Mathematics anxiety: history, theories, causes, and interventions
Bibliographic record
Abstract
This dissertation examined the evolution, theory, and interventions for mathematics anxiety and is presented as a collection of four academic papers examining (University of Alberta, n.d.), experimenting with, and reflecting on mathematics anxiety. The focus of the first paper was to examine how mathematics anxiety has evolved including its definition and how it affects working memory. The outcome of that work resulted in a new theory explaining why some people experience mathematics anxiety. This examination of mathematics anxiety theory is presented in Chapter 2 of this dissertation and is the first of the\nfour papers comprising this dissertation. The focus of the second paper was to experiment with writing as a form of intervention to alleviate mathematics anxiety. Participants in this empirical study were all female preservice teachers who were randomly selected to three groups to engage in neutral, expressive, or positive expressive writing. There were no significant differences between the three groups but there was a significant difference between pre-service teachers’ mathematics ability and their mathematics anxiety. Also, the analysis of participants’ journal writing alerted a need to examine the duration of the writing intervention. This research on mathematics anxiety with pre-service teachers is presented in Chapter 3 of this dissertation and is the second paper in this dissertation which has been published. The focus of the third paper was on women who were registered in a return-to-work \nprogram and the purpose of this empirical study was to examine the benefits of expressive writing as an intervention for mathematics anxiety. These participants were also women. The analysis of the focus group and interview data revealed that participants benefitted from the expressive writing. An unanticipated outcome of this study focused on the connection between mathematics anxiety and test anxiety. This work is presented in Chapter 4 of this dissertation and has been submitted for publication. The fourth paper is presented in chapter 5 and is written in the form of a thought paper (Snell, n.d.), and represents the concluding chapter in this dissertation. This chapter draws together the literature, theories, and all findings presented in this dissertation as well as recent work in the field of mathematics anxiety. More specifically, this chapter highlights the complexity and multi-dimensional nature of mathematics anxiety and posits whether mathematics anxiety can be separated from test anxiety. This paper has also been published. The final chapter contains a reflection on my positionality as an adult educator, lifelong learner, and researcher. In this chapter, I describe how my learning has come full-circle.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".