Self-reported effort
Students rated how hard they worked, whether they completed homework, how strongly they persisted, or how much effort they believed they invested in a task (O’Neil et al., 1995; Trautwein et al., 2009; Xu, 2018).
- Publication
- Frontiers in Education, 2025
- Research type
- Scoping review and evidence synthesis
- Scope
- Individual, developmental, motivational, family, classroom, socioeconomic, and cultural factors
- My contribution
- First author · Writing: original draft
01 · The research gap
Effort is central to learning, but the evidence was scattered.
Developing mathematical competence requires sustained cognitive investment, yet research on effort in mathematics had been distributed across different constructs, populations, measures, and theoretical traditions. Studies referred to related behaviors using terms such as effort, engagement, persistence, homework compliance, diligence, and grit (Kahneman, 1973; Trautwein et al., 2009; Galla et al., 2014; Nguyen & Suárez-Pellicioni, 2025).
The review addressed a practical and theoretical problem: prior work had linked effort to mathematics achievement and gains over time, but there had not been a comprehensive synthesis of the individual and contextual factors associated with effort investment in mathematics (Trautwein, 2007; Trautwein et al., 2009; Nguyen & Suárez-Pellicioni, 2025).
The published review therefore asked three connected questions: what individual and contextual factors explain differences in math effort, what role effort plays in achievement, and how effort may connect affective experiences such as attitudes and anxiety to performance (Nguyen & Suárez-Pellicioni, 2025).
Rather than treating low effort as a student trait, what happens when we examine
effort as a behavior shaped by skills, beliefs, age, classrooms, families,
socioeconomic conditions, and culture?
02 · Review strategy
Building a broad map of a fragmented literature
The review searched APA PsycINFO, PubMed, ProQuest Dissertations & Theses, Taylor & Francis Online, and Google Scholar. Search terms combined mathematics-related keywords with terms including effort, engagement, perseverance, persistence, and grit (Nguyen & Suárez-Pellicioni, 2025).
Studies were included when they were written in English and focused specifically on effort in mathematics rather than general academic effort. No restrictions were placed on publication year, population, or geographic location. Screening moved from titles and abstracts to full-text review. The primary search was conducted between February and May 2024, with additional relevant studies added afterward (Nguyen & Suárez-Pellicioni, 2025).
A central synthesis challenge was conceptual inconsistency. Similar behaviors were often labeled differently across studies, the kind of construct overlap described as a “jangle fallacy” (Lawson & Robins, 2021). The review therefore treated these related operationalizations as part of the broader effort literature while documenting how effort was actually measured.
03 · What counted as effort?
The field was measuring several different expressions of investment.
One of the most useful findings for future research was methodological: “effort” was not represented by a single standard measure (Nguyen & Suárez-Pellicioni, 2025).
Teacher or parent ratings
Some studies relied on adults’ judgments of how hard a student tried or how persistently a child worked during mathematics activities (Mägi et al., 2010; Upadyaya & Eccles, 2015; Silinskas & Kikas, 2019).
Behavioral indicators
Researchers also used time on task, homework time, number of problems attempted or solved, task persistence, and willingness to select more difficult problems (Fisher et al., 2012; Trautwein & Lüdtke, 2009; Milyavskaya et al., 2021).
Physiological and neural indicators
The review identified emerging approaches using physiological or neural measures as possible real-time indicators of cognitive effort, while noting that this evidence remains comparatively limited (Charles & Nixon, 2019; Tao et al., 2019; Ayres et al., 2021).
Measurement implication: time spent, self-reported effort, persistence, and cognitive load are related but not interchangeable. Time on task does not necessarily indicate cognitive engagement, and the same observable performance may require different levels of effort depending on students’ skill and strategy use (Berger, 2009; Matthews et al., 2020; Nguyen & Suárez-Pellicioni, 2025).
04 · Individual differences
Effort varied with development, skill, beliefs, and motivation.
The review found evidence that math effort differed across several individual-level characteristics, including age, skill, attitudes, effort beliefs, and goal orientation, but the strength and consistency of that evidence varied (Nguyen & Suárez-Pellicioni, 2025).
- Age: some studies reported declining homework or teacher-rated effort as students grew older, though the evidence base was limited (Trautwein et al., 2006a; Upadyaya & Eccles, 2015).
- Math skill: higher skill was often associated with greater engagement or effort, and some longitudinal evidence suggested reciprocal relationships between skill and effort (Fisher et al., 2012; Xie et al., 2013).
- Interest and enjoyment: students who found mathematics interesting or enjoyable were more likely to persist, work harder, or select more demanding mathematical activities (Milyavskaya et al., 2021; Xu, 2018).
- Competence beliefs: findings were mixed. Some studies linked stronger competence beliefs to greater effort, while others found that students who felt more capable reported needing less effort (Chouinard et al., 2007; Trautwein et al., 2009; Chen & Zimmerman, 2007; Pinxten et al., 2014).
- Goal orientation and effort beliefs: mastery-oriented goals and beliefs that effort contributes to improvement were generally associated with greater persistence and effort-based strategies (Blackwell et al., 2007; Chouinard et al., 2007).
