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Record W2056663272 · doi:10.5539/ijps.v2n1p38

Analysis of the psychological impact of Problem Based Learning (PBL) towards self directed learning among students in undergraduate medical education

2010· article· en· W2056663272 on OpenAlexvenueno aff
Srikumar Chakravarthi, Priya Mankara Vijayan

Bibliographic record

VenueInternational Journal of Psychological Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAutodidacticismPsychologyCurriculumMedical educationProblem-based learningScale (ratio)Self-controlMathematics educationDevelopmental psychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

The psychological impact of a learning tool towards self directed learning is an important outcome of medical education. Although problem based learning is believed to facilitate self directed learning, previous studies have reported conflicting results. This longitudinal survey explored the perceived psychological changes in self directed learning for two and a half years in an undergraduate phase 1 medical education program with an integrated problem-based learning curriculum. 170 of 200 students (response rate, 85%) completed the Self-Directed Learning Readiness Scale at five different time points: at the beginning of each semester year and at program completion. Scores were significantly lower during the first semester compared with other years, and fifth semester scores were significantly higher than in previous years. Scores on the three subscales (i.e., self-management, desire for learning, and self-control) increased significantly during the five semesters years of the programme. These findings support self-directed learning as a maturational process seen psychologically by the students. Implications for medical faculty and curriculum development are discussed.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.035
GPT teacher head0.471
Teacher spread0.436 · 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

Citations25
Published2010
Admission routes1
Has abstractyes

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