The Relationship between Growth Mindset and Mathematics Achievement
Bibliographic record
Abstract
This study examines the relationship between a growth mindset and mathematical performance among seventh-grade students at Tambobong National High School in Davao City. The researchers employed a descriptive-correlational research design and selected 100 students via stratified random sampling to guarantee representation from various sections. Data regarding students' beliefs were gathered using a modified Growth Mindset Scale by Dweck (2006), which comprises Likert-type items assessing students' perceptions of the malleability of intelligence. The evaluation of mathematics performance was conducted through the students' final grades in the subject, as recorded in school documentation. Descriptive statistics indicated a mean mindset score of 2.09, interpreted as “Growth Mindset with Some Fixed Ideas,” suggesting that students predominantly perceive abilities as developable, yet maintain certain fixed beliefs. Their mathematics performance yielded a mean general average of 85.8, categorized as “Very Satisfactory.” The Pearson Product-Moment Correlation Coefficient was employed to determine the relationship between the two variables, yielding a moderate yet statistically significant positive correlation (r = 0.219) This indicates that students possessing more robust growth-oriented beliefs generally achieve superior performance in mathematics. The results corroborate theories connecting mindset, motivation, and academic success, highlighting the importance of integrating mindset development into educational strategies to enhance student performance in mathematics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".