Why Effort and Mastery Matter More Than Stable Interest in Mathematics
Students are often told that success in mathematics depends on talent or passion. Research by Yundong Wu and Ruofan Lin offers a more practical message: continuing to work through difficulty, and aiming to understand rather than merely outperform others, are more closely associated with achievement.
The study involved 256 Grade 10 students at a private high school in Quanzhou, China, with 248 valid responses. It separated grit into two dimensions: perseverance of effort and consistency of interests. It also measured three achievement goals - mastery, performance approach and performance avoidance - and compared these measures with formal school mathematics test scores rather than self-reported grades.
Perseverance showed the clearest pattern. It positively predicted all three goal orientations and mathematics achievement. In the initial model, perseverance had a standardised coefficient of .360 for mathematics scores. After achievement goals were added, it remained significant at .239. Students who kept practising, revisited errors and continued after setbacks tended to perform better.
Stable interest told a different story. Consistency of interests predicted the two performance-oriented goals, but it did not significantly predict mastery goals or mathematics scores. Remaining interested in a broad academic direction may help students care about ranking or avoiding poor performance, yet it does not automatically produce the deliberate practice needed to understand a difficult concept.
Among the three goal orientations, mastery goals were the only significant positive predictor of mathematics achievement in the full model, with a coefficient of .283. Performance approach and performance avoidance goals had no significant direct effect once perseverance and mastery were considered. The results suggest a possible pathway in which sustained effort supports a desire to improve competence, which in turn supports achievement. Because the research was cross-sectional, however, it does not prove mediation or causation.
The classroom implications are concrete. Teachers can recognise error correction, strategy changes and return to difficult problems - not only correct answers. Clear learning objectives, opportunities to revise work, graduated challenge, cooperative problem-solving and short reflections can connect effort with understanding. “Work harder” is not enough; persistence becomes productive when paired with feedback and a mastery-focused strategy.
The sample came from one academically strong private school, and motivation was measured through self-report, so the findings should be tested across more schools and over time. Even with those limits, the study offers a useful shift: mathematics resilience is not simply about maintaining enthusiasm. It is about sustaining effort in ways that help students see progress, make sense of mistakes and build competence step by step.