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Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game

Published: 25 April 2016 Publication History

Abstract

Student retention is a central challenge in systems for learning at scale. It has been argued that educational video games could improve student retention by providing engaging experiences and informing the design of other online learning environments. However, educational games are not uniformly effective. Our recent research shows that player retention can be increased by using a brain points incentive structure that rewards behaviors associated with growth mindset, or the belief that intelligence can grow. In this paper, we expand on our prior work by providing new insights into how growth mindset behaviors can be effectively promoted in the educational game Refraction. We present results from an online study of 25,000 children who were exposed to five different versions of the brain points intervention. We find that growth mindset animations cause a large number of players to quit, while brain points encourage persistence. Most importantly, we find that awarding brain points randomly is ineffective; the incentive structure is successful specifically because it rewards desirable growth mindset behaviors. These findings have important implications that can support the future generalization of the brain points intervention to new educational contexts.

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  1. Brain Points: A Deeper Look at a Growth Mindset Incentive Structure for an Educational Game

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      Stewart Mark Godwin

      This research builds on previous work by the authors and follows up with an experimental design that adds depth to the original paper [1]. A growth mindset is the belief that student intelligence can be taught and will increase over time. This research encourages a growth mindset by "using a novel brain points incentive structure that directly rewards students for their [persistence and] effort." Following up from the previous research, the authors constructed five different versions of an educational game to answer four research questions. One version of the game was used as the control while the other four versions examined each of the question variables of the research topic. The first study used a sample size of 15,000 students while this study used a larger sample size of 25,000 students. This enormous sample size is the reason the authors could not compare the data from each of the four versions of the educational game. Despite this limitation, each question did reveal significant information; for example, the narrative animation in the game did cause players to quit before starting the first level. However, the brain points incentive increased player persistence and was effective because it rewarded desirable growth mindset behavior. In conclusion, this research has the potential to improve student retention in online education and should be of interest to all educational professionals. Online Computing Reviews Service

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      cover image ACM Conferences
      L@S '16: Proceedings of the Third (2016) ACM Conference on Learning @ Scale
      April 2016
      446 pages
      ISBN:9781450337267
      DOI:10.1145/2876034
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 25 April 2016

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      Author Tags

      1. educational games
      2. growth mindset
      3. incentive structures.

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      L@S 2016: Third (2016) ACM Conference on Learning @ Scale
      April 25 - 26, 2016
      Scotland, Edinburgh, UK

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      L@S '16 Paper Acceptance Rate 18 of 79 submissions, 23%;
      Overall Acceptance Rate 117 of 440 submissions, 27%

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