The Effect of Students’ Prosocial Behavior and Students’ Self-Efficacy on their Mathematics Achievement in Senior High Schools.

The Effect of Students’ Prosocial Behavior and Students’ Self-Efficacy on their Mathematics Achievement in Senior High Schools.

Akwaboah Sumaila

Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Ghana

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0278

Received: 17 May 2025; Accepted: 21 May 2025; Published: 20 June 2025

ABSTRACT

The study looked into the effect of students’ prosocial behavior and self-efficacy on their mathematics achievement in two selected senior high schools in Ghana’s Ashanti region, Descriptive survey design was used to conduct the study. Students in SHS one, two, and three were the target group. A total of 260 students were given questionnaires through a stratified random sampling methods. Bootstrap samples were used in the Structural Equation Model (SEM) analysis of the gathered data. It was discovered that students’ prosocial behaviors and students’ self-efficacy were significantly positively predictor of students’ success in mathematics. Based on the results, it was recommended that mathematics teachers should consider their interactions with students, use a range of instructional strategies and motivate students to build confidence in their abilities and to help them develop prosocial behaviors and also engage students in math classes in order to raise math achievement.

INTRODUCTION

Numeracy is one of the core skills that is emphasized, particularly in early childhood education, as it enhances academic success in all subject areas and cognitive development. This proves that the foundation of all higher education is mathematics (Duncan et al., 2020). In this modern day, a nation’s ability to successfully compete on the international stage is determined by the degree of mathematical literacy among its people (Sofowora, 2014). One may also consider mathematics to be a branch of science that deals with computation and the application of the cognitive realm. Math is used in many different disciplines, including design, software engineering, carpentry, and many more, despite the general public’s perception that it is an abstract subject (Kusmaryono, 2014). It has been established through research that, the knowledge acquired through the study of mathematics is essential for both scholastic success and efficient day-to-day functioning (Hodanova & Nocar, 2016). Learners’ arithmetic success is greatly influenced by their life circumstances, and the public and private education sectors have expressed serious concerns about their performance. (Ezenweani, 2006). The majority of senior high school and college students nowadays find it more difficult to understand mathematical concepts than any other topic, despite the importance and utility of mathematics in other disciplines and daily life as previously mentioned (Achieve, 2011). As a result, educational stakeholders have expressed concerns about students’ poor arithmetic proficiency, citing the detrimental effects this has had on the development of the country. As a result, over the past few years, researchers have been able to pinpoint a number of variables that affect students’ performance, particularly in mathematics (Reardon et al., 2009). The researchers found that a number of contributing elements have a significant effect on learners mathematics success and proficiency, including the teacher-learner relationship, leaners engagement and motivation, self-efficacy, learners’ perceptions of mathematics, collaborative strategies, leaners parents educational background and others. Numerous studies on the variables influencing pupils’ mathematical achievement have been conducted in response to this poor performance that have raised concerns. (Lasvani & Khandan, 2011; Appiah et al, 2022).

 Statement of problem

Following an in-depth examination of the literature, it became clear that a variety of variables, each with varying results and implications, could be used to forecast mathematical achievement. These variables included students’ motivation, their degree of self-efficacy, and the relationship between teachers and students (Callaman & Itaas, 2020; Lasvani & Khandan, 2011; Appiah et al, 2022). The problem at hand is that, out of all the papers we analyzed, it seemed that the majority of these studies that looked at more than one of these variables did not take into account students’ prosocial activities as a predictor of math ability. Research on the direct effects of combining two or more of these variables on arithmetic achievement is lacking, particularly when considering the prosocial actions of the students involved. To fill this gap in the literature, the researcher intended to look into the effect of students’ prosocial behavior and self-efficacy on their’ achievement in mathematics.

Purpose of the Study

This research seeks to investigate the effect of students’ prosocial behavior and self-efficacy on their’ achievement in mathematics.

