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Record W4391173699 · doi:10.21083/ajote.v13i1.7535

Tumu College of Education trainee teachers’ perceptions of mentors’ pedagogical knowledge

2024· article· en· W4391173699 on OpenAlexvenueno aff
Shani Osman, Kassim File Dangor

Bibliographic record

VenueAfrican Journal of Teacher Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionPsychologyMathematics educationMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

The study accessed the perceptions of final year students in the Tumu College of Education towards the Pedagogic Knowledge (PK) of their mentors. It also investigated whether statistically significant differences existed in terms of mentees’ gender and programmes of study regarding the pedagogic knowledge of their mentors. The study used a census method to collect data from respondents for the study by distributing a closed-ended five-point Likert scale on Perceptions of Knowledge and Skills in Teaching (PKST) Questionnaires to all 215 students pursuing Early Grade and Primary Programmes, with an 84.2% (181) return rate. However, 175 respondents’ data were used, as six of the questionnaires contained incomplete data. Findings of the study revealed that participants perceived their mentors as having a high measure of PK, with an overall mean value for the student teachers ‘perceptions of their mentors PK of 3.62 (SD =.77). The study also revealed that there was no statistically significant difference in the perceptions of student teachers towards the PK of their mentors in terms of gender or programme of study. However, the study revealed that participants perceived their mentors to be less competent in effectively incorporating information and communication technology (ICT) in the classroom. It is recommended that the Ministry of Education and Ghana Education Service organise capacity building workshops for teachers to improve their competencies in integrating ICT in their classrooms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.416
Teacher spread0.360 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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