Optimized Cohort Creation for Hybrid Online Design-learning During COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The unprecedented global pandemic COVID-19 significantly disrupted the higher education sector by forcing educators to rethink modes of content delivery.As COVID-19 restrictions slowly lifts, many institutions are operating a hybrid course delivery structure: online lectures and small groups of in-person, hands-on learning sessions.In this paper, a method to model student cohort learning communities is proposed.This model would limit viral spreading through its small and static nature, while promoting a sense of community and identity-building.A similar learning community model was implemented within a 2 nd year Integrated Learning Stream pilot program.The goal of this study is to identify the optimal student cohort configuration, based on an anonymized dataset of 81 electrical engineering students' Fall 2020 semester enrollment records.Three very large scale integrated (VLSI) circuit clustering algorithms (Hyperedge Coarsening, Modified Hyperedge Coarsening, and Best Choice) are implemented.The resulting cohorts are evaluated based on cohort members' number of possible interactions external to their cohort.The Best Choice algorithm yielded more uniform cohorts that are less connected with other clusters, showing the cohort model to be a viable method of grouping students to limit cross-cohort transmission.Post-pandemic, the proposed method can be applied in many cohort-based learning use cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it