Identifying the Predictors of Mathematics Anxiety AND Performance in Canada: An Educational Data Mining Approach
Bibliographic record
Abstract
Over the last decade, Canadian students have exhibited insubstantial improvements in mathematical scores compared to other countries as indicated by large-scale educational assessments such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). In relation to students’ mathematical performance, math anxiety - the feeling of fear or nervousness when performing math-related tasks - was found as an associated factor. However, no previous study has explored math performance and math anxiety, specifically among Albertan students. We present a work-in-progress that identifies significant predictors of math performance and math anxiety among Canadian and Albertan students, using the PISA 2018 and TIMSS 2019 datasets. This study has three phases: first, a list of predictors will be selected from the data set based on existing theories regarding students’ math performance and math anxiety. The initial list of predictors will be presented to domain experts (i.e., math teachers) for refinement based on their practical experience. A predictive model for math performance and math anxiety will be developed with Educational Data Mining techniques in the second phase. Results from the model will be presented to the domain experts for their inputs as the qualitative component, and variable importance metrics of the model will be consulted for the quantitative component. Findings from both components will be integrated consulted with the domain experts to derive actionable recommendations that would inform various stakeholders (e.g., educators, school districts, and Alberta Education) of ways to improve math performance in Alberta students.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".