Optimizing challenge in an educational game using large-scale design experiments

Conference Paper (2013)
Author(s)

J.D. Lomas (Carnegie Mellon University)

Kishan Patel (Dhirubhai Ambani Institute of Information and Communication Technology)

Jodi Forlizzi (Carnegie Mellon University)

Kenneth R. Koedinger (Carnegie Mellon University)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1145/2470654.2470668
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Publication Year
2013
Language
English
Affiliation
External organisation
Pages (from-to)
89-98
ISBN (print)
9781450318990

Abstract

Online games can serve as research instruments to explore the effects of game design elements on motivation and learning. In our research, we manipulated the design of an online math game to investigate the effect of challenge on player motivation and learning. To test the "Inverted-U Hypothesis", which predicts that maximum game engagement will occur with moderate challenge, we produced two large-scale (10K and 70K subjects), multifactor (2x3 and 2x9x8x4x25) online experiments. We found that, in almost all cases, subjects were more engaged and played longer when the game was easier, which seems to contradict the generality of the Inverted-U Hypothesis. Troublingly, we also found that the most engaging design conditions produced the slowest rates of learning. Based on our findings, we describe several design implications that may increase challenge-seeking in games, such as providing feedforward about the anticipated degree of challenge.

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