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Record W7052684031

Students’ perceptions of their self-efficacy in mathematics in a public school in Kenya

2012· dissertation· en· W7052684031 on OpenAlexaboutno aff

Bibliographic record

VenueeCommons - AKU (Aga Khan University) · 2012
Typedissertation
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionScale (ratio)Intervention (counseling)Quarter (Canadian coin)Work (physics)Descriptive statistics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to find out students’ perceptions of their self-efficacy in mathematics. In the study, 104 form four students (83 boys and 20 girls) from a public rural coeducational school were surveyed using a modified version of Nielsen and Moore’s (2003) Mathematics Self-Efficacy Scale (MSES). Thereafter, eight students selected on the basis of their mean responses on the MSES were interviewed. Through descriptive statistics, it was found that students’ self-efficacy percepts in mathematics varied from 15 to 50 on a scale of 10 to 50. About one quarter of the students were found to be highly efficacious in secondary school mathematics. Meanwhile, boys were more efficacious than girls. At the same time, the variation in the strengths of students’ self-efficacy percepts in mathematics was attributed to the interpretations that the students gave to the various indicators of capability in mathematics that operated in their environment. It is recommended that teachers use the MSES to measure their students’ self-efficacy percepts in mathematics, and thereafter, use interviews to identify the underlying sources of the self-efficacy percepts in order to institute intervention strategies that will work best in their particular contexts so as to enhance students’ self-efficacy percepts in mathematics.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.227
Teacher spread0.211 · 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 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
Published2012
Admission routes1
Has abstractyes

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