University STEM students' perceptions of creativity in non-routine problem-solving
Bibliographic record
Abstract
The primary purpose of this study is to investigate students' perceptions about the characteristics of creativity and engagement in solving non-routine problems. It involved 64 science, technology, engineering, and mathematics (STEM) university students, who participated in a two-year research project in New Zealand during which participants were given opportunities to utilise puzzle-based learning in their courses. Comparing open-ended responses of two surveys, this article focuses on student perceptions about attributes of creativity in non-routine problem-solving. These results have pedagogical implications for tertiary stem education. References A. J. Baroody and A. Dowker. The development of arithmetic concepts and skills: Constructive adaptive expertise. Routledge, 2013. URL https://www.routledge.com/The-Development-of-Arithmetic-Concepts-and-Skills-Constructive-Adaptive/Baroody-Dowker/p/book/9780805831566. S. A. Costa. Puzzle-based learning: An approach to creativity, design thinking and problem solving. implications for engineering education. Proceedings of the Canadian Engineering Education Association (CEEA), 2017. doi:10.24908/pceea.v0i0.7365. N. Falkner, R. Sooriamurthi, and Z. Michalewicz. Teaching puzzle-based learning: Development of transferable skills. Teach. Math. Comput. Sci., 10(2):245–268, 2012. doi:10.5485/TMCS.2012.0304. A. Fisher. Critical thinking: An introduction. Cambridge University Press, 2011. URL https://www.cambridge.org/us/education/subject/humanities/critical-thinking/critical-thinking-2nd-edition/critical-thinking-introduction-2nd-edition-paperback?isbn=9781107401983. E. C. Fortes and R. R. Andrade. Mathematical creativity in solving non-routine problems. The Normal Lights, 13(1), 2019. URL http://po.pnuresearchportal.org/ejournal/index.php/normallights/article/view/1237. P. Gnadig, G. Honyek, and K. F. Riley. 200 puzzling physics problems: With hints and solutions. Cambridge University Press, 2001. URL https://www.cambridge.org/us/academic/subjects/physics/general-and-classical-physics/200-puzzling-physics-problems-hints-and-solutions?format=AR&isbn=9780521774802. J. P. Guilford. Creativity: Yesterday, today and tomorrow. J. Creative Behav., 1(1):3–14, 1967. doi:10.1002/j.2162-6057.1967.tb00002.x. J. P. Guilford. Characteristics of Creativity. Illinois State Office of the Superintendent of Public Instruction, Springfield. Gifted Children Section, 1973. URL https://eric.ed.gov/?id=ED080171. G. Hatano and Y. Oura. Commentary: Reconceptualizing school learning using insight from expertise research. Ed. Res., 32(8):26–29, 2003. doi:10.3102/0013189X032008026. S. Klymchuk. Puzzle-based learning in engineering mathematics: Students\T1\textquoteright attitudes. Int. J.Math. Ed. Sci. Tech., 48(7): 1106–1119, 2017. doi:10.1080/0020739X.2017.1327088. B. Martz, J. Hughes, and F. Braun. Developing a creativity and problem solving course in support of the information systems curriculum. J. Learn. High. Ed., 12(1):27–36, 2016. URL https://files.eric.ed.gov/fulltext/EJ1139749.pdf. Z. Michalewicz, N. Falkner, and R. Sooriamurthi. Puzzle-based learning: An introduction to critical thinking and problem solving. Hybrid Publishers, 2011. B. Parhami. A puzzle-based seminar for computer engineering freshmen. Comp. Sci. Ed., 18(4):261–277, 2008. doi:10.1080/08993400802594089. URL http://www.informaworld.com/openurl?genre=article&id. G. Polya. How to solve it: A new aspect of mathematical method. Princeton University Press, 2004. URL https://press.princeton.edu/books/paperback/9780691164076/how-to-solve-it. M. A. Runco. Creativity: Theories and themes: Research, development, and practice. Elsevier, 2014. URL https://www.elsevier.com/books/creativity/runco/978-0-12-410512-6. A. H. Schoenfeld. Mathematical problem solving. Elsevier, 2014. URL https://www.elsevier.com/books/mathematical-problem-solving/schoenfeld/978-0-12-628870-4. C. Thomas, M. Badger, E. Ventura-Medina, and C. Sangwin. Puzzle-based learning of mathematics in engineering. Eng. Ed., 8(1):122–134, 2013. doi:10.11120/ened.2013.00005. M. O. J. Thomas. Developing versatility in mathematical thinking. Med. J. Res. Math. Ed., 7(2):67–87, 2008. A. Valentine, I. Belski, and M. Hamilton. Developing creativity and problem-solving skills of engineering students: A comparison of web and pen-and-paper-based approaches. Eur. J. Eng. Ed., 42(6):1309–1329, 2017. doi:10.1080/03043797.2017.1291584. G. Wallas. The art of thought. Solis Press, 1926.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".