Using Newman’s Error Analysis to Analyse Grade 3 Learners’ Errors in Solving Word Problems in a Diverse Classroom
Bibliographic record
Abstract
This study focused on utilising Newman’s error analysis model to diagnose the errors made by Grade 3 learners when solving word problems in a diverse classroom setting. The research endeavour seeks to pinpoint the errors manifested by learners when tackling word problems, with the aim of offering practical approaches to bolster the instruction of word problem-solving abilities and foster a more profound comprehension of mathematical principles among Grade 3 learners in varied educational settings. A diagnostic test with seven question items was administered to 54 learners in one primary school of Waterberg district in the Limpopo Province of South Africa. Consecutively, eight scripts of learners who featured the same errors were identified and they were included in semi-structured interviews to establish the cause of the errors. The outcomes unveiled that learners commit reading, comprehension, transformation, process and encoding errors. Based on the results, early identification of errors from learners and making them known is recommended as it will allow teachers to rectify them and promote learning with understanding.
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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 | 0.014 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".