Application of DenTeach in Remote Dentistry Teaching and Learning During the COVID-19 Pandemic: A Case Study
Bibliographic record
Abstract
In December 2019, an outbreak of novel coronavirus pneumonia occurred, and subsequently attracted worldwide attention when it bloomed into the COVID-19 pandemic. To limit the spread and transmission of the novel coronavirus, governments, regulatory bodies, and health authorities across the globe strongly enforced shut down of educational institutions including medical and dental schools. The adverse effects of COVID-19 on dental education have been tremendous, including difficulties in the delivery of practical courses such as restorative dentistry. As a solution to help dental schools adapt to the pandemic, we have developed a compact and portable teaching-learning platform called DenTeach. This platform is intended for remote teaching and learning pertaining to dental schools at these unprecedented times. This device can facilitate fully remote and physical-distancing-aware teaching and learning in dentistry. DenTeach platform consists of an instructor workstation (DT-Performer), a student workstation (DT-Student), advanced wireless networking technology, and cloud-based data storage and retrieval. The platform procedurally synchronizes the instructor and the student with real-time video, audio, feel, and posture (VAFP). To provide quantitative feedback to instructors and students, the DT-Student workstation quantifies key performance indices (KPIs) related to a given task to assess and improve various aspects of the dental skills of the students. DenTeach has been developed for use in teaching, shadowing, and practice modes. In the teaching mode, the device provides each student with tactile feedback by processing the data measured and/or obtained from the instructor's workstation, which helps the student enhance their dental skills while inherently learning from the instructor. In the shadowing mode, the student can download the augmented videos and start watching, feeling, and repeating the tasks before entering the practice mode. In the practice mode, students use the system to perform dental tasks and have their dental performance skills automatically evaluated in terms of KPIs such that both the student and the instructor are able to monitor student's work. Most importantly, as DenTeach is packaged in a small portable suitcase, it can be used anywhere by connecting to the cloud-based data storage network to retrieve procedures and performance metrics. This paper also discusses the feasibility of the DenTeach device in the form of a case study. It is demonstrated that a combination of the KPIs, video views, and graphical reports in both teaching and shadowing modes effectively help the student understand which aspects of their work needs further improvement. Moreover, the results of the practice mode over 10 trials have shown significant improvement in terms of tool handling, smoothness of motion, and steadiness of the operation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".