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Record W4388427299 · doi:10.15700/saje.v43n3a2152

An empirical review of a hybrid teacher education programme: Lessons from South Africa

2023· review· en· W4388427299 on OpenAlexaff
Folake Ruth Aluko, Tony Mays, Hendri Kruger, Mary Ooko

Bibliographic record

VenueSouth African Journal of Education · 2023
Typereview
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersDepartment of Higher Education and TrainingUniversity of Pretoria
KeywordsBlended learningInformation and Communications TechnologyPerspective (graphical)Work (physics)PedagogyDistance educationSociologyQualitative researchValue (mathematics)Empirical researchPublic relationsPolitical scienceEducational technologyComputer scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

Scholars have recommended hybrid learning to combat education problems in emerging economies due to their challenging contexts. It potentially offers a means to address growing demand without sacrificing quality or increasing costs. In this article we report on a new “hybrid” distance teacher education programme in which we sought to address the requirements of new policies (both institutional and national) by combining the blended and distance education approach. We adopted a pragmatic qualitative approach, rooted in a communitarian perspective and distance education theory. Although progressing slower than expected, the programme’s implementation to date has provided lessons that bolster the value of blended learning theory and practice in a hybrid model. The study also highlighted the critical role that the mode adopted for teacher training can play in shaping teachers’ practice. However, to work more effectively in an emerging economy, a more substantial teaching presence is suggested, coupled with modularised and ongoing information and communication technology (ICT) training and support for staff and students as areas for further research.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
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 head0.135
GPT teacher head0.466
Teacher spread0.331 · 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 designOther design
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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