Research Objectives

The following research objectives were determined after a thorough examination of literature:

1. To find out the effect of students prosocial behavior on students’ mathematics achievements.
2. To find out the effect of students self-efficacy on students’ mathematics achievements.

Research Questions

The study is centered on the following questions:

1. What is the effect of students’ prosocial behavior on students’ mathematics achievements?
2. What is the effect of students’ self-efficacy on students’ mathematics achievements?

Theoretical Framework

The current study is grounded in social cognitive theory. In the 1960s, Albert Bandura converted Social Learning Theory (SLT) into Social Cognitive Theory (SCT). In 1986, the Social Cognitive Theory was renamed to reflect its belief that learning should take place in a group environment and include constant, two-way communication between the individual, their environment, and their behavior. SCT stands apart for emphasizing friendly impact as well as constructive internal and external factors. SCT considers both the social context in which people acquire particular tendencies as well as the particular reasons that cause people to develop those tendencies. The theory considers the impact that an individual’s past experiences have on their decision to interact with others.

Figure 1: Conceptual framework showing the link between Students Self-Efficacy (SSE), Students Prosocial Behaviors (SPB), , and Students’ Mathematics Achievements (SMA).

MATERIALS AND METHOD USED

Study Design and Instrument

The research adopted a quantitative research approach through the lens of the positivist paradigm. A descriptive design survey was used, prior to data collection, school administration were consulted to ascertain the most convenient time for the researchers to visit the school and collect the required data. Upon reaching the school, the researchers gave a document confirming the authority’s approval to verify the ethical nature of the data to be collected there to the relevant person. Data were collected using a basic random sample strategy (Fraenkel et al., 2005).

In total, 260 students were selected from two high schools from Kumasi Metropolitan and Kwabre East Municipality out of which, 140 were males and 120 females, with an average age of 16 to 18 years old. Students were given questionnaire to complete in English. The questionnaire contained 49 question items in addition to personal information inquiries. The items were rated on a 5-point Likert scale, with 1 representing the strongest agreement and 5 representing the strongest disagreement. Also, the validity of the items was examined.

Table 1. Learners background information for a total of 260 learners

Source: Field survey (2024)

Validity and Reliability of instrument

Tests of the instrument’s face and content validity were conducted. The instrument was distributed to the researcher’s fellow program participants in order to guarantee face validity. The researcher’s fellow students examined the objects’ construction, alignment, structure, and arrangement in relation to the questions and goals of the study. Before the instruments were provided to the students, the necessary adjustments were made based on feedback from other students. The research supervisor was tasked with establishing the content validity of the instruments by comparing the items to the study objectives and questions. This allowed the supervisor to determine whether the instruments measured the intended subjects. The instrument’s remarks from the research supervisor were also utilized to make the necessary changes in the items.

In terms of dependability of the instruments to be ensured. Cronbach’s alpha method was employed to check the questionnaire’s internal consistency. Frequency tables were utilized to display and analyze the data for the student backgrounds as shown above. SEM (Structural Equation Modeling) was also employed. Exploratory factor analysis was used to assess the data set’s reliability and validity.

Table 2: Cronbach alpha values

Source: Field Survey,2024

Each of the three structures’ Cronbach alpha values was calculated. All three (3) of the constructs in Table 2 with all the fourteen (14) items have Cronbach alpha coefficients greater than 0.70. This suggests that the items are very consistent. Additionally, Table 2 shows that the construct of students’ self-efficacy had an alpha value of 0.853 with four measurement items, the students’ prosocial behavior had an alpha value of 0.888 with five items, and the student’s mathematics achievement had the highest alpha value of 0.967 with five measurement items.

RESULTS

Results of the Exploratory Factor Analysis (EFA)

An overview of the findings from the exploratory factor analysis is presented in Table 3. One way to characterize EFA is as an approach that emphasizes interconnected variables. According to Appiah et. al. (2022), EFA is a variable decrease strategy that separates the latent variables and the factors that support the character of a set of variables.

