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Record W2115214695 · doi:10.5430/jct.v4n2p62

An Analysis of a First-Year Class on the Self-Efficacy of University Students in Japan

2015· article· en· W2115214695 on OpenAlexvenueno aff
Kimie Shin, Masafumi Nakagami

Bibliographic record

VenueJournal of Curriculum and Teaching · 2015
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPersuasionSelf-efficacyClass (philosophy)PsychologyMathematics educationMechanism (biology)Social psychologyComputer science

Abstract

fetched live from OpenAlex

This study aimed to examine the effect of the first-year class in Japanese universities on developing students’self-efficacy. An analysis of the class design based on the theory of self-efficacy implies that such courses areprimarily intended to develop students’ self-efficacy by putting them through enactive mastery experiences. Inaddition, the courses are designed to use intensive cooperative learning to provide vicarious experiences, which actas a mechanism to trigger a synergy effect on students’ enactive mastery experiences during the course. The courseswere also designed to utilize a social persuasion mechanism to support students engaged in enactive masteryexperiences. Statistical analysis reveals that the students in these first-year courses have improved some aspects ofself-efficacy during the course period, consistent with the course design.

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)0.002
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.034
GPT teacher head0.368
Teacher spread0.334 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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