Using Computational Modeling to Integrate Science and Engineering Curricular Activities

We articulate a framework for using computational modeling to coherently integrate the design of science and engineering curricular experiences. We describe how this framework informs the design of the Water Runoff Challenge (WRC), a multi-week curriculum unit and modeling environment that integrates Earth science, engineering, and computational modeling for upper elementary and lower middle school students. In the WRC, students develop conceptual and computational models of surface water runoff, then use simulations incorporating their models to develop, test, and optimize solutions to the runoff problem. We conducted a classroom pilot study where we collected students’ learning artifacts and data logged from their use of the computational environment. We illustrate opportunities students had to integrate science, engineering, and computational thinking during the unit in a pair of contrasting vignettes.

[1]  M. David Burghardt,et al.  Informed Design: A Contemporary Approach to Design Pedagogy as the Core Process in Technology: In Classroom Settings Most Problems Are Usually Well Defined, So Students Have Little Experience with Open-Ended Problems , 2004 .

[2]  John S. Kinnebrew,et al.  A science learning environment using a computational thinking approach , 2012, ICCE 2012.

[3]  Jeannette M. Wing Computational thinking and thinking about computing , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[4]  Katherine L. McNeill,et al.  Learning‐goals‐driven design model: Developing curriculum materials that align with national standards and incorporate project‐based pedagogy , 2008 .

[5]  David Fortus,et al.  Design‐based science and real‐world problem‐solving , 2005 .

[6]  L. Schauble,et al.  Students' transition from an engineering model to a science model of experimentation , 1991 .

[7]  Richard Lehrer,et al.  From Physical Models to Biomechanics: A Design-Based Modeling Approach. , 1998 .

[8]  M. Chi,et al.  The ICAP Framework: Linking Cognitive Engagement to Active Learning Outcomes , 2014 .

[9]  Miklós Maróti,et al.  A visual programming environment for introducing distributed computing to secondary education , 2018, J. Parallel Distributed Comput..

[10]  Christopher J. Harris,et al.  Designing Knowledge‐In‐Use Assessments to Promote Deeper Learning , 2019, Educational Measurement: Issues and Practice.

[11]  W. V. van Joolingen,et al.  Scientific Discovery Learning with Computer Simulations of Conceptual Domains , 1998 .

[12]  L. Schauble,et al.  Cultivating Model-Based Reasoning in Science Education , 2005 .

[13]  Aditi Wagh,et al.  Balancing Curricular and Pedagogical Needs in Computational Construction Kits: Lessons from the DeltaTick Project. , 2015 .

[14]  William Rand,et al.  An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo , 2015 .

[15]  Arie van Deursen,et al.  Domain-specific languages: an annotated bibliography , 2000, SIGP.

[16]  Ngss Lead States Next generation science standards : for states, by states , 2013 .

[17]  Gautam Biswas,et al.  Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment , 2017, User Modeling and User-Adapted Interaction.

[18]  S. Ainsworth DeFT: A Conceptual Framework for Considering Learning with Multiple Representations. , 2006 .

[19]  Andrea A. diSessa An Overview of Boxer. , 1991 .

[20]  Gautam Biswas,et al.  Analyzing Students' Design Solutions in an NGSS-Aligned Earth Sciences Curriculum , 2019, AIED.

[21]  David Klahr,et al.  Dual Space Search During Scientific Reasoning , 1988, Cogn. Sci..

[22]  Jie Chao,et al.  Learning and teaching engineering design through modeling and simulation on a CAD platform , 2018, Comput. Appl. Eng. Educ..

[23]  Robert J. Mislevy,et al.  Implications of Evidence‐Centered Design for Educational Testing , 2007 .