Critical Success Factors in The Infusion of Instructional Technologies for Open Learning in Development Settings:
Bibliographic record
Abstract
This article seeks to identify critical success factors for the appropriate infusion of instructional technologies to advance open learning in higher education within developing settings. Describe here is a descriptive account of a two-year case study based on the author’s personal analysis of, and reflection on, factors that contributed to the infusion of instructional technologies to advance open learning at the University of Botswana. The first critical success factors identified in this article include: a clear vision, support of committed leadership, and dedicated personnel/ change agents to ensure successful project implementation. The second critical success factor identified was the need for all involved to fully appreciate and understand the systemic nature of the infusion of instructional technologies for open learning purposes, as well as garner the commitment of strategic partners working in related systems. Finally highlighted, are the requirements needed to address the complex nature of the infusion of instructional technologies into the University’s educational offerings. It is hoped that those involved in education in developing countries, and particularly those desirous of advancing open learning through the use of instructional technologies, will find this descriptive analysis useful. Indeed, those of us involved in implementing instructional technologies in developing nations are still in the initial stages of this exciting yet challenging endeavour. Key terms: infusion, open learning, instructional technologies, critical success factors, developing settings, Botswana, LASO model
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".