05 · Context matters
Effort was not only an individual characteristic.
The synthesis brought together evidence suggesting that effort is associated with features of students’ classrooms, teacher involvement, parental support, socioeconomic conditions, and cultural contexts, in addition to individual attitudes and abilities (Lau & Nie, 2008; Hentges et al., 2019; Xu et al., 2022; Nguyen & Suárez-Pellicioni, 2025).
Classroom climate
Mastery-oriented classrooms, those emphasizing learning and improvement, were associated with greater effort than classrooms emphasizing performance and comparison (Lau & Nie, 2008; Skaalvik et al., 2017).
Teacher involvement
Autonomy support, appropriate homework, and constructive feedback were positively associated with students’ homework effort and, in some studies, subsequent achievement (Xu et al., 2021, 2022).
Parental involvement
Supportive, autonomy-promoting involvement was associated with persistence, whereas overly controlling or intrusive involvement could undermine persistence or achievement (Silinskas & Kikas, 2019; Xu et al., 2018).
SES and culture
Evidence was more limited, but studies suggested that economic disadvantage can alter the perceived cost of investing effort and that cultural beliefs influence how effort is valued, interpreted, and rewarded (Hentges et al., 2019; Fwu et al., 2014).
06 · Why effort matters
Effort may be both an outcome and a mechanism.
The review did not treat effort simply as “working hard.” It synthesized evidence suggesting that effort may help explain how affective experiences, including attitudes and anxiety, are associated with mathematics performance (Singh et al., 2002; Cole et al., 2008; Yu et al., 2021).
Effort was associated with concurrent mathematics performance and, in longitudinal studies, predicted later grades or test performance after accounting for earlier achievement (Trautwein, 2007; Trautwein et al., 2009).
Several studies suggested that positive attitudes and competence beliefs may improve mathematics outcomes partly because they increase students’ willingness to invest effort (Singh et al., 2002; Cole et al., 2008).
Math anxiety was associated with choosing less effortful strategies, avoiding difficult problems, and lower math-specific persistence (Choe et al., 2019; Jenifer et al., 2022; Yu et al., 2021).
A student’s performance may reflect not only what they know, but also whether they perceive mathematics as useful, whether the effort feels costly, and whether their learning environment supports sustained engagement (Trautwein & Lüdtke, 2009; Hentges et al., 2019; Skaalvik et al., 2017).
07 · Applied interpretation
What this evidence can help educators and program teams examine
This was a literature review, not a single intervention trial. The applications below therefore represent evidence-informed directions supported by the reviewed literature, not claims that the review itself demonstrated program impact (Nguyen & Suárez-Pellicioni, 2025).
Diagnose before attributing low effort
A low-effort pattern may reflect low interest, high perceived cost, anxiety, competence beliefs, classroom structure, family dynamics, or ineffective strategies. Treating it only as a motivation problem risks missing other plausible barriers (Song et al., 2019; Hentges et al., 2019; Jenifer et al., 2022; Xu et al., 2018).
Design for mastery, not only performance
Learning environments can emphasize improvement, strategy use, feedback, and understanding rather than comparison alone. The reviewed evidence generally linked mastery-oriented environments with stronger effort investment (Lau & Nie, 2008; Skaalvik et al., 2017).
Support autonomy
Teacher and parent support appeared more favorable when it promoted autonomy and helped students act independently rather than relying on controlling or overly directive involvement (Moorman & Pomerantz, 2008; Xu et al., 2018, 2021).
Make value visible
Students were more likely to report or behaviorally demonstrate effort when mathematics felt useful, interesting, or enjoyable (Trautwein et al., 2006b; Milyavskaya et al., 2021; Xu, 2018). This makes perceived value a plausible target for intervention, although the effect of any specific intervention still requires direct testing.
Measure effort in more than one way
Combine self-report with behavioral or process-based indicators when possible. A single question about “trying hard” may not capture persistence, strategy quality, cognitive load, or real-time fluctuations (Van Gog et al., 2012; Matthews et al., 2020; Vanneste et al., 2021).
Do not separate effort from strategy
More effort is not automatically better if students are using ineffective approaches. Future interventions should examine effort regulation together with strategy selection (Kim et al., 2015; Yeager et al., 2016; Nguyen & Suárez-Pellicioni, 2025).
08 · Intervention opportunities
What the reviewed evidence suggests is worth testing
The review identified several strategies that had empirical support in the underlying literature, including relevance interventions, growth-oriented beliefs, effort-focused praise, and effort regulation. Their effectiveness still depends on context, implementation, and the outcome being measured (Brisson et al., 2017; Bettinger et al., 2018; Zentall & Morris, 2010; León et al., 2015).
Value and relevance
Help students connect mathematics to personally meaningful goals, everyday use, or future opportunities. Relevance interventions have been associated with stronger competence beliefs, effort, and achievement in some studies (Brisson et al., 2017).
Growth-oriented beliefs
Interventions can emphasize that mathematical competence develops through learning and practice, while avoiding the oversimplified message that effort alone guarantees success (Bettinger et al., 2018; Yeager et al., 2016).
