Evaluating the association between attitudes toward mathematics and mathematics achievements using multilevel modeling: A comparison of TIMSS 2019 scores for fourth and eighth grade students in Ontario and Quebec
Bibliographic record
Abstract
Using multilevel modeling, this study examined the relationship between attitudes toward mathematics (ATM) and mathematics achievement, while controlling for gender, socioeconomic status (SES), and class average SES, for Canadian grade 4 and grade 8 students using data from IEA’s Trends in International Mathematics and Science 2019 study (TIMSS). Four separate samples, grade 4 Ontario, grade 8 Ontario, grade 4 Quebec, and grade 8 Quebec, were compared to determine the effect of different age groups and context (i.e., provinces/education system) on the relationship between ATM and mathematics achievement. Consistent with the hypothesis, individual level mathematics achievement positively predicted enjoyment, emotions, self-concept, and perceived value of mathematics, and negatively predicted boredom for both grade 4 and grade 8 students. However, class level achievement showed more complex relationships with different components of ATM. For grade 4 students, the class average mathematics achievement negatively predicted only self-concept, demonstrating the big-fish-little-pond-effect (BFLPE; Marsh & Parker, 1984). For grade 8, the class level mathematics achievement negatively predicted enjoyment, emotions, and self-concept, while positively predicted boredom, demonstrating not just a BFLPE but also a happy-fish-little-pond-effect (HFLPE; Pekrun et al., 2023). A significant gender gap was also found; male students had higher enjoyment, emotions, and self-concept than female students across samples. The relationship between individual and class average SES and ATM showed inconsistent trends but, broadly, individual and class average SES did not influence ATM. One way to enhance mathematics achievement in an equitable approach is to improve ATM in the classroom. These findings have implications for both individual classroom instructions (and therefore teacher education programs) and board-level policies regarding mathematics instructions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".