Globally, anxiety disorders have emerged as the most prevalent and widespread mental health issue. According to the World Health Organization (WHO), approximately 4% of the global population currently experiences at least one anxiety disorder (1). While occasional anxiety is a normal aspect of life, it becomes problematic when it transforms into frequent, persistent worry or fear. There are various forms of anxiety disorders, including generalized anxiety disorder, social anxiety disorder, panic disorder, and phobia-related disorders etc. In educational settings, test anxiety and performance anxiety are particularly common among students. However, a distinct form of anxiety disorder, known as MA, has recently gained increasing focus as a frequent and alarming issue on a global scale in all age groups of students (2, 3). MA has been defined in various ways by researchers, educational psychologists (EPs) and practitioners from different point of view. Consequently, there is no universally accepted or rigid definition for the term MA. However, in most cases, MA is defined as a distinct negative emotional state or trait that individuals experience when confronted with mathematical problems in everyday life and academic contexts (4, 5). Beyond its negative effects on mental health and well-being, MA is well-documented for its negative correlation with mathematical competence, performance and achievement (6, 7), making it a potential barrier to success in science, technology, engineering, and mathematics (STEM) careers (8).

Though MA has short, medium, and long-term negative consequences on students’ self-confidence, math performance, math achievement, and even career choices, the specific factors and other events that trigger to the development of MA remain still unclear (9). However, existing studies broadly categorize these factors into personal, psychological, environmental, cognitive, and pedagogical influences. Among personal factors, gender has received significant attention in relation to MA. Many studies have found that females are more prone to MA than males (10–13), which would suggest that females’ math performance should also be more negatively affected. However, despite these findings, research often reveals little to no gender differences in actual math performance (14–16). Another potential factor that has been investigated under the personal factors is genetics. For instance, Malanchini et al. (17) found that 75% of the genetic variance in MA is tied to math-related attitudes, abilities, and achievement. Wang et al. (18) similarly reported that 40% of the variance in MA is genetically influenced, either directly or through contributions to mathematical ability and general anxiety. In terms of psychological factors, self-efficacy is frequently studied in connection with MA (19). For example, Khasawneh et al. (10) found a significant negative correlation between MA and self-efficacy levels, suggesting that improving students’ self-efficacy could enhance their belief in their capabilities, thereby reducing their anxiety toward math. Similar negative correlation was also observed in the study of Rozgonjuk et al. (20), where the authors suggested that since mathematics self-efficacy plays a significant role in MA, boosting self-efficacy could be an effective strategy for reducing MA. In this context, environmental factors, such as teacher support is crucial. Research has shown that student-perceived math teacher support influences MA through the mediation of the teacher–student relationship and math self-efficacy (21, 22). By fostering positive teacher–student interactions and enhancing self-efficacy, MA can be effectively reduced through increasing mathematical problem-solving ability (23). Conversely, negative classroom experiences, such as adverse teacher behavior or failures in math, are negatively correlated with students’ self-efficacy (3, 24). Similarly, other environmental factors, such as parental stereotypes, culture, social pressure also found influencing effect on MA (2, 3, 25). When examining cognitive factors, researchers often focus on the interaction between MA and working memory (26, 27). According to existing studies, this interaction is reciprocal: higher levels of MA impair working memory (26, 28), leading to more errors and poorer performance in math (29). This poor performance, in turn, further increases MA, creating a vicious cycle (30). Finally, in terms of pedagogical factors, the most frequently documented influence on MA is teaching or instructional strategies. Studies have shown that a rigid curriculum and traditional instructional strategies can significantly contribute to the development of MA (2, 31). Conversely, incorporating technology-enhanced and modernized approaches to mathematics instruction can create an engaging, active learning environment that reduces anxiety and fosters a more positive attitude toward learning math (32).

In educational contexts, it is widely recognized by educational psychologists to practitioners that MA represents a significant barrier to student success across all academic levels. Over the years, numerous studies have investigated the origins of MA and its associated factors, yet few have delved into the intricate causal relationships that play a critical role in predicting MA. Furthermore, most of the existing research has predominantly focused on Western countries, while limited attention given to developing nations such as Bangladesh. To address this gap and support the advancement of domain knowledge, this study aims to identify and analyze the key factors that contribute in shaping MA within the context of Bangladesh, particularly at the secondary education level.

The remaining sections of this study organized as follows:Section 2 briefly discusses the materials and methods. Section 3 presents the findings step-by-step. Section 4 interprets these results in the context of previous research. Section 5 offers recommendations and policy implications. And section 6 highlights the study’s limitations and suggests future research directions.

