Engineering and Science Instructors' Intended Learning Outcomes with Computational Simulations as Learning Tools

By Alejandra J. Magana

Purdue University

Published on

Abstract

This presentation describes the results of a study aiming to identify how 14 instructors incorporated into their classrooms computational simulations as learning tools. The study was based on the following research question: What were the intended learning outcomes that guided the instructors' use of computational simulations as learning tools? This study used Open-ended interviews as the data collection method and phenomenographic approaches to conduct the data analysis. The results of this study outlined an outcome space describing eight different forms in which professors incorporated these tools into their teaching.

The results of this study could serve toward the development of: (i) a common language about learning goals to facilitate communication across persons, subject matter, and grade levels; (ii) a basis for determining congruent educational objectives, activities, and assessments in a unit, course, or curriculum; and (iii) a panorama of the range of educational possibilities against which the limited breadth and depth that any particular computational simulation could provide as teaching or learning tool.

Bio

Alejandra Magana, Ph.D., 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 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.

Sponsored by

US National Science Foundation for the funding support through the NCN with the award EEC-0634750

Publications

Magana, A.J., Brophy, S.P. and Bodner, G.M. (2012). Instructors Intended Learning Outcomes for Using Computational Simulations as Learning Tools. Journal of Engineering Education. 101(2). 220-243

Cite this work

Researchers should cite this work as follows:

  • Alejandra J. Magana (2013), "Engineering and Science Instructors' Intended Learning Outcomes with Computational Simulations as Learning Tools," https://nanohub.org/resources/16592.

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Engineering and Science Instructors\' Intended Learning Outcomes with Computational Simulations as Learning Tools
  • Engineering and Science Instructors' Intended Learning Outcomes with Computational Simulations as Learning Tools 1. Engineering and Science Instru… 0
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  • Motivation for the Study 2. Motivation for the Study 23.456790123456791
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  • Main Contributions 3. Main Contributions 91.991991991992
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  • Method 4. Method 145.24524524524526
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  • Phenomenography 5. Phenomenography 167.90123456790124
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  • Method 6. Method 199.33266599933268
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  • Method 7. Method 237.33733733733735
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  • Results 8. Results 293.96062729396061
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  • Results 9. Results 333.53353353353356
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  • Results 10. Results 390.75742409075741
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  • Results 11. Results 444.51117784451117
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  • Results 12. Results 498.49849849849852
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  • Results 13. Results 540.00667334000673
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  • Results 14. Results 581.11444778111445
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  • Results 15. Results 618.18485151818493
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  • Results 16. Results 647.61428094761436
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  • Results 17. Results 694.19419419419421
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  • Discussion 18. Discussion 758.82549215882557
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  • Discussion 19. Discussion 802.16883550216892
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  • Discussion 20. Discussion 825.69235902569244
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  • Discussion 21. Discussion 875.975975975976
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  • Discussion 22. Discussion 912.379045712379
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  • Discussion 23. Discussion 934.13413413413411
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  • Discussion 24. Discussion 953.35335335335344
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  • Discussion 25. Discussion 980.64731398064737
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  • Implications 26. Implications 1002.0687354020688
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  • Roughly Aligned with Bloom's Taxonomy 27. Roughly Aligned with Bloom's T… 1075.9092425759093
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  • Implications 28. Implications 1143.2766099432768
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  • Conclusion 29. Conclusion 1209.3426760093428
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  • Acknowledgements 30. Acknowledgements 1257.5575575575576
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