Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Lea
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Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling Nicole M. Hutchins 1 & Gautam Biswas 1 & Ningyu Zhang 1 & Caitlin Snyder 1 & Ákos Lédeczi 1 & Miklós Maróti 2 # International Artificial Intelligence in Education Society 2020
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. Keywords Learning-by-modeling . Stem+CT . Synergistic learning . Evidence-centered
design . Domain-specific modeling language
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s40593-02000209-z) contains supplementary material, which is available to authorized users.
* Nicole M. Hutchins [email protected]
1
Vanderbilt University, Nashville, TN, USA
2
University of Szeged, Szeged, Hungary
International Journal of Artificial Intelligence in Education
Introduction Computational modeling is fundamental to the learning and practice of science (Wing 2011; Grover and Pea 2018). This multifaceted process includes the building, evaluating, and revising of models based on the learners’ underlying understanding of the domain theory and relations that govern the behavior of the model (Schwarz and White 2005). Learning-by-modeling encompasses these processes, facilitating model building, simulation, and analysis supported by the use of a modeling language. Adopting learning-by-modeling approaches in K-12 STEM (Science, Technology, Engineering, and Math) classrooms has proven to be an effective vehicle for integrating the teaching and learning of STEM and computational thinking (CT), helping students become active constructors as opposed to passive consumers of scientific knowledge and practices (Lehrer and Schauble 2015; Clark et al. 2009; Sun and Looi 2013; Wieman et al. 2008). Technology-enhanced environments can
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