Information and Communication Technology (ICT) Skills and the Teaching of Mathematics in Selected South African Schools
Bibliographic record
Abstract
This paper explores the use of ICT skills to promote the teaching of Mathematics in South African selected secondary schools. The paper deployed a qualitative research approach with a phenomenological case study design. The lived experiences of eight purposively selected teachers from four public schools in Buffalo Metropolitan City, East London, South Africa were recorded using semi-structured, in-depth interviews. The recorded data was coded, transcribed and subjected to thematic analysis. The findings revealed that there is a need for on-the-job training in the form of continuing professional teacher development (CPTD) to update their ICT skills. Such training can be workshops, seminars, short courses and symposiums. Also, teachers should be intentional in demonstrating true professionalism; they should innovatively engage ICT to help learners make accurate decisions, solve their problems and enhance different skills and there is a need for emotional and psychological support for the teachers. The paper concludes and recommends that introducing ICT tools in the Curriculum and Assessment Policy Statements (CAPS) document is not sufficient on its own without the monitoring of the subject heads to make sure teachers adhere to the use. The school management should make sure that these tools are available or improvised to make the learning environment conducive to the use of ICT tools.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".