Camilo Vieira
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Abstract
This article presents two case studies aimed at exploring the use of self-explanations in the context of computational science and engineering (CSE) education. The self-explanations were elicited as students’ in-code comments of a set of worked-examples, and the cases involved two different approaches to CSE education: glass box and black box. The glass-box approach corresponds to a programming course for materials science and engineering students that focuses on introducing programming concepts while solving disciplinary problems. The black-box approach involves the introduction of Python-based computational tools within a thermodynamics course to represent disciplinary phenomena. Two semesters of data collection for each case study allowed us to identify the effect of using in-code comments as a self-explanation strategy on students’ engagement with the worked-examples and students’ perceptions of these activities within each context. The results suggest that the use of in-code comments as a self-explanation strategy increased students’ awareness of the worked-examples while engaging with them. The students’ perceived uses of the in-code commenting activities include: understanding the example, making a connection between the programming code and the disciplinary problem, and becoming familiar with the programming language syntax, among others.
- O. Alabi, A. J. Magana, and R. E. Garcia. 2015. Gibbs computational simulation as a teaching tool for students understanding of thermodynamics of materials concepts. J. Mater. Educ. 37, 5--6 (2015), 239--260Google Scholar
- Robert K. Atkinson, Sharon J. Derry, Alexander Renkl, and Donald Wortham. 2000. Learning from examples: Instructional principles from the worked examples research. Rev. Educ. Res. 70, 2 (2000), 181--214. Google ScholarCross Ref
- Viviane C. O. Aureliano, Patricia C. de AR Tedesco, and Michael E. Caspersen. 2016. Learning programming through stepwise self-explanations. In Proceedings of the 2016 11th Iberian Conference on Information Systems and Technologies (CISTI’16). AISTI, 1--6.Google Scholar
- J. D. Bransford, A. L. Brown, and R. Cocking. 2000. How People Learn. National Academy Press, Washington, DC.Google Scholar
- Sean P. Brophy, Alejandra J. Magana, and Alejandro Strachan. 2013. Lectures and simulation laboratories to improve learners’ conceptual understanding. Adv. Eng. Educ. 3, 3 (2013).Google Scholar
- William M. Carroll. 1994. Using worked examples as an instructional support in the algebra classroom.J. Educ. Psychol. 86, 3 (1994), 360. Google ScholarCross Ref
- Michelene T. H. Chi. 2009. Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics Cogn. Sci. 1, 1 (2009), 73--105. DOI:http://dx.doi.org/10.1111/j.1756-8765.2008.01005.x Google ScholarCross Ref
- Michelene T. H. Chi, Miriam Bassok, Mattew W. Lewis, Peter Reimann, and Robert Glaser. 1989. Self-explanatirms: How students study and use examples in learning to solve problems. Cogn. Sci. 13 (1989), 145--182. Google ScholarCross Ref
- Jennifer L. Chiu and Michelene T. H. Chi. 2014. Supporting self-explanation in the classroom. Acknowl. Dedicat. (2014), 91.Google Scholar
- T. Cool, A. Bartol, M. Kasenga, K. Modi, and R. E. García. 2010. Gibbs: Phase equilibria and symbolic computation of thermodynamic properties. CALPHAD: Computer Coupling of Phase Diagrams and Thermochemistry 34, 4 (2010), 393--404.Google ScholarCross Ref
- N. Cowan. 2001. The magical number 4 in short term memory. A reconsideration of storage capacity. Behav. Brain Sci. 24, 4 (2001), 87--186. Google ScholarCross Ref
- Nelson Cowan. 2010. The magical mystery four how is working memory capacity limited, and why? Curr. Direct. Psychol. Sci. 19, 1 (2010), 51--57. Google ScholarCross Ref
- John W. Creswell. 2013. Qualitative Inquiry and Research Design: Choosing Among Five Approaches. Sage.Google Scholar
- A. C. Graesser, D. F. Halpern, and M. Hakel. 2007. 25 Principles of Learning. Technical Report. Task Force on Lifelong Learning at Work and at Home., Washington, DC. Retrieved from http://www.psyc.memphis.edu/learning/whatweknow/index.shtml.Google Scholar
- Dave S. Kerby. 2014. The simple difference formula: An approach to teaching nonparametric correlation. Comprehens. Psychol. 3 (2014), 11--IT. Google ScholarCross Ref
- L. Kester, F. Paas, and J. J. G. van Merriënboer. 2010. Instructional control of cognitive load in the design of complex learning envinronments. In Cognitive Load Theory, J. Plass, R. Moreno, and R. Brünken (Eds.). 109--130.Google Scholar
- Tania Lombrozo. 2006. The structure and function of explanations. Trends Cogn. Sci. 10, 10 (2006), 464--470. DOI:http://dx.doi.org/10.1016/j.tics.2006.08.004 Google ScholarCross Ref
- Alejandra J. Magana, Sean P. Brophy, and George M. Bodner. 2010. The transparency paradox: Computational simulations as learning tools for engineering graduate education. In Proceedings of the American Educational Research Association Meeting: Understanding Complex Ecologies in a Changing World.Google Scholar
- Alejandra J. Magana, Sean P. Brophy, and George M. Bodner. 2012. Student views of engineering professors technological pedagogical content knowledge for integrating computational simulation tools in nanoscale. Int. J. Eng. Educ. 28, 5 (2012), 1033--1045. http://scholar.google.com/scholar?hl=enGoogle Scholar
