MétaCan
Menu
Back to cohort
Record W3115099729 · doi:10.5430/ijhe.v10n3p88

Problem-Solving Skills as a Mediator Variable in the Relationship between Habits of Mind and Psychological Hardiness of University Students

2020· article· en· W3115099729 on OpenAlexvenueno aff
Mohamed Sayed Abdellatif, Mervat Azmi Zaki

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsHardiness (plants)PsychologyScale (ratio)CurriculumSocial psychologyDevelopmental psychologyMathematics educationPedagogy

Abstract

fetched live from OpenAlex

This study investigated the role of problem-solving skills as a mediator variable in the relationships between habits of mind and the psychological hardiness among university students, and to identify the difference between male and female students in each of the habits of mind, psychological hardiness, and problem-solving skills. The sample comprised of 285 male and female, third-year Faculty of Education, University students. The data collection utilized the habits of mind scale, the problem-solving scale, and the psychological hardiness scale (Mekhemer, 1996). SPSS v.25 and AMOS v.24 were used to process data. The findings revealed that problem-solving skills partially mediates the relationship between habits of mind and psychological hardiness, and the results also demonstrated that there are no statistically significant differences between male and female students in habits of mind, problem-solving skills, and psychological hardiness. Future research suggestions include planning stakeholders at the university stage should take into consideration the necessity to integrate habits of mind and problem-solving skills in curricula, and providing training for faculty members to enhance university students' psychological hardiness.

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)0.007
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.072
GPT teacher head0.434
Teacher spread0.362 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicProblem Solving Skills DevelopmentFrench-language works237,207