Impacts of Course Culture on Student Creativity Development in Engineering Course Projects
Bibliographic record
Abstract
This full paper is unique and fits the innovative practice strategy category because it explores effective strategies for promoting creativity among engineering students in course projects in a teaching and learning culture where professors are supportive, caring, and connected to their students. Establishing a supportive and caring relationship between professors and students, where students are comfortable making mistakes and seeking assistance, can further enhance students' sense of ownership and motivation in completing their projects, leading to better engagement and learning outcomes. At the same time, critical thinking skills and academic rigor are requirements in engineering courses and must be addressed in all projects. The paper also emphasizes the importance of self-learning experiences, which can help students stay current with emerging technologies and develop the necessary knowledge, skills, and attitudes for success in a rapidly evolving technological landscape. Establishing a supportive and caring relationship between professors and students has been shown to improve the teaching quality of professors, creating a cycle of positive change. Professors who care about student success frequently reflect on their teaching and seek new methods and strategies to increase student success. Environmental factors, as well as personal attributes, have been shown to enable creative teaching practices. Innovative teaching occurs when a professor incorporates strategies and processes that encourage cognition. Professors who display self-efficacy, are experienced, and reflect on their teaching are seen as creative. They create supportive environments designed to encourage, nurture, and value creativity. They seek out teaching strategies that support these goals. This paper explores the use of specific strategies identified to support creativity, including open-ended questions, facilitating brainstorming, fostering collaboration, incorporating real-world problems, encouraging experimentation, and helping with hands-on learning. Educators can use these strategies to create an environment that encourages students to think outside of the box, experiment with new ideas, and collaborate. Students in this program report experiencing an encouraging environment providing a comfortable atmosphere for risk taking, and increased motivation to learn based on firsthand knowledge of work environments. Best practices from various course projects and senior project design courses at a medium-sized higher education institution are showcased. Hopefully, this experience will offer valuable insights, particularly to new faculty members.
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.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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, unvalidatedLabeled directly by 2 models reading the full record.
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".