Assessing the Impact of Voice‐Over Screen‐Captured Presentations Delivered Online on Dental Students’ Learning
Bibliographic record
Abstract
The traditional lecturing method is still one of the most common forms of delivering content to students in dental education, but innovative learning technologies have the potential to improve the effectiveness and quality of teaching dental students. What challenges instructors is the extent to which these learning tools have a direct impact on student learning outcomes. The aim of this study was to assess the impact of a voice-over screen-captured learning tool by identifying a positive, nil, or negative impact on student learning as well as student engagement (affective, behavioral, and cognitive) when compared to the traditional face-to-face lecture. Extraneous variables thought to impact student learning were controlled by the use of baseline measures as well as random assignment of second-year dental students to one of two teaching conditions: voice-over screen-captured presentation delivered online and the traditional classroom lecture. A total of 28 students enrolled in the preclinical course in endodontics at a Canadian dental school participated in the study, 14 in each of the two teaching conditions. The results showed that, in most cases, the students who experienced the online lecture had somewhat higher posttest scores and perceived satisfaction levels than those in the face-to-face lecture group, but the differences did not achieve statistical significance except for their long-term recognition test scores. This study found that the students had comparable learning outcomes whether they experienced the face-to-face or the online lecture, but that the online lecture had a more positive impact on their long-term learning. The controls for extraneous variables used in this study suggest ways to improve research into the comparative impact of traditional and innovative teaching methods on student learning outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".