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Materials Science Students’ Perceptions and Usage Intentions of Computation

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Conference

2013 ASEE Annual Conference & Exposition

Location

Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013

ISSN

2153-5965

Conference Session

Teaching with Technology

Tagged Division

Educational Research and Methods

Page Count

13

Page Numbers

23.888.1 - 23.888.13

Permanent URL

https://peer.asee.org/22273

Download Count

30

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Paper Authors

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

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is an Assistant Professor at the Department of Computer and Information Technology at Purdue University West Lafayette. Magana’s research interests are centered on the integration of cyberinfrastructure, computation, and computational tools and methods to: (a) leverage the understanding of complex phenomena in science and engineering and (b) support scientific inquiry learning and innovation. Specific efforts focus on studying cyberinfrastructure affordances and identifying how to incorporate advances from the learning sciences into authoring curriculum, assessment, and learning materials to appropriately support learning processes.

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biography

Michael L Falk Johns Hopkins University Orcid 16x16 orcid.org/0000-0002-8383-4259

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Michael Falk is an Associate Professor of Materials Science and Engineering at Johns Hopkins University with joint appointments in the department of Mechanical Engineering and the department of Physics and Astronomy. He earned his Ph.D. in theoretical physics from UC Santa Barbara in 1998. His primary research area is computational materials science as applied to understanding non-equilibrium properties such as failure modes and plasticity in amorphous metals, phase transformations in energy storage materials, and the nanoscale origins of friction. His educational activities include studies of the effect of integrating computation into engineering curriculum and leading STEM Achievement in Baltimore Elementary Schools (SABES) an NSF funded community based STEM enhancement effort for grades 3-5 in three Baltimore city neighborhoods.

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Mike Reese Johns Hopkins University

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Michael Reese is the Associate Director at the Johns Hopkins Center for Educational Resources. Reese previously worked as an Educational Technologist at Caliber Learning and Booz-Allen and Hamilton. He also consulted with the University of Maryland School of Nursing on the launch of their distance education program. He earned an M.Ed. in educational technology from the University of Virginia and a B.S. in electrical engineering at Virginia Tech, where he was named the Paul E. Torgersen Leadership Scholar.

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

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Master of Engineering from Universidad Eafit.
Doctoral student in Computer and Information Technology at Purdue University.

Research interests include Computing Education, Computational Thinking and Educational Technologies.

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Abstract

Materials Science Students’ Perceptions and Usage Intentions of ComputationBackground and MotivationOver the last decade many significant changes took place in Materials Science and Engineering(MSE), including the increasingly central role of computational methods in characterizingstructure, simulating processes and predicting materials' response. This change has been mostnotable in academia with the establishment of "computational materials science and engineering"(CMSE) as a recognized sub-discipline. Survey research indicates a consensus in the field thatadequate training in modeling and simulation of materials is critical for both undergraduate andgraduate students in MSE academic programs to prepare students for careers in basic research,engineering and product development.To align with this shift, an MSE department at a research university in the Northeast launched acurricular innovation to inculcate students with a basic facility with computational methods. Thecurricular innovation consists of integrating computational learning modules in the major’s sixcore courses to simultaneously reinforce CMSE skills and foundational MSE concepts. Theresearch questions driving this study are, what are students’: 1. perceptions regarding the usefulness of integrating computation in their studies and their future careers? 2. perceptions regarding their own abilities to implementing computation for understanding and solving MSE problems? 3. intentions regarding the use of computation in their studies and future careers?Design/MethodA pre-post test design included open questions and a Likert-survey aimed to measure predictorsof future behavior according to the Technology Acceptance Model (TAM). For this particularcase, the desired future behavior is the integration of computation (e.g., algorithm design,modeling and simulation, data visualization) in students’ future studies and eventually in theirfuture careers. According to TAM, the elements that predict future behavior are perceivedusefulness, perceived ease of use and intention to use. We adapted survey questions published inthe literature to identify how students’ perceive these constructs as related to computation.Participants of this study include approximately 154 engineering students from Structure ofMaterials (28), Physical Chemistry of Materials I: Thermodynamics (33) and Biomaterials I (93).Students completed at least two learning modules integrating computation with the course’s coreconcepts. Data analysis includes descriptive statistics to identify tendencies and inferentialstatistics to identify significant differences between the pretest and the posttest.ResultsHere we report pretest results. Students responses were scored as follows: strongly disagree (1),disagree (2), undecided (3) agree (4) and strongly agree (5). Scores from 1 to 2.4 wereinterpreted as negative perceptions or intentions to use. Scores from 3.5 to 5 were interpreted aspositive perceptions or intentions to use. Scores from 2.5 to 3.4 were considered inconclusive. Results are reported in three major groups. Students who have been exposed from 3 to 4 courses (N=16), 1 to 2 courses in computing (N=112), and none (N=17). Results depicted on Table 1 suggest a possible relationship between students’ exposure to computing courses and positive student perceptions about their ability to currently use and plans to use computing in future professional and academic work. After collecting more data, the final paper will conduct a deeper analysis of how the computational modules implemented impact student attitudes along with how these vary by students in different majors. Conclusions Advances in computing contribute to science and engineering discovery, innovation, and education by facilitating representations, processing, storage, analyses, simulation, and visualization of unprecedented amounts of experimental and observational data. Computing, as both fundamental knowledge and a technical skill, is therefore required to contribute to and to compete in our fast-changing and global society. Therefore, understanding undergraduate students’ perceptions and usage intentions of computing is an important research endeavor that can suggest future adoption. Table 1. Preliminary results on the pre-test. Negative results are highlighted in red. Positive results are bolded. 3 to 4 previous 1 to 2 previous 0 previous computing courses computing courses computing courses TAM Statements Mean Std N Mean Std N Mean Std N Dev Dev Dev I feel computation (e.g., algorithm design, modeling and simulation, data visualization) will 3.5 1.2 16 2.1 1.0 110 2.6 1.3 17 be useful in my studies. Usefulness I feel computation (e.g., algorithm design, modeling and simulation, data visualization) will 3.7 1.2 15 3.6 1.1 107 2.7 1.4 17 be useful in my career. I have the ability to design an algorithm. 3.0 1.0 16 1.4 0.5 112 1.5 0.6 17 I have the ability to write a computer program. 3.6 1.0 16 2.8 1.2 109 1.4 0.5 17 I have the ability to use a computer to solve a set of linear equations. 3.8 1.0 16 3.0 1.1 111 1.9 0.9 17 I have the ability to visualize data using a computer. 3.5 1.2 16 3.1 1.1 111 1.6 0.9 17Ease of use or ability to do I have the ability to create a computer representation of an atomic or molecular structure. 1.7 0.9 16 3.0 1.1 112 1.4 0.7 17 I have the ability to numerically solve an initial value problem. 3.5 1.1 15 1.9 1.3 111 2.5 1.4 17 I have the ability to implement a numerical model based on a simple partial differential equation. 2.9 0.8 16 3.4 1.1 111 1.7 1.0 17 I have the ability to implement a graphical user interface. 2.1 1.3 16 2.7 1.1 110 1.4 0.5 17 I intend to purposefully seek courses that will allow me to increase my knowledge about 2.8 0.8 16 3.4 1.2 110 2.2 1.1 17Intention to computation. use I intend to use computation (e.g., algorithm design, modeling and simulation, data visualization) in my 2.9 1.0 16 2.8 1.2 112 2.4 1.2 17 future career.

Magana, A. J., & Falk, M. L., & Reese, M., & Vieira, C. (2013, June), Materials Science Students’ Perceptions and Usage Intentions of Computation Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/22273

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