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Synergistic learning of computational thinking (CT) and STEM has proven to be an effective method for enhancing CT education as well as advancing learning in many STEM domains. Domain Specific Modeling Languages (DSML) facilitate the building of computational modeling frameworks that are directly linked to STEM content, thus making it easier for students to focus on concepts and practices. At the same time, teachers can more easily relate curricular content to the model building tasks. This paper discusses the design, development, and implementation of a robotics DSML to support a middle school geometry curriculum.
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A Systematic Approach for Analyzing Students’ Computational Modeling Processes in C2STEM
Introducing computational modeling into STEM classrooms can provide opportunities for the simultaneous learning of computational thinking (CT) and STEM. This paper describes the C2STEM modeling environment for learning physics, and the processes students can apply to their learning and modeling tasks. We use an unsupervised learning method to characterize student learning behaviors and how these behaviors relate to learning gains in STEM and CT.
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- Award ID(s):
- 1640199
- PAR ID:
- 10110538
- Date Published:
- Journal Name:
- International Conference on Artificial Intelligence in Education (AIED) 2019.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org.more » « less
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There is increasing interest in broadening participation in computational thinking (CT) by integrating CT into pre-college STEM curricula and instruction. Science, in particular, is emerging as an important discipline to support integrated learning. This highlights the need for carefully designed assessments targeting the integration of science and CT to help teachers and researchers gauge students’ proficiency with integrating the disciplines. We describe a principled design process to develop assessment tasks and rubrics that integrate concepts and practices across science, CT, and computational modeling. We conducted a pilot study with 10 high school students who responded to integrative assessment tasks as part of a physics-based computational modeling unit. Our findings indicate that the tasks and rubrics successfully elicit both Physics and CT constructs while distinguishing important aspects of proficiency related to the two disciplines. This work illustrates the promise of using such assessments formatively in integrated STEM and computing learning contexts.more » « less
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Computational models (CMs) offer pre-college students opportunities to integrate STEM disciplines with computational thinking (CT) in ways that reflect authentic STEM practice. However, not all STEM teachers and students are prepared to teach or learn programming skills required to construct CMs. To help broaden participation in computing and reduce the potentially prohibitive demands of learning programming, we propose alternate versions of computational modeling that require low or no programming. These versions rely on code comprehension and evaluation of given code and simulations instead of code creation. We present results from a pilot study that explores student engagement with CT practices and student challenges in three types of computational modeling activities.more » « less
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Synergistic learning of computational thinking (CT) and STEM has proven to effective in helping students develop better understanding of STEM topics, while simultaneously acquiring CT concepts and practices. With the ubiquity of computational devices and tools, advances in technology,and the globalization of product development, it is important for our students to not only develop multi-disciplinary skills acquired through such synergistic learning opportunities, but to also acquire key collaborative learning and problem-solving skills. In this paper, we describe the design and implementation of a collaborative learning-by-modeling environment developed for high school physics classrooms. We develop systematic rubrics and discuss the results of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