Total variance explained= 78.138%, KMO= 0.685, Chi-square value = 6227.237 and a degree of freedom level of 276. Significant level=0.000, Determinant=4.255E-04

Source: Field Survey, 2024

Table 3 displays the KMO metric of sample adequacy, which is 0.685—much higher than 0.5. As per Hair, J. F. (2009), this outcome indicates that there is a substantial correlation between the different items. With a degree of freedom level of 276 and a Chi-square score of 6227.237, Bartlett’s sphericity test was noteworthy. Bartlett’s test indicates that all factors have been considered and that the correlations are not nearly zero because they are considerably bigger than zero, with a substantial p-value of.000 (p<.001).

The determinant is 4.255E-04 from Table 3 is also considered to be pretty outstanding. Three (3) variables were needed to be retrieved, and the researcher utilized factor analysis to discover what variable is to be extracted. A total of five components were chosen resulting in 78.138 percent cumulative variance explained. In addition, the turn varimax and factor loading are displayed, along with the rotated component matrix. The rotating varimax method was employed because it has the ability to increase the normal yield while reducing the number of complex parameters. We looked at the items significance and relevance to decide whether or not they should be retained. Items loaded at different components and those with low factor loadings were deleted, and the model fit indices were checked each time an item was removed. The results from the Rotated Component matrix showed that 16 items were deleted. The rest of the factor loadings displayed within every aspect in table 3 loaded higher than 0.70.

Result of the Confirmatory Factor Analysis (CFA)

Table 4 summarizes the results for the Confirmatory Factor Analysis (C.F.A). The CFA results were adjusted to suit the measurement employed during the EFA process. The data was obtained from a respondent of 260 which is made up of the sample size for the study. And AMOS 24.0 was employed for the analysis. Also, few adjustments were done in other to approve the model such as, the elimination of items with very small loadings.

Table 4: Confirmatory Factor Analysis

Note. CA: Cronbach’s alpha; CR: Construct reliability; AVE: Average variance extracted; Source: Field Survey, 2024; & Model fit indices: Chi-square (CMIN)=401.906; Degree of freedom (df)=233 =1.725; (TLI) = 0.968; RMSEA=0.053; Comparative fit index (CFI)=0.973; & Goodness-of-fit index (GFI)=0.891, PCLOSE = 0.07

Table four (4) shows the results of the CFA together with the standard factor loading, it also provide information from two hundred and sixty (260) samples used in the research. The p value was 0.000, chi-square value =401.906 with degree of freedom of 233, CFI = 0.973 and RMSEA = 0.053. The ratio of chi square to degree of freedom which resulted in 1.725 is well accepted because it was not up to 3.0 according to (Hu, L. T., & Bentler, P. M., 1999). The CFI was equal to 0.973 since it was greater than 0.90, it confirms the validity of the model. Hence the data and the model are highly compatible (Hu, L. T., & Bentler, P. M., 1999)). GFI value was also 0.940 indicating that the model is reliable. Also, the value of RMSEA also yielded a result of 0.054 which is less than 0.08, indicating that the RMSEA was acceptable (Hu, L. T., & Bentler, P. M., 1999). As a result of these values, it indicates that the four variables are valid and acceptable. The other model fit indices which are NFI and TLI were both above 0.90 which also indicates that three model was well fit. These results yielded shows that in general terms, the model is fit and acceptable.

Figure 2: Confirmatory factor analysis (CFA) of the 3 variables.

