Frequent, low-stakes assessments: Balance and benefits
Bibliographic record
Abstract
COVID-19 prompted a shift in post-secondary education from largely co-operative face-to-face instruction to almost complete isolation with online learning. Within this transition, there was an increased recommendation for instructors to incorporate frequent, low-stakes assessments into their online practice. Frequent low-stakes assessments are assessments designed with the intention to increase student engagement and stimulate self-guided learning, benefits that would have been crucial during this independent learning period. However, this recommendation provided little specifics on criteria to design and implement these assessments. This lack of specifics, combined with a lack of SoTL literature on these assessments, prompted instructors to produce their own individual versions and applications of frequent, low-stakes assessments for their online practice. We’ve since transitioned back to instruction with mostly in-person elements, but the effect of these ambiguous instructions persist. The perception of frequent, low-stakes assessments and their usage differs among all instructors, consequently causing students to experience multiple versions of assessments per-week per-course. This experience has led to students becoming overwhelmed with an increased workload and an overall contradiction of the intended benefits of frequent low-stakes assessments. To address this concern, this project aims to gather institutional data and stakeholders’ (students and instructors) opinions to create an operational definition of frequent, low-stakes assessments, i.e. inform on strategies to implement these assessments into a balanced workload while preserving the intended benefits. This talk will specifically explore data gathered from Canadian institutional Offices of Teaching and Learning (or equivalent) on the regulations for the design and implementation of frequent, low-stakes assessments. The overall goal of this collection is to create a consensus of Canadian institutional criteria, that will then be merged with stakeholder data to create an operational definition.
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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 | 0.066 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".