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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".