Crossing the Chasm – Introducing Flexible Learning into the Botswana Technical Education Programme: From Policy to Action
Bibliographic record
Abstract
This paper reports on a longitudinal, ethnomethodological case study of the development towards flexible delivery of the Botswana Technical Education Programme (BTEP), offered by Francistown College of Technical & Vocational Education (FCTVE). Data collection methods included documentary analysis, naturalistic participant observation, and semi-structured interviews. The author identifies and analyses the technical, staffing, and cultural barriers to change when introducing technology-enhanced, flexible delivery methods. The study recommends that strategies to advance flexible learning should focus on the following goals: establish flexible policy and administration systems, change how staff utilization is calculated when flexible learning methodologies are used, embed flexible delivery in individual performance development and department/college strategic plans, ensure managerial leadership, hire and support permanent specialists, identify champions and share success stories, and address issues of inflexible organisational culture. This study may be of value in developing countries where mass-based models are sought to expand access to vocational education and training.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".