Teachers’ preparedness for implementing the Educational Coding and Robotics curriculum in South Africa
Bibliographic record
Abstract
It is a significant step that the Department of Basic Education has taken to integrate educational coding and robotics into the mainstream school curricula in a world where technology is a normal element of everyday life. A deeper examination of the schools' and teachers' readiness to adopt this new curriculum is significant to improve learning and boost active teaching techniques. The article aimed to examine the teachers’ preparedness, interests, knowledge, and self-confidence in implementing the newly introduced learning area – Educational coding and robotics in the mainstream school curriculum. This study was framed in the Unified Theory of Acceptance and Use of Technology. Using a systematic review approach, articles obtained from PubMed, Embase, Google Scholar, Scielo, Scopus, and ERIC were critically analyzed to identify descriptive themes and analytical themes. The review showed that the attitudes of South African teachers on ECR hinge on the availability of resources, pedagogical computer skills, teachers’ technological beliefs, and the management team's influences on technology. DBE needs to work closely with the teachers’ training institutions and pedagogical experts to meet the needs of teachers and learners regarding educational coding and robotics curriculum. Engagement with teachers may increase their knowledge, skills, attitudes, and values for them to have a meaningful contribution to the educational coding and robotics curriculum.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".