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Record W3044486641 · doi:10.21914/anziamj.v61i0.15052

University STEM students' perceptions of creativity in non-routine problem-solving

2020· article· en· W3044486641 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueANZIAM Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
FundersTeaching and Learning Research Initiative
KeywordsCreativityMathematics educationCritical thinkingConstructivePerceptionSubject (documents)Creative problem-solvingPsychologyComputer scienceLibrary scienceProcess (computing)Social psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.332
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it