Reflections on mathematics ability, anxiety, and interventions
Bibliographic record
Abstract
Competency in mathematics is needed to respond to the vast employment opportunities available in the STEM sectors. These employment opportunities all require basic foundational mathematics skills, yet there is a shortfall of mathematics abilities due, in-part, to mathematics anxiety. Mathematics anxiety can surface as fear and avoidance of mathematics and has been linked to low mathematics performance and ability (Ashcraft, 2002; Luttenberger et al., 2018). This thought paper (Snell, n.d.), paper begins with a synthesis of research on mathematics anxiety including the known causal factors: cognitive/affective, social, and genetic as well as the recently proposed causal factor, missed opportunity (Brewster & Miller, 2020). Missed opportunity refers to cases where an individual who is capable academically to comprehend mathematics but has missed the opportunity to learn basic foundational skills in mathematics. Missing the opportunity to learn foundational concepts in mathematics places great stress, which can result in feelings of anxiety. Next, a synthesis of interventions for mathematics anxiety such as mindfulness exercises (Brunyé et al., 2013) and expressive writing (Brewster & Miller, 2022; Park et al., 2014) are discussed, which led to the realization that interventions are more complex than previously reported given that other factors can affect interventions such as duration of writing, quality of instruction, or additional stressors causing anxiety, including test anxiety. Knowing the causal factors influencing an individual’s mathematics anxiety may prove beneficial to designing more focused and influential interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".