Development of ODL in a newly industrialised country according to face-to-face contact, ICT, and e-readiness
Bibliographic record
Abstract
A large number of unqualified and under-qualified in-service teachers are holding back socio-economical development in South Africa, a newly industrialized country. Open and distance learning (ODL) provides an innovative strategy and praxis for developing and newly industrialized countries to reach their educational and socio-economical objectives through professional development and training. In order to examine factors which affect the success of ODL offered by the North-West University in South Africa, a qualitative and quantitative research approach is used. Factors examined include face-to-face classroom contact, the implementation and use of ICTs, and e-readiness. The relationships between these factors are also discussed. A questionnaire was administered to 87 teacher-students in four Advanced Certificate in Education (ACE) programs to collect quantitative data regarding aspects of their classes and the e-readiness of students. This data was qualitatively elaborated upon by three semi-structured, open-ended focus-group interviews. Besides descriptive statistics, Spearman’s rank-order correlations (r) were determined between variables pertaining to negative feelings towards face-to-face classroom contact, ODL as students’ choice of delivery mode, and students’ positive attitude towards information and communication technology (ICT). Combined quantitative and qualitative findings were used to evaluate the effectiveness of contact classes as well as the e-readiness of students towards the attainment of ODL development Phase D. This phase refers to UNESCO’s description of ICT implementation, integration, and use. Relationships (Spearman’s rank-order correlations) between ODL, as teacher-students’ choice of educational delivery mode, and aspects of their e-readiness suggest that the e-readiness of teacher-students is implicit to their choice of ODL as educational delivery mode for professional development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".