Distance Learning Program for Teachers at The Kigali Institute of Education: An expository study
Bibliographic record
Abstract
In 2001, a program of distance learning was started within Kigali Institute of Education in collaboration with the Rwanda's Ministry of Education. It is an in-service training program that aims to upgrade in-service secondary school teachers and alleviate the shortage of teachers both in terms of quality and number. This program runs parallel to a pre-service program, also conducted within the Kigali Institute. Academic staff members working in the pre-service program are involved in this distance learning program. After three years, a descriptive qualitative case study was conducted to determine the experiences of academic staff involved in the distance learning program. Purposive and theoretical sampling was used for participants’ identification and inclusion. Individual unstructured interview and focus group discussion was used to gather the data. A qualitative software analysis called NVivo 2, developed by Qualitative Solutions and Research (QSR) International in 2002, was used to compile and analyse the data. Results of the study revealed that faculty members involved in both in-service and pre-service programs face challenges associated with heavy workload. Moreover, the pre-service program is typically prioritized at the expense of the distance learning in-service program. Academic relationships between faculty members and tutors also need to be reinforced. Serving as the critical link between the distance learning in-service program and pre-service departments and faculties, this research also shows that course coordinators play a pivotal role in the smooth operation of the distance learning program.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".