Structured Flexibility in Assessment: Students’ Perceptions of the Impact of Different Elements of Choice on Their Decision Making, Engagement, and Learning Experiences
Bibliographic record
Abstract
Research on the learning- and engagement-related impacts of choice in assessment is growing, though it often focuses on a single kind of choice with insufficient attention to the learning priorities and motivations behind students’ decisions. The assessment design explored here offered flexibility to students by providing multiple kinds of choice while maintaining the critical structure of a single type of assignment, which was necessary to preserve learning outcomes and not overwhelm students. This study investigated the impact of choice not only on students’ perceptions of their learning and engagement but also on their decision making process in order to better understand their motivations and the influences of learning priorities and individual circumstances. Guided by a mixed methods approach involving a survey followed by interviews, we found that offering choice in assessment increased students’ perceived autonomy and control, ability to balance other demands, quality of work, and enjoyment and interest in the work, while decreasing their stress levels. We also found that offering multiple elements of choice in a single assessment supported students’ abilities to make decisions based on their complex and diverse learning priorities, motivations, and individual circumstances. This study, as well as research on flexible assessment more broadly, respond to the International Society for the Scholarship of Teaching and Learning’s grand challenges, which include a call for more investigation into student engagement in learning and the complex processes of learning.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".