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An Exploratory Study of the Role of Modeling and Simulation in Supporting or Hindering Engineering Students’ Problem-solving Skills

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Conference

2015 ASEE Annual Conference & Exposition

Location

Seattle, Washington

Publication Date

June 14, 2015

Start Date

June 14, 2015

End Date

June 17, 2015

ISBN

978-0-692-50180-1

ISSN

2153-5965

Conference Session

Teaching and Learning Strategies I

Tagged Division

Educational Research and Methods

Page Count

20

Page Numbers

26.185.1 - 26.185.20

DOI

10.18260/p.23524

Permanent URL

https://peer.asee.org/23524

Download Count

57

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Paper Authors

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Uzma Shaikh Purdue University

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Uzma Shaikh is a Graduate Student in the Department of Computer and Information Technology at Purdue University, West Lafayette, Indiana. She is currently working as a Research Assistant in the field of Computer and Education Technology. Shaikh's research focuses on using visual simulations along with scientific inquiry learning for understanding concepts related to unobservable macroscopic phenomena.

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Alejandra J. Magana Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0001-6117-7502

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Alejandra Magana is an Assistant Professor in the Department of Computer and Information Technology and an affiliated faculty at the School of Engineering Education at Purdue University. She holds a B.E. in Information Systems, a M.S. in Technology, both from Tec de Monterrey; and a M.S. in Educational Technology and a Ph.D. in Engineering Education from Purdue University. Her research is focused on identifying how model-based cognition in STEM can be better supported by means of expert technological and computing tools such as cyberinfrastructure, cyber-physical systems, and computational modeling and simulation tools.

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Camilo Vieira Purdue University Orcid 16x16 orcid.org/0000-0001-8720-0002

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Second year PhD student in Computing Education - Purdue University
Master of Engineering in Educational Technologies - Eafit University
Systems Engineer - Eafit University

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R Edwin García Purdue University, West Lafayette

