Teaching and Learning Against all Odds: A Video-Based Study of Learner-to-Instructor Interaction in International Distance Education
Bibliographic record
Abstract
Distance education and information and communication technologies (ICTs) have been marketed as cost-effective ways to rescue struggling educational institutions in developing countries, particularly in sub-Saharan Africa (SSA). This study uses classroom video analysis and follow-up interviews with teachers, students, and local tutors to analyse the interaction at a distance between learners in Mali and Burkina Faso and their French and Canadian instructors. Findings reveal multiple obstacles to quality interaction: frequent Internet disconnections, limited student access to computers, lack of instructor presence, ill-prepared local tutors, student unfamiliarity with typing and computer technology, ineffective technical support, poor social dynamics, learner-learner conflict, learner-instructor conflict, and student withdrawal and resignation. In light of the near death of the costly World Bank-initiated African Virtual University (AVU), this paper concludes by re-visiting the educational potential of traditional technologies, such as radio and video, to foster development in poor countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".