Impact of the COVID-19 lockdown in Shanghai on student engagement at the college level
Bibliographic record
Abstract
Purpose Despite various similarities and successes of mainland China and other East Asian nations in containing the spread of disease and limiting the number of pandemic-related deaths compared to Western nations, their respective COVID-19 responses in the higher education sector varied significantly. We provide new insights into the emergency remote learning-driven integration of digital learning ecosystems in mainland China. Design/methodology/approach The findings of the quantitative survey with 126 valid responses and subsequent focus groups with students and staff provide various insights. Quantitative data are analyzed using descriptive statistics and combined with the analysis of responses within focus groups, drawing on sentiment analysis. Findings This paper presents evidence of emergency-driven integration of digital learning ecosystems and undergraduate students’ engagement under the COVID-19 pandemic-induced lockdown in Shanghai, China. Findings indicated a declining trend of engagement and motivation, combined with the absence of personal interaction, pronounced information overload and impact on learning skills. The sentiment from students provides refined evidence while academic staff reflected on student engagement and performance. Despite increasing online activity, in crisis management, the findings suggest the importance of instructors superseding all technological advancements. Originality/value These findings lend support to Kahu’s (2013) model of student engagement in higher education and extends it by the factor “online relationship to academics/information overload” as part of the psychosocial influences on university students’ engagement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".