How Students’ Perceptions of Assignments that Help Them Learn Can Inform Course Design Decisions
Bibliographic record
Abstract
The assessments that instructors choose to implement suggest to students where they should focus their study efforts and can thus be leveraged to engage students in learning. However, engagement is also influenced by students’ perceptions of the inherent learning value of the assessments. These perceptions should therefore be taken into account when designing assessments. Our center for teaching and learning surveyed students at a large Canadian research-intensive university to learn about their perceptions of assignments that help them learn. The goal of the investigation was to gather information that could be used to inform course design decision-making and thus improve students’ learning experience. A thematic analysis of the 106 responses received indicate that students perceive assignments to be helpful when they are hands-on, involve problem solving, have real-world application, and allow flexibility. Through a content analysis, we identified 91 (86%) of the assignments as involving higher order thinking and 14 (13%) lower order. We also identified 29 (27%) of the responses as involving the adoption of values and attitudes. While a more nuanced approach to planning assessments is needed than just doing what students say helps them learn, our students’ responses provided local examples that our instructors and other course/instructional designers can draw on to plan relevant and meaningful assessments to support students with achieving course learning outcomes.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".