- Alejandra J. Magana, Michael L. Falk, and Michael J. Reese Jr. 2013. Introducing discipline-based computing in undergraduate engineering education. ACM Trans. Comput. Educ. 13, 4 (2013), 16. Google ScholarDigital Library
- Alejandra J. Magana, Michael L. Falk, Camilo Vieira, and Michael J. Reese. 2016. A case study of undergraduate engineering students’ computational literacy and self-beliefs about computing in the context of authentic practices. Comput. Hum. Behav. 61 (2016), 427--442. DOI:http://dx.doi.org/10.1016/j.chb.2016.03.025 Google ScholarDigital Library
- Alejandra J. Magana and Jyoti I. Mathur. 2012. Motivation, awareness, and perceptions of computational science. Comput. Sci. Eng. 14, 1 (2012), 74--79. Google ScholarDigital Library
- Richard E. Mayer. 1981. The psychology of how novices learn computer programming. ACM Comput. Surv. 13, 1 (1981), 121--141. Google ScholarDigital Library
- M. David Merril. 2002. First principles of instruction. Educ. Technol. Res. Dev. 50, 3 (2002), 43--59. Google ScholarCross Ref
- George A. Miller. 1956. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol. Rev. 63, 2 (1956), 81. Google Scholar
- R. Moreno and B. Park. 2010. Cognitive load theory: Historical development and relation to other theories. In Cognitive Load Theory, J. Plass, R. Moreno, and R. Brünken (Eds.). Cambridge University Press, New York, 9--28.Google Scholar
- Briana B. Morrison, Lauren E. Margulieux, and Mark Guzdial. 2015. Subgoals, context, and worked examples in learning computing problem solving. In Proceedings of the 11th Annual International Conference on International Computing Education Research. ACM, 21--29.Google ScholarDigital Library
- Leonard J. Mselle and Hashim Twaakyondo. 2012. The impact of memory transfer language (MTL) on reducing misconceptions in teaching programming to novices. Int. J. Mach. Learn. Appl. 1 (2012), 1--6. DOI:http://dx.doi.org/10.4102/ijmla.v1i1.3. Google ScholarCross Ref
- NSF. 2011a. Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges—Final Report. Technical Report. National Science Foundation.Google Scholar
- NSF. 2011b. Empowering the Nation Through Discovery and Innovation. Technical Report. National Science Foundation.Google Scholar
- Fred Paas, Alexander Renkl, and John Sweller. 2003. Cognitive load theory and instructional design: Recent developments. Educ. Psychol. 38, 1 (Mar 2003), 1--4. DOI:http://dx.doi.org/10.1207/S15326985EP3801_1 Google ScholarCross Ref
- Fred G. W. C. Paas and Jeroen J. G. Van Merrinboer. 1994. Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach.J. Educ. Psychol. 86, 1 (1994), 122--133. DOI:http://dx.doi.org/10.1037/0022-0663.86.1.122 Google ScholarCross Ref
- M. Q. Patton. 2002. Qualitative Research 8 Evaluation Methods (2nd ed.). Sage Publications, Thousand Oaks, CA.Google Scholar
- PITAC. 2005. Computational Science: Ensuring America’s Competitiveness. Technical Report. President’s Information Technology Advisory Committee. Retrieved from http://www.nitrd.gov/pitac/reports/20050609.Google Scholar
- Alexander Renkl. 1997. Learning from worked-out example: A study on individual differences. Cogn. Sci. 21 (1997), 1--29. Google ScholarCross Ref
- A. Renkl. 2005. The worked-out examples principle in multimedia learning. In The Cambridge Handbook of Multimedia Learning, R. E. Mayer (Ed.). Cambridge University Press, New York. Google ScholarCross Ref
- J. Sweller, P. Ayres, and S. Kalyuga. 2011. Cognitive Load Theory. Springer, New York. Google ScholarCross Ref
- J. Sweller and P. Chandler. 1994. Why some material is difficult to learn. Cogn. Instruct. 12, 3 (1994), 185--233. Google ScholarCross Ref
- J. Sweller, J. J. G. van Merriënboer, and F. G. W. C. Paas. 1998. Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 3 (1998), 251--296. Google ScholarCross Ref
- Jeroen J. G. Van Merrienboer and John Sweller. 2005. Cognitive load theory and complex learning: Recent developments and future directions. Educ. Psychol. Rev. 17, 2 (2005), 147--177. Google ScholarCross Ref
- Jeroen J. G. van Merriënboer, Richard E. Clark, and Marcel B. M. Croock. 2002. Blueprints for complex learning: The 4C/ID-model. Educ. Technol. Res. Dev. 50, 2 (2002), 39--64. DOI:http://dx.doi.org/10.1007/BF02504993 Google ScholarCross Ref
- Camilo Vieira. 2016. Students’ Explanations in Complex Learning of Disciplinary Programming. Ph.D. Dissertation. Purdue University.Google Scholar
- Camilo Vieira, Anindya Roy, Alejandra J. Magana, Michael L. Falk, and Michael J. Reese Jr. 2016. In-code comments as a self-explanation strategy for computational science education. In Proceedings of the 2016 ASEE Annual Conference 8 Exposition. Google ScholarCross Ref
- Camilo Vieira, Junchao Yan, and Alejandra J. Magana. 2015. Exploring design characteristics of worked examples to support programming and algorithm design. J. Comput. Sci. Educ. 6, 1 (2015), 2--15. Google ScholarCross Ref
- Joseph J. Williams and Tania Lombrozo. 2010. The role of explanation in discovery and generalization: Evidence from category learning. Cogn. Sci. 34, 5 (2010), 776--806. DOI:http://dx.doi.org/10.1111/j.1551-6709.2010.01113.x Google ScholarCross Ref
Index Terms
- Writing In-Code Comments to Self-Explain in Computational Science and Engineering Education
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