Mathematically high and low performances tell us different stories: Uncovering motivation-related factors via the ecological model
Bibliographic record
Abstract
This study investigated how motivational factors contribute to math performance through the ecological model within exceptionally high and low achieving student populations. Using PISA 2018 data, a model including three layers of the ecological model were constructed to examine the ecological background of math performance for each group: exceptionally low & high achievers. Employing structural equation modeling, the results revealed that high math performance was ecologically associated with factors: attitudes towards competition, growth mindset, motivation to master tasks, self-efficacy, teacher enthusiasm, teacher feedback, teacher support, value of school, and parents' emotional support. However, low math performance was related to a wider range of factors, including the aforementioned variables, as well as enjoyment of reading and learning goals. This research emphasizes a practical viewpoint that suggests using different interventions to maximize the potential of students in various positions on the math ability spectrum since the factors differ in explaining mathematically high and low performance. In this study, we investigated motivation related factors that affect students with both high and low achievements in mathematics. Our results indicate that the factors associated with math performance differ between high and low achievers. This highlights the significance of need for differentiated educational strategies to maximize the potential of students across the math ability spectrum. This differentiation between the two groups may help in developing a tailored approach, enabling educators to promote a learning environment that is both inclusive and effective.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".