Discovering online learning barriers: survey of health educational stakeholders in dentistry
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
OBJECTIVES: Given the exponential explosion of online learning tools and the challenge to harness their influence in dental education, there is a need to determine the current status of online learning tools being adopted at dental schools, the barriers that thwart the potential of adopting these and to capture this information from each of the various stakeholders involved in dental online learning (administrators, instructors, students and software/hardware technicians). The aims of this exploratory study are threefold: first, to understand which online learning tools are currently being adopted at dental schools; second, to determine the barriers in adopting online learning in dental education; and third, to identify a way of better preparing stakeholders in their quest to encourage others at their institutions to adopt online learning tools. METHODS: Seventy-two participants representing eight countries and 13 stakeholder groups in dentistry were invited to complete the online Survey of Barriers in Online Learning Education in Health Professional Schools. The survey was created for this study but generic to all healthcare education domains. Twenty participants completed the survey. RESULTS: demonstrated that many online learning tools are being successfully adopted at dental schools, but computer-based assessment tools are the least successful. Added to this are challenges of support and resources for online learning tools. Participants offered suggestions of creating a blended (online and face-to-face) tutorial aimed at assisting stakeholders to help their dental schools in adopting online learning tools CONCLUSION: The information from this study is essential in helping us to better prepare the next generation of dental providers in terms of adopting online learning tools. This paper will not only provide strategies of how best to proceed, but also inspire participants with the necessary tools to move forward as they assist their clients with adopting and sustaining online learning tools and models.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| 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