This study aimed to identify and analyze the key factors contributing to MA among secondary-level students in Bangladesh, using a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The findings offer valuable insights into the complex interplay of factors that exacerbate MA, reinforcing the results of previous research while contributing new evidence from the context of a developing country. The results highlight a moderate but significant impact of cognitive challenges (CC), particularly working memory difficulties, on MA (β = 0.110, t = 2.659, p = 0.008). Although extensive research has established a negative relationship between MA and working memory (56, 57), no prior study has empirically identified working memory difficulties as a predictor of MA. Therefore, our findings can be interpreted as follows: working memory difficulties contribute to poor mathematical performance, which, in turn, may trigger MA, consistent with the Deficit Theory (58). In this sense, our findings align with existing literature, which suggests that cognitive overload impairs mathematical performance, thereby exacerbating anxiety (26, 28). We also found that both Negative Past Experiences (NPE) and Lack of Perceived Teacher Support (LPTS) had significant positive causal effects on Low Self-Efficacy (LSE), with coefficients of β = 0.241 (t = 4.914, p < 0.001) and β = 0.234 (t = 5.440, p < 0.001), respectively. These results indicate that negative past experiences and insufficient teacher support significantly contribute to reduced student self-efficacy. These findings are consistent with those of Shukla et al. (24) and Dowker et al. (3), who reported that negative classroom experiences, such as adverse teacher behavior or failures in mathematics, are negatively correlated with students’ self-efficacy. Furthermore, the strong positive association between low self-efficacy (LSE) and MA (β = 0.694, t = 22.695, p < 0.001), demonstrating that students who doubt their mathematical abilities are more likely to experience MA. Notably, the total indirect effect of LSE on MA, mediated through both Negative Attitude (NA) towards math and Test Anxiety (TA), was substantial (β = 0.410, t = 10.232, p < 0.001). This finding underscores the significant impact of LSE on MA through these mediators (NA and TA), aligning with studies by Khasawneh et al. (10) and Rozgonjuk et al. (20), which highlight the critical role of self-efficacy in managing mathematical anxiety. Additionally, the study identified that both NA toward mathematics and TA independently have a significant impact on MA (math achievement). Finally, this study also revealed that MA has a causal positive relationship with lack of motivation (LM) in math learning. The connection between math anxiety and decreased motivation is also evident in previous studies (59–62). This is because the anxiety associated with math can lead to avoidance behaviors, reduced confidence in their math abilities, and adversely affects their future math performance (60).

Based on the findings of this study, several key recommendations and policy implications are suggested to address and mitigate MA among secondary-level students in Bangladesh:

A. Enhance Teacher Training: Prioritize the development and implementation of teacher training programs that focus on creating supportive classroom environments and employing teaching methods that build student confidence and self-efficacy in mathematics.

B. Invest in Professional Development: Ensure ongoing professional development for teachers, emphasizing the latest research in educational psychology, teaching strategies, and tools for effectively managing classroom dynamics and student anxiety.

C. Real-World Application: Integrate activities and curricula that highlight the practical applications of mathematics in everyday life. Foster a growth mindset by encouraging students to view mathematical challenges as opportunities for growth.

D. Support for Students with Cognitive Challenges: Provide additional support for students facing cognitive challenges, particularly related to working memory. This could involve personalized tutoring, memory aids, and teaching strategies that break down complex problems. Leveraging educational technologies can support cognitive development and offer interactive learning experiences.

E. Comprehensive Support Systems: Develop integrated support systems including counselors, psychologists, and special educators to address the diverse needs of students.

F. Boost Student Motivation: Employ strategies to increase student motivation and engagement in mathematics, such as gamification, setting achievable goals, and providing timely, constructive feedback.

Despite potential findings, this study acknowledges several limitations. Firstly, a significant limitation is the exclusion of several potential factors, such as gender, parental attitudes, and instructional strategies, due to the research’s limited scope. Including these variables would have complicated the exploration, as examining all such factors in a single study would be challenging, and incorporating mediator factors would add further complexity to the structural model. Secondly, the use of self-report measurements could introduce potential bias, as this method is often criticized for the difficulty respondents may face in making conscious judgments and the tendency to provide socially desirable answers (63). However, we attempted to mitigate this limitation by ensuring students through informed consent that their responses would remain confidential. Thirdly, in this study, we focused primarily on self-reported cognitive difficulties (working memory difficulties) rather than objective cognitive testing. We recognize that the lack of objective cognitive assessments is a limitation, as it would provide a more comprehensive understanding of the participants’ actual cognitive performance. So, in future, objective cognitive assessment is suggested to gain a more nuanced understanding of the cognitive challenges faced by individuals with MA. Fourthly, there is a scarcity of previous studies in developing countries like Bangladesh that explore the complex causal relationships between factors related to MA. This has made it challenging to adequately compare this study’s findings with other studies in the context of Bangladesh. Fifthly, the study’s quantitative cross-sectional design, with data collected over a short period, limits the ability to fully understand and generalize the relationships between the factors. Finally, this study single-level PLS-SEM due to the nature of the data, which reflects individual-level factors influencing MA. However, a multilevel design, especially considering hierarchical relationships (e.g., student-teacher or school-level effects) could be utilized for more precise finings. So, future research should include longitudinal studies, both quantitative and qualitative with multi-level SEM design, to enhance the understanding and generalizability of these relationships.

This study unveiled a complex interplay of factors contributing to mathematics anxiety (MA) among Bangladeshi secondary students. By employing a robust quantitative approach, this study demonstrates that negative past experiences, perceived lack of teacher support, and cognitive challenges, such as working memory difficulties, significantly contribute to MA. These factors, combined with low self-efficacy, negative attitudes, and test anxiety further create a complex web that exacerbates MA and undermines students’ motivation and performance in mathematics. Addressing these issues requires a multifaceted approach, including enhanced teacher training, professional development, and the integration of practical applications of mathematics in curricula. Additionally, supporting students with cognitive challenges and fostering comprehensive support systems are essential for mitigating MA. By implementing these recommendations, educational authorities can better support students, improve their mathematical experiences, and ultimately enhance their future performance in mathematics.