Greater Realism in Authentic Assessments Promotes Student Motivation and Engagement
Bibliographic record
Abstract
Student motivation is an important predictor of both performance and attitudes toward schoolwork. Higher levels of intrinsic, or autonomous, motivation are facilitated by high-impact teaching practices, including experiential learning and using authentic experiences and evaluations. The present study was inspired by instructor perception that students in their third semester in a four-year undergraduate design program were more engaged with, and more motivated by, one course project over another. Although both projects were authentic assessments, the preferred project had more realism, including real external stakeholders and context. We assessed students’ subjective experience while working with two projects taught in the same course over two years, where the projects varied in level of realism. Phase 1 of the study measured students’ intrinsic motivation for the two projects using a questionnaire based on the Intrinsic Motivation Inventory. Phase 2 of the study again measured students’ intrinsic motivation for the two projects after the less-preferred project was adjusted to be more realistic. This study showed evidence that students experienced higher levels of engagement and intrinsic motivation when working with more realistic projects involving real external stakeholders and context, compared to a project with less realism. Projects with real problems, goals, and outcomes seem to give students a higher sense of autonomy, competence, and relatedness than fictitious ones—improving their self-regulation, engagement, and well-being.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".