Intergenerational Transmission of Math anxiety: Discussion About Research of Parents’ and Children’s Math Anxiety
Bibliographic record
Abstract
Math is an abstract and challenging subject, so students may have math anxiety when studying math. Math anxiety might be transmitted intergenerationally. This article discusses research on math anxiety’s intergenerational transmission in three aspects. The first aspect is the factors of parents influencing children’s math anxiety levels. These factors are parents’ math anxiety, intelligence mindset, parent-child relationships, and parental educational involvement. The second aspect is the influence of parental math anxiety on children. Parental anxiety can influence children’s math anxiety, math outcomes, and how much math they learn, and it can affect children as early as kindergarten age. The third aspect is the methods and interventions reducing children’s math anxiety and improving outcomes. Stopping parents with high math anxiety levels, using math applications involving interaction between parents and children, changing fixed mindset to growth mindset, and doing mindfulness are all interventions that can help reduce children’s math anxiety. In conclusion, the intergenerational transmission of math anxiety is critical and represented by factors of parents influencing children’s math anxiety level and the influence of parental math anxiety on children. Parents should use appropriate ways to reduce their children’s math anxiety. Further research should focus on the cause-and-effect relationship between parents’ math anxiety and 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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".