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Anxiety disorders are some of the most widespread mental health issues worldwide. In educational settings, individuals may suffer from specific forms of test and performance anxiety that are connected to a knowledge domain. Unquestionably, the most prominent of these is math anxiety. Math anxiety is a widespread problem for all ages across the globe. In the international assessments of the Programme for International Student Assessment (PISA) studies, a majority of adolescents report worry and tension in math classes and when doing math. To understand how math anxiety takes effect, it has to be regarded as a variable within an ensemble of interacting variables. There are antecedents that facilitate the development of math anxiety. They concern environmental factors such as teachers' and parents' attitudes toward their students' and children's ability in math, societal stereotypes (eg, on females' math abilities), or personal factors such as traits or gender. These antecedents influence a number of variables that are important in learning processes. Math anxiety interacts with variables such as self-efficacy or motivation in math, which can intensify or counteract math anxiety. Outcomes of math anxiety concern not only performance in math-related situations, they can also have long-term effects that involve efficient (or not-so-efficient) learning as well as course and even vocational choices. How can math anxiety be counteracted? A first step lies in its correct diagnosis. Questionnaires for the assessment of math anxiety exist for all age groups, starting at primary education level. Help against math anxiety can be offered on different levels: by educational institutions, by teachers and a change in instructional approaches, by parents, or by the affected person. However, much more research is needed to develop effective measures against math anxiety that are tailored to an individual's characteristics and needs.

The trends in students’ performance in science and mathematics at secondary school level were examined within a decade-long interval in Ghana. Using West African Examinations Council (WAEC) data from approximately 20% of schools countrywide, it was observed that the results did not provide a definitive pattern. Factors such as teachers’ inability to complete between 50% and 75% of the curricula, insufficient-time to enact and complete the curriculum, lack of enough qualified teachers, and use of poor teaching approaches accounted for the trends. This study in Ghana has implications in teacher recruitment deployment in general as well as duration of secondary education globally to examine their efficacy.

Learning engagement is a multifactorial paradigm that forecasts content achievement. Yet, learning engagement is rarely assessed in conjunction with mathematics. Therefore, this study explored a survey of learning engagement as a predictor of students’ performance in mathematics. A stratified random sampling technique was deployed to choose a sample of 1200 senior secondary school students from 40 senior secondary schools across the six education districts of Lagos state in Nigeria. A valid and reliable Mathematics Engagement Questionnaire (MEQ, Cronbach alpha=0.81) was deployed in collecting primary data related to learning engagement while secondary data related to end-of-year examination grades were collected from school records and were used to measure performance in mathematics. The results showed that there were weighty associations among behavioural, affective, cognitive engagement, and performance in mathematics. In addition, affective engagement was the greatest contributor to variance in performance in mathematics (β=0.935, p<0.001), and this was trailed by behavioural engagement (β=0.722, p<0.001), and the least contributor to performance in mathematics was cognitive engagement (β=-0.521, p<0.01). It is the suggestion of this study that stakeholders in mathematics education should articulate policies and curricula that allow the development of cognitive, behavioral and affective engagement in order to increase and sustain students’ performance in mathematics.

This study explored innovative methods for teaching mathematics to seventh-grade students with persistently low performance by using an AI-driven neural network approach, specifically focusing on solving first-degree inequalities. Guided by the Response to Intervention (RTI) framework, the intervention aimed to reduce math anxiety and build academic resilience through the development of cognitive and metacognitive strategies. A rigorous pre- and post-test design was employed to evaluate changes in performance, anxiety levels, and resilience. Fifty-six students participated in the 12-week program, receiving personalized instruction tailored to their individual needs. The AI tool provided real-time feedback and adaptive problem-solving tasks, ensuring students worked at an appropriate level of challenge. Results indicated a marked decrease in math anxiety alongside significant gains in cognitive skills such as problem-solving and numerical reasoning. Students also demonstrated enhanced metacognitive abilities, including self-monitoring and goal setting. These improvements translated into higher academic performance, particularly in the area of inequalities, and greater resilience, highlighting the effectiveness of AI-based strategies in supporting learners who struggle persistently in mathematics. Overall, the findings underscore how AI-driven teaching approaches can address both the cognitive and emotional dimensions of mathematics learning. By offering targeted, adaptive support, educators can foster a learning environment that reduces stress, promotes engagement, and facilitates long-term academic success for students with persistently low performance in mathematics.

This study aimed to explore school factors that influence students’ achievements in Trends in International Mathematics and Science Study (TIMSS) in schools in the Emirate of Abu Dhabi, in the United Arab Emirates. The study sample for TIMSS 2015 consisted of 4838 students in eighth grade; 2172 girls, 2666 boys, and 156 schools from Abu Dhabi attended and participated in TIMSS 2015. Principal component analysis (PCA) was run on 77 items of school questionnaires administered to school principals to provide information about the school contexts for teaching and learning. The five factors from the school questionnaire were general school resources, school discipline and safety, parental support, principal experience and education, and library and instruction resources. Multiple regression models were implemented to examine the impact of school factors on student achievement in TIMSS 2015. The models are statistically significant, indicating that they fit the data well. This also demonstrates a significant linear relationship between students’ achievement in TIMSS 2015 and the variables related to school factors.

