Implementing Alternative Assessment Strategies in Chemistry Amidst COVID-19: Tensions and Reflections
Bibliographic record
Abstract
The COVID-19 pandemic in the first quarter of 2020 resulted in the worldwide disruption of teaching and learning in main stream schools and in institutes of higher learning. Singapore was not spared. With the closure of schools in early April, it was imminent that the delivery and assessment of our freshman general chemistry course must be overhauled for the new semester. While the delivery of Home-based Learning (HBL) was a challenge for all educators, it was a mammoth roadblock for chemistry courses because of laboratory classes. Besides being thrusted to learn and use new technology tools for online lessons, instructors also had to quickly explore and design alternative assessments to substitute in-person written examinations and tests. This paper documents the struggles that played out in the decision to implement concept map assessments and “split-half” laboratory classes for safe distancing. Although these interventions are not novel, we confronted tensions as we sought to address academic integrity, administrative guidelines, and our own inadequacy particularly in concept map assessments. In light of positive and negative feedback from both staff and students, lessons were drawn to enhance future implementation and for further research.
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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 | 0.223 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.016 | 0.026 |
| 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".