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Record W2753271936 · doi:10.3138/jvme.0316-068r

Curricular Revision and Reform: The Process, What Was Important, and Lessons Learned

2017· article· en· W2753271936 on OpenAlexvenueno aff
Jan E. Ilkiw, Richard W. Nelson, Johanna L. Watson, Alan J. Conley, Helen E. Raybould, Munashe Chigerwe, Karen A. Boudreaux

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumClass (philosophy)Process (computing)Medical educationCurriculum developmentPedagogyPsychologyPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

Beginning in 2005, the Doctor of Veterinary Medicine program at the University of California underwent major curricular review and reform. To provide information for others that follow, we have documented our process and commented on factors that were critical to success, as well as factors we found surprising, difficult, or problematic. The review and reform were initiated by the Executive Committee, who led the process and commissioned the committees. The planning stage took 6 years and involved four faculty committees, while the implementation stage took 5 years and was led by the Curriculum Committee. We are now in year 2 of the institutionalizing stage and no longer refer to our reform as the "new curriculum." The change was driven by a desire to improve the curriculum and the learning environment of the students by aligning the delivery of information with current teaching methodologies and implementing adult learning strategies. We moved from a department- and discipline-based curriculum to a school-wide integrated block curriculum that emphasized student-centered, inquiry-based learning. A limit was placed on in-class time to allow students to apply classroom knowledge by solving problems and cases. We found the journey long and arduous, requiring tremendous commitment and effort. In the change process, we learned the importance of adequate planning, leadership, communication, and a reward structure for those doing the "heavy lifting." Specific to our curricular design, we learned the importance of the block leader role, of setting clear expectations for students, and of partnering with students on the journey.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.988
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.456
Teacher spread0.384 · 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.

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

Citations40
Published2017
Admission routes1
Has abstractyes

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