Challenges of Implementation of e learning in Mathematics, Science and Technology Education (MSTE) in African schools: A Critical Review
Bibliographic record
Abstract
This paper discusses the general ICT challenges in education and poses questions, the attempt of whose answers establish the manner in which e-learning technology could be appropriate for understanding and communicating the structures of mathematics and science. Challenges in understanding mathematics and science arise out of the interaction between these two intertwined yet disparate disciplines. While mathematical proof is established deductively and hence conclusive and not amenable to confutation in a logically possible world, scientific truth is established inductively on probable yet utilitarian grounds in the actual world. While challenges in implementation of the understanding of mathematics and science through technology arise from social and infrastructural issues related to ICT in African environment, the difficulty posed by challenges of communicating the principles of understanding the structure of mathematics and science are not yet insurmountable. An attempt to bring into coherence the mathematical and scientific understanding through e-learning instructional paradigm in quasi-philosophic terms is the main subject of this paper.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".