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Towards automatic experimentation of educational knowledge

Published: 26 April 2014 Publication History

Abstract

We present a general automatic experimentation and hypothesis generation framework that utilizes a large set of users to explore the effects of different parts of an intervention parameter space on any objective function. We also incorporate importance sampling, allowing us to run these automatic experiments even if we cannot give out the exact intervention distributions that we want. To show the utility of this framework, we present an implementation in the domain of fractions and numberlines, using an online educational game as the source of players. Our system is able to automatically explore the parameter space and generate hypotheses about what types of numberlines lead to maximal short-term transfer; testing on a separate dataset shows the most promising hypotheses are valid. We briefly discuss our results in the context of the wider educational literature, showing that one of our results is not explained by current research on multiple fraction representations, thus proving our ability to generate potentially interesting hypotheses to test.

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    cover image ACM Conferences
    CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
    April 2014
    4206 pages
    ISBN:9781450324731
    DOI:10.1145/2556288
    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: 26 April 2014

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    Author Tags

    1. datamining
    2. education
    3. games

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    April 26 - May 1, 2014
    Ontario, Toronto, Canada

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    CHI '14 Paper Acceptance Rate 465 of 2,043 submissions, 23%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    • (2019)Beyond A/B TestingProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303812(539-548)Online publication date: 4-Mar-2019
    • (2019)Crowdsourcing Interface Feature Design with Bayesian OptimizationProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300482(1-12)Online publication date: 2-May-2019
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    • (2015)Large-Scale Educational CampaignsACM Transactions on Computer-Human Interaction10.1145/269976022:2(1-24)Online publication date: 10-Mar-2015
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