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Record W4386574293 · doi:10.5539/jel.v12n6p82

Project-Based Learning in General Psychology Class for Undergraduate Students

2023· article· en· W4386574293 on OpenAlexvenueno aff
Wassana Na Sulong, Kanyakorn Sermsook, Oraya Sooknit, Wittaya Worapun

Bibliographic record

VenueJournal of Education and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
FundersRajamangala University of Technology Srivijaya
KeywordsPsychologyMathematics educationContext (archaeology)Class (philosophy)Educational psychologyActive learning (machine learning)Psychology of learningPedagogyApplied psychologyComputer science

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the effects of project-based learning on the learning achievement and satisfaction of undergraduate learners in General Psychology. The study included 30 undergraduate students enrolled in a general psychology class. The research utilized a project-based learning management plan, a learning achievement test, and a satisfaction questionnaire as the instruments. The results of the study revealed significant positive impacts of project-based learning on both the learning achievement and satisfaction of students in the context of general psychology. These findings highlight the effectiveness of project-based learning in enhancing students’ learning outcomes and overall satisfaction in the field of general psychology. The study contributes to the existing body of literature supporting the benefits of project-based learning as an instructional method for undergraduate education.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.051
GPT teacher head0.456
Teacher spread0.405 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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