; in this process, students engage in computational thinking and learn about finite-state machines. We present the infrastructure, games, and results for a series of experiments on both game play and game creation.
This content will become publicly available on March 21, 2023
Grade 5 Students’ Elective Replay After Experiencing Failures in Learning Fractions in an Educational Game: When Does Replay After Failures Benefit Learning?
Despite theoretical benefits of replayability in educational games, empirical studies have found mixed evidence about the effects of replaying a previously passed game (i.e., elective replay) on students’ learning. Particularly, we know little about behavioral features of students’ elective replay process after experiencing failures (i.e., interruptive elective replay) and the relationships between these features and learning outcomes. In this study, we analyzed 5th graders’ log data from an educational game, ST Math, when they studied fractions—one of the most important but challenging math topics. We systematically constructed interruptive elective replay features by following students’ sequential behaviors after failing a game and investigated the relationships between these features and students’ post-test performance, after taking into account pretest performance and in-game performance. Descriptive statistics of the features we constructed revealed individual differences in the elective replay process after failures in terms of when to start replaying, what to replay, and how to replay. Moreover, a Bayesian multi-model linear regression showed that interruptive elective replay after failures might be beneficial for students if they chose to replay previously passed games when failing at a higher, more difficult level in the current game and if they passed the replayed games.
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- LAK22: LAK22: 12th International Learning Analytics and Knowledge Conference
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- National Science Foundation
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