Source: Field Survey (2024)

Result of the Path Analysis

The path analysis results summary (direct and indirect effects) have been displayed in table 5 below

Table 5: Hypothetical analyses results

Model Fit Indices: CMIN = 404.039; DF = 236; CMIN/DF = 1.712; CFI =0 .973; TLI = .968; NFI=0. 918; GFI= 0.891; RMSEA = 0.052; RMR=0.067 PCLOSE=0.072

Note. CI=confidence interval; LL=lower limit UL=upper limit.  B.S. E= Standard Error.  BCpCI= Bias Corrected and Accelerated 95% CI for 5000 bootstrap resamples of the sample size (260) Source: Field survey (2024)

The model is fit, as indicated by the chi square value of 404.039. Since the RMSEA of 0.052 is less than 0.08, it also indicates a well-fitting model. Hu, L. T., & Bentler, P. M. (1999) . the two model fit indices, the NFI and TLI, were both over 0.90, indicating that the model was similarly well-fit. For every component, the p value is less than 0.005, indicating statistical significance for the relationship between students’ self-efficacy and prosocial behaviors (SPB) and their arithmetic achievement. As a result of that student’s prosocial behaviors (SPB) and students’ self-efficacy as a predictor of student’s mathematics achievement appeared to have a very high favorable influence on students’ success in mathematics. Based on the research objectives and hypothesis, the indirect and direct effect have been explained below.

Figure 3: Path diagram

Source: Field Survey (2024)

Research Question one (1): What is the effect of student prosocial behavior on students’ mathematics achievements?

The first research question intended to bring to light the effect student’s prosocial behavior on senior high school students’ mathematics achievement answered by the direct path analysis (SPB→SMA). A structural equation model (Path analysis) was used to answer this objective using 5000 bootstrap samples and bias corrected percentile confidence intervals. The bootstrap samples were understood through the use of confidence intervals. For a particular result to be considered significant, both the upper and lower borders of the confidence interval must be of the same sign (‘+ +’ or ‘- -‘), meaning that both the lower and upper confidence intervals must exclude ‘0’. This indicates that if the confidence interval excludes zero (0) then the path coefficient cannot be zero. Table 10 displays the specifics of the outcome.

Table 6: the effect of students’ prosocial behaviors on students Mathematics Achievement

Note BCpCI= Bias Corrected and Accelerated 95% CI for 5000 bootstrap resamples of the sample size (260) Source: Field survey (2024)

Table 6 above suggests that the students’ prosocial behavior is a positive predictor of mathematical achievement, with a p-value less than 0.05. The analysis’s findings demonstrated that, at 5% statistical significance, the association between students’ achievement in mathematics and their prosocial behavior had a p-value of 0.000. The results based on the hypothesized routes show that the relationship between teachers and students can positively impact senior high school students’ mathematics achievement (β = 0.269; BCpCI 95% CI (0.140, 0.460)). This indicates that learner mathematics achievement would increase by 0.269 for every unit increase in students’ prosocial behavior. The study’s findings imply that students’ performance in mathematics can be enhanced by the degree to which students engage in helping their colleague students, which is solely focused on accomplishing academic objectives specifically in mathematics.

Research Question two (2): What is the effect of student self-efficacy on students’ mathematics achievements?

The first research question intended to bring to light the effect students’ self-efficacy on senior high school students’ mathematics achievement answered by the direct path analysis (SSE→SMA). A structural equation model (Path analysis) was used to answer this objective using 5000 bootstrap samples and bias corrected percentile confidence intervals. The bootstrap samples were understood through the use of confidence intervals. For a particular result to be considered significant, both the upper and lower borders of the confidence interval must be of the same sign (‘+ +’ or ‘- -‘), meaning that both the lower and upper confidence intervals must exclude ‘0’. This indicates that if the confidence interval excludes zero (0) then the path coefficient cannot be zero. Table 10 displays the specifics of the outcome.

Table 7: the effect of students’ self-efficacy on students Mathematics Achievement

Table 7 above suggests that student’s self-efficacy is a positive predictor of mathematical achievement, with a p-value less than 0.05. The analysis’s findings demonstrated that, at 5% statistical significance, the association between students’ achievement in mathematics and their self-efficacy had a p-value of 0.012. The results based on the hypothesized routes show that the relationship between teachers and students can positively impact senior high school students’ mathematics achievement (β = 0.360; BCpCI 95% CI (0.242, 0.791)). This indicates that learner mathematics achievement would increase by 0.269 for every unit increase in students’ prosocial behavior. The study’s findings imply that students’ performance in mathematics can be enhanced by the degree to which students believe in their ability to be successful in mathematics.

