Online Education During a Pandemic – Adaptation and Impact on Student Learning
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
Universities and educational institutions worldwide had to abruptly suspend their in-person classes and offer the rest of the term in an online for-mat. This adjustment meant that instructors had to switch their instruction format and redesign their assessment strategies to ensure good quality edu-cation. In this work, we present the methods used in two courses for this transition and the impact on student learning. Specifically, we present data from two courses: second-year engineering mathematics and first-year object-oriented programming. The online instruction was delivered covering all the objectives, and the online assessment environment was designed with all possible safeguards to maintain integrity. Our data from these assessments show that the measures were successful. Further, the data indicate that while the pandemic severely impacted the first-year students, the second-year students did not experience any learning issues in the transition. We also present the lessons learned for future improvement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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