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University of Toronto's Dental School Shows “New Teeth”: Moving Towards Online Instruction

2008· article· en· W2344163240 on OpenAlexaffabout
Florin D. Salajan, G. Mount

Bibliographic record

VenueJournal of Dental Education · 2008
Typearticle
Languageen
FieldDentistry
TopicDental Research and COVID-19
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlackboard (design pattern)InteractivityMedical educationBrainstormingCurriculumSet (abstract data type)MultimediaDentistryComputer scienceMathematics educationPsychologyMedicinePedagogy

Abstract

fetched live from OpenAlex

This article presents the approach the Faculty of Dentistry at the University of Toronto employed to modernize its methods of instruction by using online technologies. A small team of faculty, students, and content developers was assembled to work with individual faculty members to brainstorm and research ideas for innovative teaching practices in dental studies. The team was not content to simply post digital versions of the ubiquitous PowerPoint lectures in Blackboard, selected in 2006 by the University of Toronto as its sole platform for online course delivery, but rather set out to introduce interactivity with the course material. Consequently, a series of interactive applications was created, such as the virtual microscope in Oral Pathology, the 3D cavity preparations in Restorative Dentistry, and the Master Media Repository. During the summer of 2006, the Faculty of Dentistry made progress toward becoming one of the university's front-runners in online course innovation. The result of this collaboration between faculty members and the team was ten courses with interactive online presence, representing approximately 20 percent of the undergraduate curriculum. Since the summer of 2006, the Faculty of Dentistry has continued to pursue its goal of providing meaningful online instruction in all of its courses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.024
GPT teacher head0.326
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2008
Admission routes2
Has abstractyes

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