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GENDER DIFFERENCES IN UNIVERSITY STUDENTS' PERCEPTIONS OF AND CONFIDENCE IN PROBLEM-SOLVING ABILITIES

2014· article· en· W2010381027 on OpenAlexaff
Shelly L. Wismath, Maggie Zhong

Bibliographic record

VenueJournal of Women and Minorities in Science and Engineering · 2014
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPerceptionPsychologySelf-confidenceTest (biology)Confidence intervalLow ConfidenceMathematics educationGender gapMedical educationDevelopmental psychologySocial psychologyStatisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

Problem-solving skills are crucial components of an education for the 21st century, and confidence and self-efficacy have been shown to be critical to the development and practice of such skills. In a study of perceptions of and confidence in problem-solving abilities by students enrolled in a university course specifically focused on teaching problem-solving skills, we found significant gender differences in perceived confidence and ability. The average score for all students (male and female) on these indicators increased significantly from the pre-test to post-test. However, female students ranked themselves much lower in both confidence and abilities at the start of the course than male students, but also showed a remarkably larger increase in these indicators by the end of the course. This result confirms the necessity of and potential for helping female students develop confidence in their problem-solving abilities. Courses designed to improve problem-solving skills in university students can increase the confidence and abilities of both male and female students, while decreasing the gap between the genders.

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)0.001
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 score0.007
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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