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Record W2214391975 · doi:10.21083/ajote.v4i1.3012

COMPARISON OF SUPPORT INTERVENTIONS DURING A BLENDED COURSE FOR EDUCATORS FROM URBAN AND RURAL SETTINGS

2015· article· en· W2214391975 on OpenAlexvenueno aff
Juliet Stoltenkamp, Martha Kabaka

Bibliographic record

VenueAfrican Journal of Teacher Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionFacilitationMedical educationInformation and Communications TechnologyBlended learningFaculty developmentProgram Design LanguageProfessional developmentPsychologyEngineeringKnowledge managementPedagogyEducational technologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This research focused on the design and delivery of a blended Professional Development (PD) Program for in-service teacher-educators from both urban and rural settings. The overall purpose of the PD Program was to enhance the educators’ Information Communication Technologies (ICT) skills, with emphasis on eTools for supporting teaching-and-learning methodologies. Two groups of teacher-educators undertook the course. A strong facilitation and support approach was maintained throughout the PD Program to encourage self-directed learning. A case study approach was adopted to explore the experiences in the overall implementation and impact of the program. This article reflects on the findings regarding program design and structure; access to resources; impact time management; design of a support structure for the monitoring and evaluation of the program; and educators as self-directed learners using eTools to enhance teaching-and-learning methodologies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.072
GPT teacher head0.443
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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