Effort-focused feedback
Feedback that recognizes persistence and strategy can encourage challenging task choices and continued engagement more effectively than praise focused solely on innate ability (Mueller & Dweck, 1998; Zentall & Morris, 2010).
Effort regulation
Students may benefit from support in deciding when to persist, when to change strategies, and how to sustain effort when intrinsic motivation is low (Kim et al., 2015; León et al., 2015).
09 · Evidence boundaries
What the review clarified, and what remains unresolved
The review showed that effort is associated with mathematics achievement and identified individual and contextual factors that covary with effort. It also summarized evidence consistent with possible mediating pathways linking attitudes and anxiety to performance. However, the underlying studies varied substantially in design, measurement, population, and causal strength (Singh et al., 2002; Trautwein, 2007; Yu et al., 2021; Nguyen & Suárez-Pellicioni, 2025).
Most studies relied heavily on self-report, which can be affected by timing, introspective limits, and response interpretation. Behavioral measures such as time on task can also be ambiguous because time does not necessarily equal cognitive engagement. Physiological measures may offer useful real-time indicators, but that literature remains comparatively limited (Van Gog et al., 2012; Matthews et al., 2020; Ayres et al., 2021).
Evidence on socioeconomic status, cultural differences, sex differences, and some developmental patterns was especially limited or mixed. The review therefore called for stronger measurement and further work on effort regulation, physiological indicators, and the interaction between effort and strategy use (Hentges et al., 2019; Charles & Nixon, 2019; León et al., 2015; Nguyen & Suárez-Pellicioni, 2025).
Important boundary: the review does not support the conclusion that students who underperform simply “need to try harder.” The evidence instead suggests that effort is related to skill, motivation, perceived cost, available support, and strategy quality, and the review explicitly cautions that effort alone may be insufficient when strategies are ineffective (Xie et al., 2013; Hentges et al., 2019; Yeager et al., 2016).
10 · What this case demonstrates
Turning a fragmented literature into a structured research agenda
- Conducting broad, multi-database literature searches and staged screening.
- Reconciling overlapping constructs and inconsistent measurement practices.
- Synthesizing evidence across developmental, motivational, family, classroom, socioeconomic, and cultural levels.
- Distinguishing correlational, longitudinal, mediational, and intervention evidence.
- Identifying methodological weaknesses that limit interpretation.
- Translating academic evidence into testable questions for educational programs and interventions without overstating causal impact.
Selected references
Research grounding for this case
The case study summarizes the published review and highlights a subset of the studies synthesized in the article.
- Nguyen, N., & Suárez-Pellicioni, M. (2025). How hard did you try? A scoping review of the literature on effort investment in math. Frontiers in Education, 10, 1575780. DOI →
- Trautwein, U. (2007). The homework-achievement relation reconsidered: Differentiating homework time, homework frequency, and homework effort. Learning and Instruction, 17, 372-388.
- Trautwein, U., Lüdtke, O., Schnyder, I., & Niggli, A. (2006). Predicting homework effort: Support for a domain-specific, multilevel homework model. Journal of Educational Psychology, 98, 438-456.
- Milyavskaya, M., Galla, B. M., Inzlicht, M., & Duckworth, A. L. (2021). More effort, less fatigue: The role of interest in increasing effort and reducing mental fatigue. Frontiers in Psychology, 12, 755858.
- Hentges, R. F., Galla, B. M., & Wang, M.-T. (2019). Economic disadvantage and math achievement: The significance of perceived cost from an evolutionary perspective. British Journal of Educational Psychology, 89, 343-358.
- Lau, S., & Nie, Y. (2008). Interplay between personal goals and classroom goal structures in predicting student outcomes. Journal of Educational Psychology, 100, 15-29.
- Xu, J., Wang, C., Du, J., & Núñez, J. C. (2022). Profiles of student-perceived teacher homework involvement, and their associations with homework behavior and mathematics achievement: A person-centered approach. Learning and Individual Differences, 96, 102159.
- Choe, K. W., Jenifer, J. B., Rozek, C. S., Berman, M. G., & Beilock, S. L. (2019). Calculated avoidance: Math anxiety predicts math avoidance in effort-based decision-making. Science Advances, 5, eaay1062.
- Jenifer, J. B., Rozek, C. S., Levine, S. C., & Beilock, S. L. (2022). Effort(less) exam preparation: Math anxiety predicts the avoidance of effortful study strategies. Journal of Experimental Psychology: General, 151, 2534-2541.
- Brisson, B. M., Dicke, A. L., Gaspard, H., Häfner, I., Flunger, B., Nagengast, B., et al. (2017). Short intervention, sustained effects: Promoting students’ math competence beliefs, effort, and achievement. American Educational Research Journal, 54, 1048-1078.
- Van Gog, T., Kirschner, F., Kester, L., & Paas, F. (2012). Timing and frequency of mental effort measurement: Evidence in favour of repeated measures. Applied Cognitive Psychology, 26, 833-839.
Thoughts, discussion, ideas, reflections?
What should future research measure when we say a student “tried hard”?
If this review raised a methodological question, suggested another factor that may shape effort, or gave you an idea for a future study, feel free to join the discussion.