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Record W2974406578 · doi:10.21083/ajote.v8i0.4611

Professionalism, Urban Settings, and Teachers’ Self-Efficacy in Developing Countries: A Ghanaian Perspective.

2019· article· en· W2974406578 on OpenAlexvenueno aff
Bernard Gumah, Nora Bakabbey Kulbo, Prince Clement Addo

Bibliographic record

VenueAfrican Journal of Teacher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-efficacyDemographicsPerspective (graphical)PsychologyRural areaDeveloping countryBridging (networking)Medical educationSociologySocial psychologyPolitical scienceEconomic growthMedicineDemography

Abstract

fetched live from OpenAlex

In achieving the goals of education, it is imperative for teachers to have high self-efficacy which has a direct positive effect on their delivery and for the overall benefit of their pupils. This study was in three-fold. First was to access the influence of teachers’ demographics on their self-efficacy. Second, how work environment influences teachers’ self-efficacy and finally, how their self-efficacy impact students’ performances in the Bolgatanga municipality of Ghana. The efficacy dimensions studied are classroom management practices, classroom instructional practices, and student engagement. It was noted that whiles gender has no significant impact on teachers’ self-efficacy, older, more educated and highly experienced teachers had higher self-efficacy. Also, teachers in the urban area tend to have higher self-efficacy than those in rural areas. Not overlooking other factors, students’ poor performance in some rural areas can largely be attributed to the lower self-efficacy of their teachers as compared to their urban counterparts. Governments should intensify their extrinsic motivation packages to make life more comfortable for teachers working in rural areas and by bridging the rural-urban developmental gap. It is also imperative to intensify self-efficacy in teacher trainees to increase their self-confidence where ever they find themselves.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.016
GPT teacher head0.327
Teacher spread0.312 · 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 designQualitative
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

Citations5
Published2019
Admission routes1
Has abstractyes

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