Alejandra J. Magana
Skip Abstract Section
Abstract
This article investigates the effectiveness of a course employing a discipline-based computing approach. The research questions driving this study were: (1) Can experiences with discipline-based computing promote students’ acquisition and application of foundational computing concepts and procedures? (2) How do students perceive and experience the integration of discipline-based computing as relevant to their future career goals? (3) How do students perceive the structure of the class as useful and engaging for their learning? We used qualitative and quantitative research methods to approach the research questions. The population studied was 20 engineering undergraduates from Johns Hopkins University. Results of this study suggest that students performed proficiently in applying computing methods, procedures, and concepts to the solution of well-structured engineering problems. Results also suggest that student self-perceptions of their overall computing abilities and their abilities to specifically solve engineering problems shifted from low to high confidence. Students consistently found the course to be important and useful for their studies and their future careers. They also found the course to be of very high quality and identified the instructors and the teaching and feedback methods employed as very useful for their learning. Finally, students also described the course as very challenging compared with other courses in their own department and at the university in general.
- Adams, J. B. 2008. Computational science as a twenty-first century discipline in the liberal arts. J. Comput. Small Coll. 23, 5, 15--23. Google Scholar
Digital Library
- Agresti, A. 2007. An Introduction to Categorical Data Analysis. Wiley-Blackwell.Google Scholar
- Azemi, A. and Pauley, L. 2006. Teaching the introductory computer programming course for engineers using MATLAB and some exposure to C. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’06).Google Scholar
- Belu, R. and Belu, A. 2010. A learning-by-doing approach to teaching computational physics. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’10).Google Scholar
- Bowen, J. D. 2004. Motivating civil engineering students to learn computer programming with a structural design project. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’04).Google Scholar
- Bransford, J. 2000. How People Learn: Brain, Mind, Experience, and School. National Academies Press.Google Scholar
- Brown, J. S., Collins, A., and Duguid, P. 1989. Situated cognition and the culture of learning. Educ. Research 18, 32--42.Google ScholarCross Ref
- Brush, T. and Saye, J. W. 2002. A summary of research exploring hard and soft scaffolding for teachers and students using a multimedia supported learning environment. J. Interactive Online Learn. 1, 1--12.Google Scholar
- Carlisle, M. C. 2010. Using YouTube to enhance student class preparation in an introductory Java course. In Proceedings of the 41st Annual Technical Symposium of Computer Science Education (SIGCSE’10). 470--474. Google ScholarDigital Library
- Ceder, G. 1998. Predicting properties from scratch. Sci. 280, 1099.Google ScholarCross Ref
- Cook, M. P. 2006. Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Sci. Educ. 90, 1073--1091.Google Scholar
- De Jong, T. 1991. Learning and instruction with computer simulations. Educ. Comput. 6, 217--229.Google ScholarCross Ref
- De Jong, T. and van Joolingen, W. R. 2007. AECT Handbook of Educational Communications and Technology 3rd Ed. J. M. Spector, M. D. Merril, J. J. G. van Merrienboer, and M. P. Driscoll Eds., Lawrence Erlbaum Associates, Mahwah, NJ, 457--468.Google Scholar
- Dede, C., Salzman, M. C., Loftin, R. B., and Sprague, D. 1999. Multisensory immersion as a modeling environment for learning complex scientific concepts. Model. Sim. Sci. Math. Educ. 282--319.Google Scholar
- Denning, P. J. 2000. Computer science: The discipline. In Encyclopedia of Computer Science, A. Ralston, E. Reilly, and D. Hemmendinger Eds., Grove’s Dictionaries, New York.Google Scholar
- Devens, P. E. 1999. MATLAB & freshman engineering. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’99).Google Scholar
- El-Zein, A., Langrish, T., and Balaam, N. 2009. Blended teaching and learning of computer programming skills in engineering curricula. Adv. Eng. Educ. 1, 1--18.Google Scholar
- Fredericks, J. A., Blumenfeld, P. C., and Paris, A. H. 2004. School engagement: Potential of the concept, state of the evidence. Rev. Educ. Res. 74, 59--109.Google ScholarCross Ref
- Gannod, G. C., Burge, J. E., and Helmick, M. T. 2008. Using the inverted classroom to teach software engineering. In Proceedings of the 30th International Conference on Software Engineering (ISCE’08). 777--786. Google ScholarDigital Library
- Gannod, G. C., Burge, J. E., and Helmick, M. T. 2008. Using the inverted classroom to teach software engineering. In Proceedings of ICSE. 777--786. Google ScholarDigital Library
- Goldman, K., Gross, P., Heeren, C., Herman, G. L., Kaczmarczyk, L., Loui, M. C., and Zilles, C. 2010. Setting the scope of concept inventories for introductory computing subjects. ACM Trans. Comput. Educ. 10, 5. Google ScholarDigital Library
- Green, R. A. 2000. Mastery learning with the MATLAB webserver. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’00).Google Scholar