Students’ negative affective performance in mathematics learning is a global problem, and the situation is especially challenging in high-achieving East Asian countries. Taiwan’s Just Do Math (JDM) program was launched to resolve this problem and to serve as an example of successful scaling up with regard to implementation of innovation. We developed a framework consisting of a synthesis of the literature on essentials for successful implementation and for scaling up, and applied this framework for the purpose of identification of the factors contributing to the success of the JDM program. The identified factors include the following: the development of low-complexity sources for teachers’ use and the low degree of adaptation required to sustain fidelity; the consideration of characteristics of adopters to promote innovation outside schools; construction of communication channels to ensure the dissemination of the core ideas of JDM; the feedback mechanism through which participating teachers can acknowledge the relative advantages of innovation as realized in the program; the consideration of the influence of historical, social, political, and economic factors on program implementation; and the efforts to ensure teachers’ in-depth learning, acknowledgment of the promoted principles, and the sustainability of implementation through implementation of JDM activities in regular mathematics classes.

This study aims to explore the potential use of digital technology for supporting outdoor mathematics teaching and learning process. A study with explorative research approach were conducted in Indonesia. A portal and a mobile app for math trail program was created and several math trail tasks were designed around the city and uploaded into a system by the teachers. Then students run the activity by the help of mobile app to find and solve mathematical modelling tasks around the city. Data were gathered by means of participatory observation, interviews, questionnaires, and worksheets. The findings indicate that a meaningful digital technology-supported mathematical outdoor activity was successfully designed and implemented. The use of digital technology has the potential to support teachers in facilitating outdoor mathematics teaching and learning process. Students gained mathematical experiences and their performance in mathematics have improved. Further studies are essential for project development and implementation in other cities with different situation and different aspects of study.

The purpose of this meta-analysis was to examine the relation between mathematics anxiety (MA) and mathematics performance among school-aged students, and to identify potential moderators and underlying mechanisms of such relation, including grade level, temporal relations, difficulty of mathematical tasks, dimensions of MA measures, effects on student grades, and working memory. A meta-analysis of 131 studies with 478 effect sizes was conducted. The results indicated that a significant negative correlation exist between MA and mathematics performance, r = -.34. Moderation analyses indicated that dimensions of MA, difficulty of mathematical tasks, and effects on student grades differentially affected the relation between MA and mathematics performance. MA assessed with both cognitive and affective dimensions showed a stronger negative correlation with mathematics performance compared to MA assessed with either an affective dimension only or mixed/unspecified dimensions. Advanced mathematical task that require multistep processes showed a stronger negative correlation to MA compared to foundational mathematical tasks. Mathematics measures that affected/reflected student grades (e.g., final exam, students 'course grade, GPA) had a stronger negative correlation to MA than did other measures of mathematics performance that did not affect student grades (e.g., mathematics measures administered as part of research). Theoretical and practical implications of the findings are discussed.

Abstract The current meta-analysis quantifies the average effect of worked examples on mathematics performance from elementary grades to postsecondary settings and to assess what moderates this effect. Though thousands of worked examples studies have been conducted to date, a corresponding meta-analysis has yet to be published. Exclusionary coding was conducted on 8033 abstracts from published and grey literature to yield a sample of high quality experimental and quasi-experimental work. This search yielded 43 articles reporting on 55 studies and 181 effect sizes. Using robust variance estimation (RVE) to account for clustered effect sizes, the average effect size of worked examples on mathematics performance outcomes was medium with g = 0.48 and p = 0.01. Moderators assessed included example type (correct vs. incorrect examples alone or in combination with correct examples), pairing with self-explanation prompts, and timing of administration (i.e., practice vs. skill acquisition). The inclusion of self-explanation prompts significantly moderated the effect of examples yielding a negative effect in comparison to worked examples conditions that did not include self-explanation prompts. Worked examples studies that used correct examples alone yielded larger effect sizes than those that used incorrect examples alone or correct examples in combination with incorrect examples. The worked examples effect yields a medium effect on mathematics outcomes whether used for practice or initial skill acquisition. Correct examples are particularly beneficial for learning overall, and pairing examples with self-explanation prompts may not be a fruitful design modification. Theoretical and practical implications are discussed.

The relationship between anxiety and mathematics has often been investigated in the literature. Different forms of anxiety have been evaluated, with math anxiety (MA) and test anxiety (TA) consistently being associated with various aspects of mathematics. In this meta-analysis, we have evaluated the impact of these forms of anxiety, distinguishing between different types of mathematical tasks. In investigating this relationship, we have also included potential moderators, such as age, gender, working memory, type of task, and type of material. One hundred seventy-seven studies met the inclusion criteria, providing an overall sample of 906,311 participants. Results showed that both MA and TA had a significant impact on mathematics. Sociodemographic factors had modest moderating effects. Working memory (WM) also mediated the relationship between MA and TA with mathematics; however, this indirect effect was weak. Theoretical and educational implications, as well as future directions for research in this field, are discussed.