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Record W1967198162 · doi:10.1109/ism.2006.71

Engagement with Online Curriculum Materials and the Course Test Performance of Medical Students

2006· article· en· W1967198162 on OpenAlexaff
Tyrone Donnon, Claudio Violato, Jean‐Gaston DesCôteaux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCurriculumMedical educationTest (biology)Online courseVariance (accounting)Course (navigation)Computer sciencePsychologyMathematics educationMedicineEngineeringPedagogy

Abstract

fetched live from OpenAlex

The purpose of this study was to explore the use of an online curriculum system by first year medical students and its relationship to academic performance. The course test performance and online use of course based materials and resources were monitored for 114 first year medical students on a continuous basis from August 15, 2003, to May 31, 2004. Repeated measures of analysis of variance and multiple regressions were used to assess differences in use online and test performance. All of the first year medical students showed dramatic fluctuations in online use of curriculum materials dictated by the seven regularly scheduled course examinations. The relationship between online study patterns and examination performance, however, was found not to be statistically significant. In conclusion, the patterns of online use of curriculum materials by first year medical students is not consistent, but varies dramatically as a function of the assessment process

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.010
GPT teacher head0.329
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2006
Admission routes1
Has abstractyes

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