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Record W6968608855 · doi:10.5281/zenodo.15498843

'Assessing Students' Problem-Solving Skills and Self-Efficacy Belief through Posing-Exploring-Doing-Evaluating (PEDE) Model

2025· article· en· W6968608855 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsControl (management)Significant differenceQuarter (Canadian coin)Set (abstract data type)Knowledge retentionTreatment and control groups

Abstract

fetched live from OpenAlex

The study examined how the Posing-Exploring-Doing-Evaluating (PEDE) model affects students' problem-solving skills and self-confidence in math at Catumbalon National High School, Valencia City, Bukidnon, during the third quarter of the 2024-2025 school year. It used a quasi-experimental design with 31 students in the experimental group and 34 in the control group to address challenges in math proficiency identified by local and international assessments. Results showed that students who learned with the PEDE model scored higher on posttests and retention tests in problem-solving than those in the control group, with a significant difference in retention scores. However, self-efficacy (students' belief in their abilities) reached only a moderate level for the PEDE group, though there was a significant difference between groups before and after the study. The PEDE model effectively improves students' problem-solving skills and moderately boosts their self-confidence in math compared to traditional 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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.159
GPT teacher head0.413
Teacher spread0.253 · 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.

Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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