ABSTRACT
The purpose of the present study was to investigate how the inclusion of computational creativity exercises (CCEs) merging computational and creative thinking in undergraduate computer science (CS) courses affected students' course grades, learning of core CS knowledge, self-efficacy, and creative competency. CCEs were done in lower- and upper-division CS courses at a single university. Students in CCE implementation courses were compared to students in the same courses in different semesters. Propensity score matching was used to create comparable groups (control and implementation) based on students' GPA, motivation, and engagement. Results showed that implementing CCEs in undergraduate CS courses enhanced grades, learning of core CS knowledge, and self-efficacy for creatively applying CS knowledge. However, CCEs did not impact creative competency. The effect of the CCEs was consistent across upper- and lower-division courses for all outcomes. Unlike previous studies that only established the support for CCEs, such as positive dosage effects, the results of this study indicate that CCEs have a causal effect on students' achievement, learning, and self-efficacy, and this effect is independent of general academic achievement, motivation, and engagement. These findings establish the CCEs as a validated, evidence-based instructional method.
- ACM/IEEE-CS Joint Task Force on Computing Curricula. (2013, Dec.). Com-puter Science Curricula 2013. {Online}. Available:Google Scholar
- A. Eck, L-K., Soh, and C. Brassil. 2013. Supporting active wiki-based collaboration. In Proc. CSCL'13 (Madison, WI), 176--183.Google Scholar
- A. J. Elliot, K/ Murayama, and R. Pekrun. 2011. A 3 X 2 achievement goal model. J. of Educ. Psychol., 103, 632--648.Google ScholarCross Ref
- R. Epstein. 1996. Cognition, creativity, and behavior: Selected essays. Praeger, Westport, CT.Google Scholar
- R. Epstein. 2005. Generativity theory and creativity. Theories of creativity. In Theories of Creativity, M.A. Runco and R.S. Albert, Eds. Hampton Press, Cresskill, NJ.Google Scholar
- R. Epstein, S. M. Schmidt, and R. Warfel. 2008. Measuring and training creative competencies: Validation of a new test. Creativity Res. J., 20, 7--12.Google ScholarCross Ref
- S. Guo, and M. W. Fraser. 2015. Propensity score analysis: statistical methods and applications. Sage Publications, Thousand Oaks, CA.Google Scholar
- M. Guzdial. 2008. Paving the Way for Computational Thinking. Commun. ACM, 51, 25--27. Google ScholarDigital Library
- J. Husman, and J. Hilpert. 2007. The intersection of students' perceptions of instrumentality, self-efficacy, and goal orientations in an online mathematics course. Zeitschrift für Pädagogische Psychologie/ German J. of Educ. Psychol., 21, 229--239.Google ScholarCross Ref
- J. Husman and D. F. Shell. 2008. Beliefs and perceptions about the future: A measurement of future time perspective. Learning and Individual Differences, 18, 166--175.Google ScholarCross Ref
- L. D. Miller, L.-K. Soh, V. Chiriacescu, E. Ingraham, D. F. Shell, and M. P. Hazley. 2013. Improving learning of computational thinking using creative thinking exercises in cs-1 computer science courses. In Proc. FIE'13 (Oklahoma City, OK), 1426--1432.Google Scholar
- L. D. Miller, L.-K. Soh, V. Chiriacescu, E. Ingraham, D. F. Shell, and M. P. Hazley 2014. Integrating computational and creative thinking to improve learning and performance in CS1. In Proc. SIGCSE'14 (SIGCSE'14), (Atlanta, GA), 475--480). Google ScholarDigital Library
- K. G. Nelson, D. F. Shell, J. Husman, E. J. Fishman, and L.-K. Soh. 2015. Motivational and self-regulated learning profiles of students taking a foundational engineering course. J. Eng. Educ., 104, 74--100.Google ScholarCross Ref