- Guzdial, M. 1994. Software-realized scaffolding to facilitate programming for science learning. Interact. Learn. Envir. 4, 1--44.Google ScholarCross Ref
- Hafner, J. 2000. Atomic-scale computational materials science. Acta Materialia 48, 71--92.Google ScholarCross Ref
- Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., and Hosking, A. L. 2009. A multidisciplinary approach towards computational thinking for science majors. In Proceedings of the 40th ACM Technical Symposium on Computer Science. Google ScholarDigital Library
- Hensel, R. and Sun, Y. 2006. Application of object scaffolding to develop a hands-on problem-centered and project-based freshman MATLAB course. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’06).Google Scholar
- Herniter, M. E., Scott, D., and Pangasa, R. 2001. Teaching programming skills with MATLAB. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’01).Google Scholar
- Hinds, T. and Somerton, C. W. 2007. Integrating the teaching of computer skills with an introduction to mechanical engineering course. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’07).Google Scholar
- Hinds, T., Urban-Lurain, M., Sticklen, J., Amey, M., and Eskil, T. 2005. Curricular integration of computational tools: A first step. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’05).Google Scholar
- Howard, W. E. and Musto, J. C. 2005. Computer-based skills in an MET curriculum. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’05).Google Scholar
- Hrynuk, J., Pennington, M., Illig, D., and Dempsey, J. P. 2008. Freshman engineering: An introductory computer course teaching MATLAB and LABVIEW. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’08).Google Scholar
- Hu, C. 2007. Integrating modern research into numerical computation education. Comput. Sci. Eng. 9, 78--81. Google ScholarDigital Library
- Hutchison, M. A., Follman, D. K., Sumpter, M., and Bodner, G. M. 2006. Factors influencing the self-efficacy beliefs of first-year engineering students. J. Eng. Educ. 95, 39.Google ScholarCross Ref
- Jimerson, S., Campos, E., and Greif, J. 2003. Toward an understanding of definitions and measures of school engagement and related terms. CA. School Psychol. 8, 7--27.Google ScholarCross Ref
- Johnson, W. and Goeser, P. 2007. Intradisciplinary teaching in the engineering studies curriculum: Applying numerical modeling techniques to statics problems. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’07).Google Scholar
- Jonassen, D. and Strobel, J. 2006. Modeling for meaningful learning. Engaged Learn. Emerg. Tech. 1--27.Google Scholar
- Kaczmarczyk, L. C., Petrick, E. R., East, J. P., and Herman, G. L. 2010. Identifying student misconceptions of programming. In Proceedings of the 41st Annual Technical Symposium of Computer Science Education (SIGCSE’10). 107--111. Google ScholarDigital Library
- Lage, M. J., Platt, G. J., and Treglia, M. 2000. Inverting the classroom: A gateway to creating an inclusive learning environment. J. Econ. Educ. 31, 30--43.Google ScholarCross Ref
- Lent, R. W., Lopez, F. G., and Bieschke, K. J. 1991. Mathematics self-efficacy: Sources and relation to science-based career choice. J. Counsel. Psychol. 38, 424.Google ScholarCross Ref
- Luchini, K., Colbry, D., and Punch, W. 2007. Designing introductory programming courses for graduate and undergraduate students: A parallel case study. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’07).Google Scholar
- Magana, A. J., Falk, M., Reese, M., and Vieira, C. 2013. Materials science students’ perceptions and usage intentions of computation. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’13).Google Scholar
- Magana, A. J. and Mathur, J. I. 2012. Motivation, awareness, and perceptions of computational science. Comput. Sci. & Eng. 14, 74--79. Google ScholarDigital Library
- Magana, A. J., Vieira, C., Polo, F., Yan, J., and Sun, X. 2013. An exploratory survey on the use of computation in undergraduate engineering education. In Proceedings of the Frontiers in Education Conference (FIE’13).Google Scholar
- Mayer, R. E. 2004. Should there be a three-strikes rule against prue discovery learning? Amer. Psychol. 59, 14--19.Google ScholarCross Ref
- McKenna, A. F. and Carberry, A. R. 2012. Characterizing the role of modeling in innovation. Int. J. Eng. Educ. 28, 263--269.Google Scholar
- Mobasher, A. A., Jalloh, A. R., Rojas-Oviedo, R., Deng, Z. T., and Quian, C. 2002. Incorporating MATLAB in the mechanical engineering courses at Alabama A&M University. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’& Exposition (ASEE’’02).Google Scholar
- Morrell, D. 2007. Design of an introductory MATLAB course for freshman engineering students. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’07).Google Scholar
- Morris, P. J., Long, L. N., Haghighat, A., and Brady, M. L. 1996. Curriculum development in advanced computation. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’96).Google Scholar
- Narayanan, G. 2007. Teaching of essential MATLAB commands in applied mathematics course for engineering technology. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’07).Google Scholar
- Njoo, M. and De Jong, T. 1993. Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. J. Res. Sci. Teach. 30, 821--844.Google ScholarCross Ref
