Self-beliefs mediate mathematical performance between primary and lower secondary school: A large scale longitudinal cohort study
Bibliographic record
Abstract
It is often argued that enhancement of self-beliefs should be one of the key goals ofeducation. However, very little is known about the relation between self-beliefs and performance when students move from primary to secondary school in highly differentiated educational systems with early tracking. This large-scale longitudinal cohort study examines the extent to which academic self-efficacy (i.e., how confident students are that they will be able to master their schoolwork) and math self-concept (i.e., students’ perceived math competence) mediate the relation between math performance at the end of primary school (Grade 6) and the end of lower secondary school (Grade 9) in such a system. The study involved 843 typically-developing students in the Netherlands. Self-efficacy and math self-concept were measured with self-report questionnaires. Math performance was measured with nationally validated tests. The relation between math performance in Grade 6 and in Grade 9 was uniquely mediated by both self-efficacy in Grade 6 and math self-concept in Grade 9, but in opposing directions. Math self-concept was the most influential mediator, explaining nearly a quarter of the total effect of Grade 6 math performance on Grade 9 math performance. Unexpectedly, high self-efficacy in Grade 6 was negatively related to Grade 9 math performance, particularly for girls and high-track students. These findings suggest that self-efficacy may not necessarily be a protective factor in highly differentiated early tracking educational systems and may need to be actively managed when students move to secondary school.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".