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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Data-Driven Design Decisions to Improve Game-Based Learning of Fractions

verfasst von : Manuel Ninaus, Kristian Kiili, Robert S. Siegler, Korbinian Moeller

Erschienen in: Games and Learning Alliance

Verlag: Springer International Publishing

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Abstract

The educational value of games for learning is of prime importance. To design tasks that promote learning, findings from basic research and user data from earlier versions of the game can be very helpful. In the current study, we reanalysed data from two studies using a game-based assessment and a training tool for improving fraction magnitude knowledge. Similar to results from basic research, data of both studies indicated that performance in game-based versions of number line estimation and magnitude comparison were highly correlated (even after controlling for students’ overall math achievement). This suggests that both tasks share an underlying (spatial) representation of number magnitude. Based on this we designed a new task to improve fraction learning. In particular, we merged mechanics of number line estimation and magnitude comparison to consider shared processing of number magnitude in both tasks in our game-based learning of fractions.

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Vorheriges Kapitel Erratum to: Emotions Detection Through the Analysis of Physiological Information During Video Games Fruition
Nächstes Kapitel Building a Game to Build Competencies
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Metadaten
Titel
Data-Driven Design Decisions to Improve Game-Based Learning of Fractions
verfasst von
Manuel Ninaus
Kristian Kiili
Robert S. Siegler
Korbinian Moeller
Copyright-Jahr
2017
Buch
Games and Learning Alliance

Print ISBN: 978-3-319-71939-9

Electronic ISBN: 978-3-319-71940-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-71940-5_1

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