The use of Children’sLiterature to Teach Mathematics to improve Confidence and Reduce Math Anxiety
Bibliographic record
Abstract
Mathematics is one of the core subjects that students at the elementary level need to have a firm foundation of. Unfortunately, it is often perceived by some elementary school teachers as difficult to teach. Likewise, many children have a negative view of the subject, and have difficulty learning the concepts. Research has found that teachers who have a negative perception of the mathematics often project these feelings onto their students \n \n(Anderson, 2007; Ma & Xu, 2004; Relich, 1996). The purpose of this research paper is to examine the impact of using children’sliterature to teach mathematics at the elementary level. To conduct this qualitative research I will interview three elementary teachers who are integrating children’s literature into their mathematics lessons to find out what strategies they are using and how they perceive children’s literature impacting students’ engagement with the mathematics curriculum.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".