Understanding Capacity in Creativity and Problem Analysis among Engineering Students: A Preliminary Study
Bibliographic record
Abstract
Insight into student understanding of their own learning is a key element in being able to enhance curriculum design and implement effective approaches in student-centred learning. In this paper we examine the findings of a preliminary study into student perceptions of their own capacity for thinking creatively and analysing problems. This preliminary study serves as a pilot to a larger joint University study between Queens University, Canada and the University of Adelaide involving design engineering students. Results of the pilot study will inform the conduct of a longitudinal study, projected to be administered over a three year period. The aim of the longitudinal study relates to three specific categories: to serve as a catalyst for the participants to develop further skills in reflective practice, necessary to self-regulated learning and self-efficacy; to obtain data that will provide insight into the ways in which students think, feel about, and perceive their own learning related to aspects of their studies in design engineering and; to contribute to the field of knowledge related to student perception of learning, self-regulation of learning, self-efficacy and the links to life-long learning.This paper presents the results of phase one of the pilot study. The focii of this investigation consists of a) trialling the instrument in an authentic course environment to test implementation and applicability of the survey including ease of understanding by the respondents, and b) collection and analysis of preliminary data on second year engineering student perceptions of their own learning related to creative thinking and problem analysis skills. Examination of these results discusses emergent themes and makes initial recommendations on curriculum enhancement as well as recommendations on survey instrument design and implementation relating to the ongoing study
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".