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Abstract

An Exploratory Study of the Role of Modeling and Simulation in Supporting or Hindering Engineering Students’ Problem Solving SkillsBackground and MotivationIn the context of problem-solving in science and engineering, the use and creation of computingartifacts are being used to understand and design systems 1. Specifically, the role of modelingand simulation has become the new form of literacy in engineering domains. In educationalcontexts, simulations have been primarily used for inquiry learning and conceptual change 2.However, to effectively integrate these tools in engineering contexts so students can also developproblem solving and design skills, in addition to inquiry skills, necessitates the adoption of a“practice perspective”3. In a practice perspective the focus of learning is on participation inauthentic contexts, where the learning experiences: (a) are personally meaningful to the learner,(b) relate to the real-world, and (c) provide an opportunity to think in the modes of a particulardiscipline 4. Since practice consists of a process of action and reflection in context 5, we arguethat learning through practice requires involving learners in original “field” experiences wherethey participate in (i) the process of collecting, transforming, and summarizing data and (ii) therepresentation of the relationship between the observed event and its re-representation 3. Toexplore the role of computing in engineering problem solving, the guiding research question forthis study is: How modeling and simulation practices support or hinder problem solvingprocesses?MethodThis exploratory qualitative study includes 10 graduate students from a kinetics of materialscourse at a Midwestern University who were engaged in computational design challenges. Theprocedures consisted of having students investigate one of the following design challenges: (a)thin film growth, (b) sintering of ceramics, (c) battery modeling, and (d) thermoelectric design.Once students conducted a preliminary literature review on a given topic, then they were askedto implement a computational solution for the corresponding design challenge. Studentscompleted their designs following the four steps of the problem solving process as follows:Problem recognition phase: students investigated the conceptual aspects of the problem to besolved, that is, students identified concepts associated with kinetics of materials. Problemframing phase: in this stage students elaborated possible conceptual models of their solutionsand identified a possible mathematical model that can represent the physical phenomenonassociated with the design challenge. Problem synthesis phase: in this stage students work inimplementing their models by means of domain-specific software (e.g., MATLAB,Mathematica, Python); they also validate their own implemented models by comparing andcontrasting them upon existing simulations, empirical data from journal articles, test casesprovided by the instructor, or theoretical models described on textbooks. Problemimplementation phase: in this stage students use their validated implementation to solve theproblem or design challenge. Students then prepared a report where they included all thecomponents mentioned above. These reports will be qualitatively analyzed and scored with arubric that will evaluate all outputs from each of the stages of the problem solving process. Thisevaluation will also include aspects of how students then used their own created models to solvean engineering design problem and the degree of effectiveness of their solutions. Students werealso interviewed prompted them to describe their solutions, their thought processes and designdecisions, the challenges they encountered in solving the problem, and strategies they used toovercome those challenges.ResultsTo approach our analysis we have developed an operational framework where we will identifyhow modeling and simulation processes coupled with problem solving processes (see Table 1).At this moment we are analyzing the data for this study, and therefore more detailed results willbe presented in the final version of the paper. In our analyses we will specifically identify (a)how students enacted the stages of the problem solving process (see Rubric Appendix A), (b) analignment between the outputs of each of the stages of the problem solving process in terms ofthe conceptual model proposed, the mathematical model identified, and the computational modelimplemented (to be analyzed qualitatively to identify mappings and transformations amongrepresentations), and (c) how computational tools supported or hindered these processes (toidentify patterns on students’ experiences).Table 1. Operational framework to identify the relationship between problem solving and modeling and simulation Phase 6 Possible interplay between problem solving7 and modeling and simulation8 Problem Identify/understand the problem. recognition Gather information, set goals and plan. Generate ideas and evaluate different alternatives.Problem framing Formulate the problem in a way that enables the learner to use a computer and other tools to help solve it. Problem Model possible solutions, take measurements, characterize the solution, perform calculations, and synthesis automate the solution through an algorithm, implement the solution. Problem Use the model to evaluate and analyze possible solutions, and select one idea among alternatives. implementation Produce/represent the solution and communicate the solution to others.Conclusion and ImplicationsThe implications of our study relate to (a) the design of problem solving learning experiencesthat engage learners in authentic practices contextualized in real world problems and that thesame develop modeling and simulation skills and (b) the integration of different constructsassociated with workplace engineering practices that can enable us to propose an analyticalframework to investigate them concurrently. Outcomes will also include factors that contributeto, or prevent, effective teaching and learning with computational tools.References:1 Emmott, S. & Rison, S. Towards 2020 science. Science in Parliament 65, 31-33 (2008).2 de Jong, T., Linn, M. C. & Zacharia, Z. C. Physical and Virtual Laboratories in Science and Engineering Education. Science 340, 305-308 (2013).3 Roth, W. M. & McGinn, M. K. Graphing: Cognitive ability or practice? Science Education 81, 91-106 (1997).4 Shaffer, D. W. & Resnick, M. " Thick" Authenticity: New Media and Authentic Learning. Journal of Interactive Learning Research 10, 195-215 (1999).5 Ehn, P. Scandinavian design: On participation and skill. Participatory design: Principles and practices, 41-77 (1993).6 Litzinger, T. A. et al. A cognitive study of problem solving in statics. Journal of Engineering Education 99, 337-353 (2010).7 Atman, C. J. & Bursic, K. M. Verbal protocol analysis as a method to document engineering student design processes. Journal of Engineering Education 87, 121-132 (1998).8 [CSTA]. Operational definition of computational thinking, (2012).Appendix A Assessment Rubric STUDENT ID: Poor (0-2) … Excellent (9-10) Problem Recognition (20%) - There is no - All the relevant aspects of the problem are review of explored, described and discussed. Investigate the conceptual aspects of the conceptual aspects problem to be solved, that is, students identified for the problem … - A critical review of the conceptual aspects has concepts associated with kinetics of materials. been carried out. Problem Framing (25%) - There is no clear - Possible models are proposed and carefully model for the analyzed. Elaborate possible conceptual models of their implementation. solutions and identified a possible mathematical - The selected model is the appropriate to solve the … model that can represent the physical problem or design challenge. phenomenon associated with the design challenge. - Clear mathematical representation of the chosen model is presented. Problem Synthesis (25%) - The solution - The solution is very accurate and well aligned with produces wholly the described mathematical model. The solution Construction and validation of their models by incorrect output produces correct output in all cases with only minor means of domain-specific software (e.g., under all of the exceptions. MATLAB, Mathematica, Python); they also tests run. validate their own implemented models by … - The solution has been tested and compared to comparing and contrasting them upon existing - The model is alternative solutions. Advantages and drawbacks are simulations, empirical data from journal articles, incorrectly coded. analyzed. test cases provided by the instructor, or theoretical models described on textbooks. Problem Implementation and Deployment of - The - The solution is seamlessly configured to solve the Disciplinary Concepts (30%) implementation is problem or design challenge. not suitable to Use of the simulation to solve the problem or solve the problem - The interpretation of the output in terms of the design challenge. or design … disciplinary concepts is correct and thorough. challenge. How students then used their own created models to solve an engineering design problem and the degree of effectiveness of their solutions

Shaikh, U., & Magana, A. J., & Vieira, C., & García, R. E. (2015, June), An Exploratory Study of the Role of Modeling and Simulation in Supporting or Hindering Engineering Students’ Problem-solving Skills Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23524

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