Self-Directed Learning Through Computer-Aided Mathematics Instruction: First-Year Teacher Education Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it