Math Anxiety in Early Elementary School in Ontario
Bibliographic record
Abstract
Math anxiety, which is defined as negative feelings such as tension, worry, or apprehension about math, is thought to negatively affect children in a myriad of ways. However, the majority of math anxiety research has been conducted with university students and adults, and less is known about math anxiety in the early years of formal education. Further, math anxiety research often fails to take into account important contextual factors such as instructional style and classroom environment that students are exposed to, thus omitting important considerations. The overall goals of this dissertation are (1) to contribute to knowledge in the area of math anxiety in early elementary school-aged students in terms of measurement of math anxiety and its relation to math performance, and (2) to mobilize research into practice by providing vital information directly to educators and clinicians to support their students with math anxiety. First, new questionnaire items are presented that more accurately represent the breadth of math learning experiences of Ontario students who receive a curriculum incorporating both traditional and inquiry-based pedagogy (Ontario Ministry of Education, 2020). Second, the relation between math anxiety and children’s math performance, is explored along with examining the effects of two potential moderators on this association: working memory and executive functioning skills. Lastly, the real-world practical implications for this research and the existing literature are synthesized into a question-and-answer style paper aimed for educators and clinicians working with early elementary school-aged children, to build their knowledge of math anxiety and offer an emotion-focused response style to math anxious students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".