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Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling

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Abstract

Driven by our technologically advanced workplaces and the surge in demand for proficiency in the computing disciplines, it is becoming imperative to provide computational thinking (CT) opportunities to all students. One approach for making computing accessible and relevant to learning and problem-solving in K-12 environments is to integrate it with existing Science, Technology, Engineering, and Math (STEM) curricula. However, novice student learners may face several difficulties in trying to learn STEM and computing concepts simultaneously. To address some of these difficulties, we present a systematic approach to learning STEM and CT by designing and developing domain-specific modeling languages (DSMLs) to aid students in their model building and problem-solving processes. The paper discusses a theoretical framework and the design principles for developing DSMLs, which is implemented as a four-step process. We apply the four-step process in three domains: Physics, Marine Biology, and Earth Science to demonstrate its generality, and then perform case studies to show how the DSMLs impact student learning and model building. We conclude with a discussion of our findings and then present directions for future work.

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Acknowledgements

We thank Shuchi Grover, Brian Broll, Satabdi Basu, Kevin McElhaney, Justin Montenegro, Beth Sanzenbacher, Naveed Mohammed, Kristen Pilner Blair, Doris Chin, Rachel Wolf and all of our C2STEM and SPICE project contributors for their assistance on this project.

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Data available on request from the authors.

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Available at https://github.com/c2stem

Funding

This project was supported under National Science Foundation Award DRL-1640199 and National Science Foundation Award DRL-1742195.

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Authors and Affiliations

  1. Vanderbilt University, Nashville, TN, USA

    Nicole M. Hutchins, Gautam Biswas, Ningyu Zhang, Caitlin Snyder & Ákos Lédeczi

  2. University of Szeged, Szeged, Hungary

    Miklós Maróti

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  1. Nicole M. Hutchins
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Correspondence to Nicole M. Hutchins.

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Hutchins, N.M., Biswas, G., Zhang, N. et al. Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling. Int J Artif Intell Educ 30, 537–580 (2020). https://doi.org/10.1007/s40593-020-00209-z

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