- R. Pekrun, A. Frenzel, T. Goetz, and R. P. Perry. 2007. The control value theory of achievement emotions: An integrative approach to emotions in education. In Emotion in Education¸ P.A. Schutz and R. Pekrun, Eds. Academic Press, San Diego, CA.Google Scholar
- M. S. Peteranetz, A. E. Flanigan, D. F. Shell, and L.-K. Soh. 2017. Computational creativity exercises: An avenue for promoting learning in computer science. IEEE Trans. on Ed., 99, 1--9.Google Scholar
- P. R. Pintrich. 2003. A motivational science perspective on the role of student motivation in learning and teaching contexts. J. of Educ. Psychol., 95, 667--686.Google ScholarCross Ref
- C. Senko, C. S. Hulleman. and J. M. Harackiewicz. 2011. Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions. Educ. Psychologist, 46, 26--47.Google ScholarCross Ref
- D. F. Shell, D. W. Brooks, G. Trainin, K. Wilson, D. F. Kauffman, and L. Herr. 2010. The Unified Learning Model: How Motivational, Cognitive, And Neuro-biological Sciences Inform Best Teaching Practices. Springer, the Netherlands.Google Scholar
- D. F. Shell, M. P. Hazley, L.-K. Soh, E. Ingraham, and S. Ramsay. 2013. Associations of students' creativity, motivation, and self-regulation with learning and achievement in college computer science courses. In Proc. FIE'2013 (Oklahoma City, OK), 1637--1643.Google Scholar
- D. F. Shell, M. P. Hazley, L.-K. Soh, E. Ingraham, and S. Ramsay. 2014. Impact of creative competency exercises in college computer science courses on students' creativity and learning. Presented at the Annual Meeting of the Amer. Educational Res. Assoc., (Philadelphia, PA), Apr. 3--7.Google Scholar
- D. F. Shell, M. P. Hazley, L.-K. Soh, L. D. Miller, V. Chiriacescu, and E. Ingraham. 2014. Improving learning of computational thinking using computational creativity exercises in a college CS1 computer science course for engineers. In Proceedings of the 44th Annual Frontiers in Education (FIE) Conference, (Madrid, Spain), 3029--3035.Google Scholar
- D. F. Shell, and J. Husman. 2008. Control, motivation, affect, and strategic self-regulation in the college classroom: a multidimensional phenomenon. J. of Educ. Psychol., 100, 443--459.Google ScholarCross Ref
- D. F. Shell, J. Husman, J. E. Turner, D. M. Cliffel, I. Nath, and N. Sweany. 2005. The impact of computer supported collaborative learning communities on high school students' knowledge building, strategic learning, and perceptions of the classroom. J. of Educ. Comput. Res., 33, 327--349.Google ScholarCross Ref
- D. F. Shell and L.-K. Soh. 2013. Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. J. of Sci. Educ. and Tech., 22, 899--913.Google ScholarCross Ref
- D. F. Shell, L.-K. Soh, A. E. Flanigan, and M. S. Peteranetz., 2016. Students' initial course motivation and their achievement and retention in college CS1 courses. In Proc. SIGCSE 2016 (Memphis, TN), 639--644. Google ScholarDigital Library
- D. F. Shell, L.-K. Soh, A. E. Flanigan, M. S. Peteranetz, and E. Ingraham. 2017. Improving Students' Learning and Achievement in CS Classrooms through Computational Creativity Exercises that Integrate Computational and Creative Thinking. In Proc. SIGCSE 2017 (Seattle, WA), 543--548 Google ScholarDigital Library
- L.-K. Soh, D. F. Shell, E. Ingraham, S. Ramsay, and B. Moore. 2015. Learning through computational creativity. Commun. ACM, 58, 8, 33--35. Google ScholarDigital Library
- J. Wing, 2010. Computational Thinking: What and Why. Link Magazine.Google Scholar
Index Terms
- Examining the Impact of Computational Creativity Exercises on College Computer Science Students' Learning, Achievement, Self-Efficacy, and Creativity
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