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Record W1595502741 · doi:10.19173/irrodl.v13i1.968

Challenges of virtual and open distance science teacher education in Zimbabwe

2012· article· en· W1595502741 on OpenAlexvenueno aff
Vongai Mpofu, Tendai Samukange, Lovemore Kusure, T. Zinyandu, Clever Denhere, Shakespear Ndlovu, Renias Chiveya, Monica Matavire, Leckson Mukavhi, Isaac Gwizangwe, Elliot Magombe, Nyakotyo Huggins, Munyaradzi Magomelo, Fungai Sithole, Chingombe Wiseman

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDistance educationMedical educationOpen educationQuality (philosophy)PsychologyPedagogyMathematics educationMedicine

Abstract

fetched live from OpenAlex

This paper reports on a study of the implementation of science teacher education through virtual and open distance learning in the Mashonaland Central Province, Zimbabwe. The study provides insight into challenges faced by students and lecturers on inception of the program at four centres. Data was collected from completed evaluation survey forms of forty-two lecturers who were directly involved at the launch of the program and in-depth interviews. Qualitative data analysis revealed that the programme faces potential threat from centre-, institution-, lecturer-, and student-related factors. These include limited resources, large classes, inadequate expertise in open and distance education, inappropriate science teacher education qualifications, implementer conflict of interest in program participation, students’ low self-esteem, lack of awareness of quality parameters of delivery systems among staff, and lack of standard criteria to measure the quality of services. The paper recommends that issues raised be addressed in order to produce quality teachers.

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)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.124
GPT teacher head0.516
Teacher spread0.393 · 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 designNot applicable
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

Citations26
Published2012
Admission routes1
Has abstractyes

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