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Record W4411774413 · doi:10.5430/jct.v14n3p70

Students’ Motivation to Learn Science Subjects in Saudi Universities: Gender Dynamics

2025· article· en· W4411774413 on OpenAlexvenueno aff
Saad Zafir Alshehri

Bibliographic record

VenueJournal of Curriculum and Teaching · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsDynamics (music)Mathematics educationPsychologyMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

This study aimed to explore the motivational factors influencing university students’ engagement with science subjects in Saudi Arabia, with a particular focus on gender dynamics. Grounded in self-determination and social cognitive theories, the research employed a descriptive-correlational design to examine whether motivational levels differ between female and male students and to assess the relationship between gender and motivation toward learning science. Data were collected using an adapted version of the Students’ Motivation Questionnaire. The findings revealed no significant differences in overall motivation levels between genders, nor a strong association between gender and motivation to study science. However, female students demonstrated greater confidence in managing their motivation, behavior, and performance compared to their male peers. Perceived competence was similar across genders, and self-determination did not emerge as a key motivational factor. These findings suggest that while gender differences in motivation may be minimal, female students' self-regulatory strengths warrant further attention. Additional research is recommended to explore the underlying factors that discourage women from pursuing careers in science.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.303
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.323
Teacher spread0.310 · 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.

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
Published2025
Admission routes1
Has abstractyes

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