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Applying the Deep Learning Method for Simulating Outcomes of Educational Interventions

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Abstract

Predicting outcomes of educational interventions before investing in large-scale implementation efforts in school settings is essential for educational policy-making. However, due to time and resource limitations, conducting longitudinal, large-scale experiments testing outcomes of interventions in authentic settings is difficult. Here, we introduce the deep learning method as a way to address this issue and illustrate the use of the deep learning method for the prediction of intervention outcomes through a MATLAB implementation. The presented deep learning method extracts predictable patterns from an empirical dataset to simulate large-scale intervention outcomes. Findings from our simulations suggest that the deep learning applied simulation model can predict intervention outcomes significantly more accurately compared to the traditional regression analysis methods.

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Author notes

  1. Kangwook Lee and Firat Soylu have contributed equally to this work.

Authors and Affiliations

  1. Educational Psychology Program, University of Alabama, Box 870231, Tuscaloosa, AL, 35478, USA

    Hyemin Han & Firat Soylu

  2. The School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yeseong-gu, Daejeon, 34141, South Korea

    Kangwook Lee

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  1. Hyemin Han
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Correspondence to Hyemin Han.

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This article is part of the topical collection “Deep learning approaches for data analysis: A practical perspective” guest-edited by D. Jude Hemanth, Lipo Wang and Anastasia Angelopoulou.

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Han, H., Lee, K. & Soylu, F. Applying the Deep Learning Method for Simulating Outcomes of Educational Interventions. SN COMPUT. SCI. 1, 70 (2020). https://doi.org/10.1007/s42979-020-0075-z

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