ADVANCING STUDENT MOTIVATION AND COURSE INTEREST THROUGH A UTILITY VALUE INTERVENTION IN AN ENGINEERING DESIGN CONTEXT
Bibliographic record
Abstract
Understanding and improving student motivation is critical for educators because motivation is essential for academic success. Student motivation is multifaceted and complex with interest as one of many factors related to motivation and motived behavior. Student interest in course material can be supported by helping them understand the value and relevance of the material to their life. Within an expectancy-value framework, one aspect for understanding students perceived task value is to assess their perception of the utility value, or their view of the usefulness, of the task to their present or future goals. One way to encourage value is to have students write about the relevance of the course material to their life through structed utility value interventions. This study will compare the performance, interest, and motivation between students who participated in structured utility value interventions and those in a control group who did not during a third year multidisciplinary engineering design course. Initial indications are that students’ interest and motivation increase when given the utility value intervention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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, 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".