Bibliographic record
Abstract
Mathematics anxiety has adverse effects on the mathematical performance of school children.Studies on mathematics anxiety have revealed that school children from primary to secondary levels exhibit mathematics anxiety.The symptoms of mathematics anxiety are similar to those of anxiety disorders, which highlights the implications of mathematics anxiety on the well-being of school children.This research aimed to assess the effects of mathematics anxiety on the mathematical performance of school children and explore the factors affecting mathematics anxiety and the academic performance of school children in mathematics.For this purpose, this research reviewed the studies from the past five years, ranging from 2018-2022.The study concluded that mathematics anxiety adversely affects the performance of school children.The study examined the different theories which explained the association of mathematics anxiety with the performance of school children, suggesting that there is a bi-directional relationship in which high mathematics anxiety adversely influences the academic performance of school children, and their inferior performance further exacerbates their mathematics anxiety.School children from elementary to higher levels of schooling exhibited mathematics anxiety, which negatively affected their mathematical performance.Various factors, including cognitive, environmental and familial factors, were found to influence the development of mathematics anxiety and mediate the influence of anxiety based on mathematics on the academic achievement of school children.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".