Learners’ Mathematical Personal Identities and Mathematics Learning
Bibliographic record
Abstract
I explore and describe LMPIDs and their implications for learners’ achievements in mathematics and its learning. A qualitative research method was employed with a purposeful sample from one school in Gauteng Province, South Africa. Ten mathematics learners and three mathematics teachers were interviewed, and the learners’ parents completed questionnaires. It became evident that mathematics learners’ mindsets, beliefs, and attitudes are not isolated traits. They are socially constructed and shaped through relationships with others. Learners’ mindsets influence their attitudes either positively or negatively. These mindsets substantially affect how learners engage with mathematics, their level of competence and determination, their level of excitement, as well as their confidence, whether they find mathematics interesting or dull, challenging or easy. These attitudes shape how they relate to mathematics. All these qualities impact the development of LMPIDs, which in turn influence their achievements. High-achieving mathematics learners are those who receive the necessary support to excel in mathematics and related learning. I offer recommendations, supported by the data, for effective mathematics learning and achievement in Gauteng Province, South Africa, and beyond.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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".