DISCUSSION OF FINDINGS

The effect of students prosocial behavior on their’ mathematics achievements.

The research finding after data analysis shows that, that students’ prosocial behavior is a positive predictor of their mathematics achievement. Which is aligns with several existing studies, suggesting a significant link between social behaviors and academic performance. Prosocial behavior, which includes actions such as helping, sharing, and cooperating, can create a positive learning environment, enhance peer interactions, and improve classroom dynamics. These factors can contribute to better academic outcomes, including in subjects like mathematics.

A research work done by Wentzel (2012) suggest that that students who exhibit high levels of social responsibility and prosocial behavior tend to perform better academically. The positive classroom environment created by prosocial behaviors promotes effective learning and academic success. Prosocial behavior, characterized by actions such as helping, sharing, and cooperating, can create a more positive learning environment. This, in turn, enhances peer interactions and improves classroom dynamics, contributing to better academic outcomes, including in mathematics.

Prosocial behaviors help build a supportive and collaborative classroom culture where students feel more comfortable and motivated to engage in learning activities. Such behaviors can reduce instances of disruptive behavior, increase student engagement, and foster a sense of belonging, all of which are conducive to academic achievement. Furthermore, prosocial students often develop better relationships with their teachers and peers, leading to increased opportunities for collaborative learning and peer support.

The effect of students self-efficacy on their’ mathematics achievements.

According to the study after the data analysis, one of the main positive predictors of students’ mathematics achievement is their level of self-efficacy in learning mathematics. The results suggest that students’ self-efficacy actually predicts mathematics achievement in the other way from what many research papers and theoretical frameworks have found, which holds that students’ self-efficacy is a negative predictor of mathematical achievement. In other words, children who exhibit strong mathematical self-efficacy but do not exhibit excessive confidence are more likely to excel in the subject. The results of this study are in line with those of Appiah et al. (2022) and Odiri (2022), who showed that students’ self-efficacy positively influences their math achievement in Kumasi Metropolis Senior High Schools. Based on the results, educators should assist their students in developing strong self-confidence when studying mathematics. According to the study, students’ perceptions of their academic aptitude impact how they apply their existing knowledge and talents, which in turn affects their academic performance.

The results of the study, however, do not support the findings of Moores & Chang (2008), who found that higher levels of self-efficacy can lead to overconfidence and, ultimately, a decline in students’ performance in mathematics. Furthermore, a Vancouver study from 2005 demonstrated that self-efficacy is limited to satisfaction with one’s performance level and might result in complacency, which in turn can have a negative correlation with performance.

CONCLUSION AND RECOMMENDATIONS

The findings of this research indicate that both prosocial behavior and self-efficacy are positive predictors of students’ mathematics achievement. This suggests that students who engage in cooperative and supportive behaviors, as well as those who have confidence in their own abilities to succeed, tend to perform better in mathematics. These results underscore the importance of fostering a supportive and confidence-building classroom environment to enhance academic outcomes in mathematics.

Prosocial behavior contributes to a positive classroom atmosphere, facilitating peer learning and reducing disruptions, while self-efficacy encourages students to tackle challenging problems with persistence and resilience. Together, these factors create an environment where students are both socially and psychologically equipped to succeed in mathematics.

RECOMMENDATIONS

Schools and teachers should encourage prosocial behaviors by creating collaborative learning environments, fostering a culture of helping and sharing, and integrating social-emotional learning (SEL) programs. This approach can indirectly boost mathematics achievement by improving the overall classroom climate. Again, teachers should employ strategies to boost students’ confidence in their mathematical abilities. This can include providing regular positive feedback, setting achievable goals, and encouraging a growth mindset. Helping students to see mistakes as learning opportunities rather than failures can also build their self-efficacy

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