Dental Undergraduate Students’ Perceptions of Blended Learning in the COVID-19 and Post–COVID-19 Years: Survey Study
Bibliographic record
Abstract
BACKGROUND: The outbreak of the COVID-19 pandemic created significant challenges but also a unique opportunity, accelerating the evolution of higher education, including dental education. This encouraged dental education to adopt more flexible modes like blended learning. OBJECTIVE: This study aimed to explore senior undergraduate dental students' views on blended learning during and after the COVID-19 pandemic and to identify modifiable factors influencing their engagement. METHODS: A survey was conducted among final-year undergraduate students at a top-ranking dental school in mainland China during the fall semesters of 2020-2021 and 2023-2024. The survey assessed satisfaction with blended learning, preferences for engagement, strengths compared to purely online or offline teaching, and factors influencing engagement during and after the pandemic. RESULTS: Response rates were 75% (85/114) in 2020 and 73% (47/64) in 2023. Blended learning was used in 53% (26/49) of evaluated courses. High satisfaction was reported by 82% (93/114) in 2020 and 59% (38/64) in 2023, with significant differences between high- and low-satisfaction groups (P<.001). Satisfaction with specific course types and learning activities was analyzed. Factors associated with higher satisfaction were evaluated using Pearson correlation. Students acknowledged the strengths of blended learning over online- or offline-only formats. In total, 70% (80/114) in 2020 and 61% (39/64) in 2023 expressed a desire to participate in blended dental education. Factors decreasing engagement included unstable technical support (68/114, 60% in 2020 vs 26/64, 41% in 2023), poor online-offline integration (58/114, 51% vs 34/64, 53%), lack of motivation (51/114, 45% vs 24/64, 38%), and insufficient teacher-student interaction (44/114, 39% vs 20/64, 31%). Factors increasing engagement included high-quality learning materials (76/114, 67% vs 43/64, 67%) and improved technical environments (62/114, 54% vs 35/64, 55%). CONCLUSIONS: Final-year dental students were generally satisfied with blended learning and recognized its strengths compared to purely online or offline formats, both during and after the pandemic. More efforts are required to enhance students' potential engagement in blended learning for the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".