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Record W2046255612 · doi:10.5539/jel.v1n1p1

Study Strategies Are Associated with Performance in Basic Science Courses in the Medical Curriculum

2012· article· en· W2046255612 on OpenAlexvenueno aff
John A. McNulty, David C. Ensminger, Amy Hoyt, Arcot J. Chandrasekhar, Gregory Gruener, Baltazar Espiritu

Bibliographic record

VenueJournal of Education and Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
FundersLoyola University Chicago Stritch School of Medicine
KeywordsConstructiveMathematics educationRote learningPsychologyCurriculumTeaching methodPedagogyComputer scienceCooperative learningProcess (computing)

Abstract

fetched live from OpenAlex

We investigated the study strategies of first and second year medical students and tested the associations between study habits and performances in their basic science courses. Upon completion of every basic science course, students completed a survey ranking the study strategies they utilized throughout each course. Results of a principle component analysis showed that study strategies clustered into one of three study factors: “rote” learning, “constructive” learning, or “review” learning. Each of these study factors comprised related study strategies. Students tended to use “constructive” strategies predominantly, but altered their study habits based on content delivered in specific courses. Trends emerged indicating negative correlations for “rote” learning and course performance whereas there were positive correlations for “constructive” learning and course performance. Courses where “constructive” learning had the greatest effects also tended to have the greatest number of questions that required “constructive” reasoning on the final exam.

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)0.019
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.306
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.078
GPT teacher head0.459
Teacher spread0.381 · 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 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

Citations12
Published2012
Admission routes1
Has abstractyes

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