Flexible assessment: some benefits and costs for students and instructors
Bibliographic record
Abstract
Research on flexible assessment suggests that providing students with choice in assignments can increase motivation and deepen investment in learning. Although instructors are often advised to adopt flexible assessment, they are also warned about potential detriments such as perceived lack of rigour among colleagues, the stress that decision-making can bring to students, and increased workload for themselves. This paper draws upon student responses to a survey, a class discussion, and instructor observations to identify benefits and costs of flexible assessment in a fourth-year history course. Among the benefits are that students can pursue their interests more freely in both content and form, while the instructor can enjoy creative and original student work. The costs include anxiety among students who may be unsure how best to choose their assessments, and additional work for the instructor who must manage a multiplicity of assignments within the confines of an institutional grading system. The implementation of flexible assessment is recommended provided that the flexibility is compatible with the course’s learning outcomes, the students’ level of independence, and the instructor’s capacity to take on an unpredictable amount of extra work. Suggestions are offered for how to implement flexible assessment without creating too much of a burden for either students or instructors.
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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 | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".