Optimized Cohort Creation for Hybrid Online Design-learning During COVID-19
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".