Engaging Creative Media Students’ Motivation: The Influence of Autonomy, Peer Relationships, and Opportunities in the Industry
Bibliographic record
Abstract
Motivating students in creative media courses can be a challenge due to the demand for creativity which is hard to betaught. Hence, motivation needs to be re-identified and re-addressed for the creative disciplines. Conventionally,creative media courses adopt the studio-based learning, and with this unique dynamic teaching approach, students arerequired to have face-to-face tutorial sessions with their tutors on a regular basis, as well as participate in groupprojects and produce creative artefacts of industry standard quality. In this paper, we investigate the criticalmotivators for creative media students and identify those factors throughout the study. The study aims to examinehow crucial and influential the autonomy, peer relationship and the future career opportunities for students’motivation. Research includes a survey with questions based on a conceptual framework adopted fromself-determination theory. The results suggest that autonomy, peer relationships and the opportunity for future careersare the primary motivators for students in the programme. The implications of the findings are discussed, andrecommendations are provided to faculty members in the creative programmes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".