The relationship between undergraduate students’ mathematics anxiety and motivation to learn mathematics: a mixed method study
Bibliographic record
Abstract
This paper aims to investigate whether there is a relationship between math anxiety and motivation to learn math by a mix-method design. Statistical analysis was conducted utilising quantitative survey data, and a qualitative method was used to analyse two focus groups' data. The survey sample consisted of 116 undergraduate students from Universities in the United Kingdom, United States, Canada, Australia, and China. The academic Motivation Toward Mathematics Scale (AMTMS) and Single-Item Math Anxiety Scale (SIMA) were used as data collection tools. Descriptive analysis and correlation analysis were used for data analysis. The qualitative study sample consisted of 12 students divided into high and low-motivation groups. First-cycle and second-cycle coding methods with thematic analysis were conducted to generate themes. According to the study results, there was a negative relationship between mathematics anxiety and motivation. Specifically, the amotivation sub-scale had the strongest positive correlation with math anxiety. Conversely, the intrinsic regulation sub-scale had the strongest negative correlation with math anxiety. The focus group offered detailed insights that helped explain the results and provided subjective views on the relationships between the two variables. This study can aid educators in moderating students' math anxiety and motivation, developing effective teaching strategies, and promoting student math achievement. The study's qualitative results could also inform future research on the cause and effect of math anxiety and motivation.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".