A systematic review of secondary students’ attitudes towards mathematics and its relations with mathematics achievement
Bibliographic record
Abstract
For a significant number of students, attitudes towards mathematics decrease notably during secondary education. Thus, there is an urgent need to improve students’ mathematics attitudes because attitudes may negatively affect conceptual understanding of mathematics or mathematics performance. However, without a clear unified construct of mathematics attitudes, the ambiguity surrounding this construct prevents researchers from drawing broad conclusions about how to improve students’ overall mathematics attitudes. Therefore, we conducted a systematic review of 95 studies focused on mathematics attitudes to clarify the construct and measurement of mathematics attitudes, and to provide a holistic picture of the relations between mathematics attitudes and math achievement. The review suggested the adoption of a multidimensional definition that regards mathematics attitudes as a combination of specific mathematical cognitions (value, gender roles/beliefs, confidence, self-concept), affects (enjoyment, anxiety), and behavioural intentions (i.e., willingness and tendency to spend more time learning mathematics subjects). The review then explored the relations between each subdimension of attitudes and mathematics performance. In general, anxiety and gender roles were negatively correlated with mathematics performance (r = -.27 to -.48; -.21) whereas enjoyment, self-concept, confidence, perceived value, and behavioural intentions were positively related to achievement (r = .27 to .68; .21 to .76; .34 to .42; .11 to .30; .21 to .34, respectively). Thus, mathematics attitudes appear to comprise three components with several subdimensions that each uniquely contribute to mathematics achievement. Going forward, researchers of mathematics attitudes should a) specify the components of mathematics attitudes used to guide their investigation b) adopt measures in line with their chosen components, and c) investigate how each subdimension of mathematics attitudes uniquely and cumulatively contribute to mathematics ability.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".