Math Anxiety Outshines Personality in Predicting How Well Students Judge Their Own Performance

Why do some students walk out of a math exam convinced they aced it, only to be blindsided by a failing grade, while others fret endlessly about answers that turn out to be correct? A new study published in Current Psychology by Taylor Thigpen, Lin-Miao L. Agler, and Kenji Noguchi of the University of Southern Mississippi tackles this question by dissecting the psychological machinery behind metacognitive judgments in mathematics—the mental process by which learners estimate how well they have performed and compare those estimates against reality. The research, published online as volume 45, article 1438 of the journal, delivers a message that is both intuitively satisfying and practically important: when it comes to knowing how well you know, the specific beats the general.

The study set out to compare three tiers of factors that might shape how accurately students judge their own math performance. At the broadest level sat the Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—the stable dispositions that psychologists have long studied as predictors of academic outcomes. One tier down sat subject-specific person attributes, represented by individual math anxiety, the domain-bound dread that many students feel when confronted with numbers and equations. At the most contextual level sat task difficulty itself, a property of the learning materials rather than of the learner. The central question was which of these tiers would most reliably predict self-estimated performance, actual performance, and—crucially—the accuracy of the match between the two, a property researchers call calibration.

Calibration is one of the most consequential concepts in educational psychology, and its importance is hard to overstate. A student with poor calibration faces a double bind: those who are overconfident stop studying too soon, believing they have mastered material they have not, while those who are underconfident waste precious study time rehearsing what they already know and may experience corrosive anxiety about their competence. Decades of research, stretching back to classic work on the illusion of knowing and on confidence calibration in judgment under uncertainty, have shown that monitoring accuracy feeds directly into self-regulated learning. If your internal gauge of your own knowledge is broken, every downstream decision—what to study, when to stop, whether to attempt a harder course—rests on faulty data.

The experimental design was elegantly straightforward. College students were assigned to solve one of two sets of math problems: an easy set deliberately pitched significantly below college level, or a relatively difficult set that better matched the expectations of college coursework. Before attempting the problems, participants estimated how they expected to perform; afterward, they estimated how they believed they had performed. They then completed the Big Five personality inventory developed by John and Srivastava, along with a standardized math anxiety scale. This structure allowed the researchers to examine metacognitive judgments both prospectively and retrospectively, and to ask whether the same factors predicted the raw judgments, the actual scores, and the alignment between the two.

The headline finding was that specific factors were more consistent predictors of students’ calibration accuracy than general traits. In other words, knowing a student’s overall personality profile told you less about whether their confidence would track their actual performance than knowing how anxious they were about math in particular, or how hard the problems they faced actually were. This pattern echoes a growing recognition in the literature that domain-specific measures often outperform broad dispositional ones when the outcome of interest lives inside a particular academic subject. General traits may set a backdrop, but the action happens in the foreground of the specific domain.

That said, personality was not irrelevant. Neuroticism—the trait characterized by emotional volatility, worry, and susceptibility to negative affect—emerged as a significant predictor of metacognitive judgments and their accuracy. This makes theoretical sense, because neuroticism and math anxiety share overlapping features related to general anxiety. A student high in neuroticism tends to experience worry across many situations, and that pervasive apprehension appears to color how they estimate their mathematical performance. Yet the study found that subject-specific math anxiety was the more consistent predictor of the two. When the researchers pitted the broad disposition against its domain-specific cousin, the cousin won. The implication is that anxiety does its damage to metacognitive accuracy most powerfully when it is anchored to the material at hand, not merely when it floats freely as a general temperament.

The third tier of factors, task difficulty, also proved to be a stable predictor of ability judgments and calibration accuracy. This finding aligns with a long line of research showing that the properties of the materials themselves shape metacognitive monitoring. Students confronting genuinely difficult problems face a harder inferential task when judging their own performance: the cues they rely on—fluency, familiarity, ease of processing—are less trustworthy when the material is demanding. Conversely, easy problems can breed their own distortions, since smooth processing may generate confidence that outstrips what the performance actually warrants. The fact that difficulty predicted both the judgments students made and how well those judgments matched their scores underscores that metacognition is never a purely internal affair; it is a dialogue between the mind and the task.

The study’s framing draws on influential theoretical accounts of self-regulated learning, including models that place metacognitive monitoring at the intersection of cognition, motivation, and affect. Within such frameworks, the current results suggest a layered architecture: general personality traits contribute a unique but less reliable signal, domain-specific anxiety contributes a stronger and steadier one, and contextual task factors anchor the whole system to the immediate situation. For educators, this hierarchy carries practical weight. Interventions aimed at improving students’ self-assessment in mathematics may achieve more by addressing math anxiety directly and by calibrating students’ expectations to the actual difficulty of their materials than by attempting to reshape broad personality dispositions, which are famously resistant to change.

The research also speaks to a persistent concern in mathematics education. Math anxiety has been documented for decades as a force that consumes working memory resources, degrades performance under pressure, and steers capable students away from quantitative careers. The new findings extend this picture into the metacognitive realm: anxiety does not only impair the doing of math, it impairs the knowing of how well one has done. A student whose fear of mathematics systematically distorts their self-assessments is doubly disadvantaged, because poor calibration undermines the very self-regulation strategies—strategic study, judicious practice, informed course selection—that could otherwise help them improve.

The authors, writing in Current Psychology, note that factors with domain and contextual specificity had more reliable predictive effects on metacognitive assessment accuracy in math, although general personality traits also play a unique role. The work was supported by the Eagle Scholars Program for Undergraduate Research through the Drapeau Center for Undergraduate Research at the University of Southern Mississippi, and all procedures were approved by the university’s Institutional Review Board. As classrooms increasingly emphasize self-directed learning and students are asked to monitor their own progress, studies like this one offer a sharper map of where the errors in self-knowledge come from. The gauge on the dashboard matters—but so do the driver’s history with that particular road, and the road itself.

Subject of Research: How personality traits, math anxiety, and task difficulty affect the accuracy of college students' metacognitive self-estimates in mathematics

Article Title: The role of personality, anxiety, and task difficulty in math metacognition

Article References: Thigpen, T., Agler, L.-M. L., & Noguchi, K. (2026). The role of personality, anxiety, and task difficulty in math metacognition. Current Psychology, 45(17), Article 1438. https://doi.org/10.1007/s12144-026-10002-1

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10002-1

Keywords: metacognition, calibration, math anxiety, Big Five personality, neuroticism, task difficulty, self-regulated learning, college students, mathematics education, self-estimates, Current Psychology, role