Effectiveness of Online Education During the COVID-19 Pandemic: Evidence from Chinese Universities
Bibliographic record
Abstract
The COVID-19 pandemic triggered a global shift from face-to-face instruction to online learning, posing new operational challenges for business schools, particularly in equipping students with quantitative skills essential for the labor market. Leveraging China's abrupt lockdown policies as a natural experiment, we examine the heterogeneous effects of online education on student academic performance. Using a panel dataset of 15,329 observations from 7,867 undergraduate students across nine Chinese universities over four semesters (Fall 2018 to Spring 2020), we compare academic outcomes before and during the transition to online learning. We find that online education led to an average increase of 8–11 points in mathematics scores on a 100-point scale during the pandemic. Applying principal component analysis, we identify four key policy measures that capture lockdown stringency: Stay-at-home orders, workplace closures, public transportation suspension, and public information campaigns. Stricter stay-at-home orders issued by the government reduce the effectiveness of online learning; however, these negative effects are partially offset by increased parental supervision and reduced external distractions resulting from workplace closures and the suspension of public transportation. Further, online learning is more effective for reasoning-focused courses (e.g., mathematics) than interpretation-focused courses (e.g., English), and the academic benefits of face-to-face peer interactions diminish significantly in online settings relative to offline environments. Our findings offer actionable insights for managing educational delivery during operational disruptions, highlighting the importance of tailoring online curriculum design and support systems to course content and student mobility constraints.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".