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Record W2964979215 · doi:10.29309/tpmj/2019.26.08.3875

Learning strategies of dental undergraduates of orthodontics and prosthodontics.

2019· article· en· W2964979215 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Professional Medical Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsMedicineMedical educationScale (ratio)ProsthodonticsSession (web analytics)Descriptive statisticsDentistryOrthodonticsStatisticsComputer science

Abstract

fetched live from OpenAlex

It is very important for faculty members to know how students learn so that they can modify teaching methods accordingly. To measure the learning preferences of dental undergraduates at Faisalabad Medical University, Pakistan. Study Design: A Cross-sectional study. Setting: Orthodontic Department, Dental Section- Faisalabad Medical University, Faisalabad. Period: Session 2017-18. Materials and Methods: Present study was conceived on the final year dental undergraduates (n=40) of Faisalabad Medical University, Pakistan to determine the learning preferences. Questionnaire was administered using Felder and Soloman’s Index of Learning Styles. The descriptive statistics were applied and survey data were converted in to scores. Results: The results showed that most of the undergraduate dental students were verbal learners (50%). On the sequential/global scale, 55% were balanced and 40% were sequential learners. On the active/reflective scale, 45% were balanced, and 30% were active. On the sensing/intuitive scale, 50% were balanced, and 38% were sensing. Conclusion: The undergraduate dental students were found to be mostly verbal learners.

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.

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)0.001
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 score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.345
Teacher spread0.329 · 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