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Record W4379057447 · doi:10.21083/ajote.v12i1.7264

Pre-service Teachers’ Preparedness to Teach during Teaching Practice in Tanzania

2023· article· en· W4379057447 on OpenAlexvenueno aff
Hawa Mpate, Glenda Campbel-Evans, Jan Gray

Bibliographic record

VenueAfrican Journal of Teacher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTanzaniaPreparednessClassroom managementGeneral partnershipTeacher educationMedical educationFocus groupData collectionPsychologyMathematics educationPedagogyMedicineSociologyPolitical science

Abstract

fetched live from OpenAlex

Teachers are vital to the success of any education system. However, concern continues to be raised about the quality of teachers’ preparation in the teachers’ colleges and about the quality of teachers in schools in Tanzania (Global Partnership for Educational Support in Tanzania Mainland, 2013; Mgaiwa, 2018; Makoro, 2020). In line with such concerns, this study explored Tanzania’s pre-service teachers’ demonstration of knowledge and skills of teaching during teaching practice. The study was conducted in three secondary schools located in Moshi rural District in Kilimanjaro region in Tanzania. It involved five pre-service teachers from one of the Diploma Teachers Colleges, five supervisors from the same Teachers’ College and mentor teachers from the three home schools where pre-service teachers were placed for teaching practice. The study is descriptive, employing qualitative methods of data collection. Interviews, focus group discussions and observations were used to collect data. Drawing on Shulman’s (1986) categories of knowledge, data were analyzed thematically. The findings revealed that the pre-service teachers had limited skill in lesson preparation and classroom management, which negatively impacted their learning during teaching practice. Based on these findings, the study recommends that teacher education colleges should take steps to better prepare pre-service teachers to effectively handle lesson planning, teaching, and classroom management.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.382
Teacher spread0.359 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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