Co-Designing OER with Learners: A Replacement to Traditional College Level Assessments
Bibliographic record
Abstract
Academic integrity issues in higher education have been reported as increasing as the pandemic and need to learn remotely continues. The use of homework sites like Chegg, that provide learners with answers to tests and assignments increased significantly through 2019 and 2020 (Walsh et al., 2021). Open advocates have been espousing the benefits of open educational resource assignments co-constructed with learners and published in the open prior to the pandemic. These have largely been writing assignments taking the form of blogs with a focus on teaching practices. An example of this phenomenon is the Open Learner Patchbook where learners write blog posts to share in the open (Open Education Global, 2019). A faculty involved in two projects that co-designed Open Education Resources (OER) with learners was curious to know what processes learned could be applied to co-designing OER assignments in their own teaching practice as an alternative to traditional assessments where answers can be found on homework sites. Easton et al. (2019) propose that original assignments encourage learners to complete their own work. This presentation focuses on what was learned in the co-design process with learners and what can be applied to teaching practices in college diploma and certificate courses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".