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Record W4361980668 · doi:10.54691/bcpep.v9i.4603

The Impact of the Project-Based Learning Method on Students

2023· article· en· W4361980668 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBCP Education & Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTeamworkProblem-based learningProject-based learningMathematics educationPsychologyWork (physics)Active learning (machine learning)Teaching methodComputer scienceEngineeringPolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

With the development of science and technology, Problem-based Learning (PBL) has gradually attracted people's attention, which has become an important issue about how to enable students to develop more. This paper mainly analyzes the previous studies to illustrate the different effects of PBL model education on students, especially when being compared with traditional model education methods. This paper not only examines the effects of PBL, but also the underlying reasons of these effects. Results show that PBL positively impacts students compared with traditional education. Also, it can improve students' active thinking, hands-on, and teamwork cooperative ability. These positive effects can help students adapt to future work and project learning. Meanwhile, the reason for these positive effects is the change in PBL and traditional teaching methods.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.506
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.065
GPT teacher head0.558
Teacher spread0.493 · 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