Impact of the COVID-19 lockdown in Shanghai on student engagement at the college level
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
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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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 | 0.001 | 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 | 0.000 |
| 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