Effects of Distance-Learning Strategies in Dental Fixed Prosthodontics Amidst the COVID-19 Pandemic: Cross-Sectional Questionnaire Study on Preclinical Dental Students’ Perspective
Bibliographic record
Abstract
BACKGROUND: COVID-19's high transmissibility led to gathering restrictions where dental schools experienced disruptions due to restrictions on attending in-person lectures and limitations placed on applied preclinical and clinical activities. Students not only had to rapidly switch to digital technology-based learning (TB-learning) modules but also experienced high levels of social isolation and anxiety around virus transmission. OBJECTIVE: This study aims to evaluate the preclinical students' perception of switching TB-learning modules amidst the COVID-19 pandemic, identifying which module parameters were associated with strong student outcomes. METHODS: A web-based survey of 39 Likert scale questions was delivered to preclinical dental students (Western University) to evaluate students' perceptions concerning TB-learning, fear amidst the COVID-19 pandemic, and the impact on their preclinical training. A Spearman rank correlation coefficient was determined to estimate the relationship between 2 variables in isolation (P=.01). An ordinal regression analysis was performed on variables of interest to determine how module variables (typically within the instructor's control) influenced the student outcomes (P=.05). RESULTS: The response rate was 30% (n=39). TB-learning was considered vital (34/39, 87.2%) as the students' education improved (18/39, 46.2%). However, 53.8% (n=21) of students showed increased difficulties in retaining, visualizing, or understanding the materials using TB-learning, and 64.1% (n=25) found it more difficult to concentrate than in in-person classes. In total, 79.5% (n=31) of students showed different levels of agreement about feeling fatigued from TB-learning. Through Spearman ρ correlation analysis, the quality of questions in quizzes (ρ=0.514; P<.001), relevant handouts (ρ=0.729; P<.001), and high-quality audiovisuals (ρ=0.585; P<.001) were positively correlated with students responding that the modules were useful to preclinical training. Similarly, good organization (ρ=0.512; P<.001), high-quality questions in quizzes (ρ=0.431; P=.01), and relevant handouts (ρ=0.551; P<.001) were positively correlated with web-based classes as an effective way to learn. In total, 91.6% (n=36) of the students agreed that COVID-19 was a dangerous disease, whereas 53.8% (n=21) showed different levels of agreement that they were afraid to be infected personally, and 69.2% (n=27) feared passing COVID-19 along to family and friends. A total of 82.1% (n=32) of the students showed that COVID-19 impacted their overall learning process and had a negative impact on their practical preclinical training (31/39, 79.5%). CONCLUSIONS: The students found a difference between TB-learning and face-to-face learning methods, where the students perceived fatigue toward the web-based method with difficulty concentrating and visualizing the subject. Moreover, there was a consensus that COVID-19 itself affected the students' overall learning process and preclinical training. As dental schools continue implementing TB-learning into their curriculum, this investigation identifies the students' struggles with the paradigm shift. In an effort to improve TB-learning, this work highlights 4 variables (organization, quizzes, quality handouts, and quality audiovisuals) within the control of instructors that can help improve content deliverance, improving the students' experience.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".