Acceptance of E-Learning Devices by Dental Students
Bibliographic record
Abstract
BACKGROUND: E-Learning programs and their corresponding devices are increasingly employed to educate dental students during their clinical training. OBJECTIVE: Recent progress made in the development of e-learning software as well as in hardware (computers, tablet PCs, smartphones) caused us to more closely investigate into the habits of dental students in dealing with these learning techniques. METHODS: Dental students during their clinical training attended a survey compiled in cooperation with biostatisticians. The questionnaire probands were asked to complete based on previous surveys of similar subjects, allowing single as well as multiple answers. The data, which were obtained with respect to the learning devices students commonly employ, were compared with their internet learning activities. RESULTS: The e-learning devices utilized are of heterogeneous brands. Each student has access to at least one hardware type suitable for e-learning. All students held mobile devices, about 90 percent employed laptops, and about 60 percent possess smartphones. Unexceptional all participants of the survey acknowledged an unlimited internet access. In contrast, only 16 percent of students utilized tablet PCs. A detailed analysis of the survey outcome reveals that an increasing use of mobile devices (tablet PC, smartphone) facilitates internet learning activities while at the same time utilization of computers (desktop, laptop) declines. CONCLUSIONS: Dental students overwhelmingly accept e-learning during their clinical training. Students report outstanding preconditions to conduct e-learning as both their access to hardware and to the internet is excellent. Less satisfying is the outcome of our survey regarding the utilization of e-learning programs. Depending of the hardware employed only one-third to barely one-half of students comprise learning programs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".