- NRC. 2011. Report of a workshop on the pedagogical aspects of computational thinking. National Research Council National Research Council of the National Academies, Washington, D.C.Google Scholar
- PITAC. 2005. Computational science: Ensuring America’s competitiveness. President’s Information Technology Advisory Committee (PITAC), 27.Google Scholar
- Reid, D. J., Zhang, J., and Chen, Q. 2003. Supporting sicentific discovery learning in a simulation environment. J. Comp. Assist. Learn. 19, 9--20.Google ScholarCross Ref
- Rosca, R. 2006. Learning MATLAB---Just-in-time for freshman year? In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’06).Google Scholar
- Rowe, C., Shiavi, R., and Tung, J. 2004. Incorporation of fourth generation computing environment into a freshman engineering program: An historical perspective. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’04).Google Scholar
- Sathyamoorthy, M. 2003. Integrating MATLAB in mechanics and structural analysis courses. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’03).Google Scholar
- Shiflet, A. B. 2002. Computer science with the sciences: an emphasis in computational science. SIGCSE Bull. 34, 40--43. Google ScholarDigital Library
- Soh, L. K., Samal, A., Scott, S., Ramsay, S., Moriyama, E., Meyer, G., Moore, B., Thomas, W. G., and Shell, D. F. 2009. Renaissance computing: An initiative for promoting student participation in computing. SIGCSE Bull. 41, 1, 59--63. Google ScholarDigital Library
- Stickel, M. 2011. Putting mathematics in context: an integrative approach using MATLAB. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’11).Google Scholar
- Sticklen, J., Amey, M., Eskil, T., Hinds, T., and Urban-Lurain, M. 2004. Application of object-centered scaffolding to introductory MATLAB. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’04).Google Scholar
- Tabak, I. 2004. Synergy: A complement to emerging patterns of distributed scaffolding. J. Learn. Sci. 13, 305--335.Google ScholarCross Ref
- Tew, A. E. and Guzdial, M. 2011. The FCS1: A language independent assessment of CS1 concepts. In Proceedings of the 42nd SIGCSE Technical Symposium on Computer Science Education (SIGSCE’11). 111--116. Google ScholarDigital Library
- Thomassian, J. C., Kumazawa, R., and Kinnicutt, P. 2007. A study of freshmen students’ outlook to media based tutorials of MATLAB/Java in computing for engineers. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’07).Google Scholar
- Thornton, K. and Asta, M. 2005. Current status and outlook of computational materials science education in the U.S. Model. Sim. Mat. Sci. Eng. 13, R53--R69.Google ScholarCross Ref
- Thornton, K., Nola, S., Garcia, R. E., Asta, M., and Olson, G. B. 2009. Computational materials science and engineering education: A survey of trends and needs. J. Min. Met. Mater. Soc. 61, 12--17.Google ScholarCross Ref
- Urban-Lurain, M., Amey, M., Sticklen, J., Hinds, T., and Eskil, T. 2004. curricular integration of computational tools by evolutionary steps. In Proceedings of the American Society for Engineering Education Annual Conference & Exposition (ASEE’’04).Google Scholar
- Van Joolingen, W. R., De Jong, T., and Dimitrakopoulout, A. 2007. Issues in computer supported inquiry learning in science. J. Comp. Assist. Learn. 23, 111--119.Google ScholarCross Ref
- Veermans, K., Van Joolingen, W., and De Jong, T. 2006. Use of heuristics to facilitate scientific discovery learning in a simulation learning environment in a physics domain. Int. J. Sci. Educ. 28, 341--361.Google ScholarCross Ref
- Wankat, P. C. 2002. Improving engineering and technology education by applying what is known about how people learn. J. SMET Educ.: Innov. Res. 3, 3--8.Google Scholar
- Winn, W. 2002. Research into practice: Current trends in educational technology research: The study of learning environments. Educ. Psychol. Rev. 14, 331--351.Google ScholarCross Ref
- Zacharia, Z. C. 2007. Comparing and combining real and virtual experimentation: An effort to enhance students’ conceptual understanding of electric circuits. J. Comp. Assist. Learn. 23, 120--132.Google ScholarCross Ref
Index Terms
- Introducing Discipline-Based Computing in Undergraduate Engineering Education
Recommendations
Undergraduate Computational Science and Engineering Education
It is widely acknowledged that computational science and engineering (CSE) will play a critical role in the future of the scientific discovery process and engineering design. However, in recent years computational skills have been deemphasized in the ...
Interdisciplinary approaches to revitalizing undergraduate computing education
"Through the CISE Pathways to Revitalized Undergraduate Computing Education (CPATH) program, NSF's Directorate for Computer and Information Science and Engineering (CISE) is challenging its partners - colleges, universities and other stakeholders ...
Issues in undergraduate education in computational science and high performance computing
Supercomputing '94: Proceedings of the 1994 ACM/IEEE conference on SupercomputingThe ever increasing need for mathematical and computational literacy within our society and among members of the work force has generated enormous pressure to revise and improve the teaching of related subjects throughout the curriculum, particularly at ...
Comments