Self-Directed Learning Through Computer-Aided Mathematics Instruction: First-Year Teacher Education Experience
Bibliographic record
Abstract
Objective: The study investigates first-year teacher education students’ self-directed learning through Computer-Aided Mathematics Instruction (CAMI).Methods: A total of 230 first-year mathematics teachers specialising in Further Education and Training (FET) phase teaching participated in the study, where responses from 50 student teachers were purposively and conveniently selected to report on in this paper. A qualitative research method approach was used and open–ended questionnaires were utilised to collect the data for first-year teacher education students’ self-directed learning. The questionnaires were analysed using descriptive data analysis.Results: Results of the study revealed that CAMI was used to monitor students’ learning, the time the learning takes place, the performance of the student within the duration of time, and to evaluate student performance. The results also revealed the skills that characterised self-directed learning and active learning where the student teachers were motivated to learn more and to solve difficult problems in mathematics.Conclusions: The study recommends technology integration, such as CAMI, in teacher education and teaching and learning in the Higher Education Institutions (HEIs), to promote self-directed learning and support effective learning for future learners.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".