Academic Achievement and its Psychological Correlates on School Students

Without students, schools, co-ops, and universities are nothing. The most valuable resource for every educational institution is its students. Student Academic Achievement has a direct impact on the nation's social and economic growth. Academic achievement of the students is crucial in creating the greatest graduates who will serve as great leaders and labourers for the nation, contributing to the economic and social growth of that nation (Alietal, 2009). Measurement of student academic achievement has attracted a lot of attention in past studies; it is a difficult component of students' lives. Psychological issues have an impact on academic achievement. These elements have a significant impact on students' academic achievement. Modern education has been strongly affected by psychology. There are many fundamental connections and relationships between psychology and education. Human behaviour has an impact on both educational theory and practise. The field of educational psychology was created in order to systematically examine student behaviour and support the educational process. This area of applied psychology is unique. It makes use of psychological knowledge in education while attempting to understand children's educational behaviour. According to Judd, "Educational psychology is the discipline that identifies and explains the changes that persons go through as they progress through different phases of development from infancy to adulthood. Evidently, educational psychology addresses a variety of issues related to teaching and learning. Education psychology is sometimes referred to as "the psychology of teaching and learning" for this reason. One of the most crucial metrics used to assess a school's efficacy is student academic achievement as shown by test results (Haertel, James, and Levin, 1987). Considering how school boards may improve students' academic performance, Student achievement is the best indicator of the value of education, according to Lashway (2002). Today, it is customary for educators, administrators, and law enforcement officials to begin their action plans by emphasising that student learning is the true measure of success. Hoffer (2000) also reached the same result. Numerous insights concerning variables that affect and correlate with academic success have been made by educational psychology. Schools are anticipated to have a variety of impacts on their pupils, from the inculcation of highly precise cognitive abilities to the inculcation of rather widespread attitudes and customs. The public is presently very concerned about how effectively schools accomplish all of their objectives, but the anxiety is highest with regard to the cognitive objectives of education. The influence of school on kids' personalities is crucial. Schools should serve more than only related courses; rather, they should help students develop to the fullest extent possible. information but also excellent habits, a stable personality, and emotional equilibrium. It is common knowledge that instructors place a greater emphasis on personality in many institutions. Academic achievement cannot exist in a vacuum; rather, it relies on a number of variables, such as the student's willingness to study. Studying practises, anxiety, adjustment, school climate, intelligence, and more. This has been shown by several study results.

LITERATURE REVIEW

Ngozi m. Eya , florence o. Attah (2020) The major results revealed, inter alia, that the attitude and academic performance of students in chemistry are closely linked, that motivation is strongly linked to students' academic performance in chemistry and that there is an important link between self-regulation and students' performance in chemistry. These results affect the job choices of students in scientific and engineering education in Nigerian universities. For sociopsychological variables and student achievements in chemistry, the substantial positive connection involves enhanced enrolled and better performing university-level students in science and engineering. Hariz Enggar Wijaya et. al, (2017) study in Tasikmalaya there were 96 students who were inscribed for the first year of Islamic boarding, 48.96% male, and 51.04% female. The Brief Self-Control Scale, Self-Adjustment Scale, and the final grading report gathered the data. Data study revealed the correlation between self-controlling and self-adaptation and academic performance of students. For additional research, many regressions have shown that academic achievements can only be predicted by self-control. Data research has also shown that males and girls vary greatly in academic performance, self-control and self-adjustment In order to explore the psycho-social correlates of teenage pupils' academic success, Deepa Franky and S. Chamundeswari (2014) randomly chose 96 students from secondary state board schools. Numerous psychological and sociological variables, including attitude, teacher effectiveness, adaptability, socioeconomic position, home environment, and school environment, have a significant impact on a student's academic progress. The key period of adolescence is when adjustment changes quickly and vary; one's social position affects the peer group they are in. Data analysis reveals that pupils at coeducational state board schools for boys and girls at the secondary level have a comparable adjustment trend. Further evidence suggests that co-educational kids do better than males in state board schools in terms of socioeconomic standing. The survey also found that co-educational children outperformed males and girls in state board schools in terms of academic success. This may be a result of the

The current situations, practises, and procedures are attempted to be described and interpreted; otherwise, the survey style of study would be more pertinent and informative. It is more concerned with the overall statistics of the population, or a sample thereof, than it is with the characteristics of particular people. The descriptive approach was appropriate for this study since the researcher was trying to find out how stress, curiosity, and behaviour deviation affected teenage students' academic performance. The "Influence of stress, interest, and behaviour deviance on the academic accomplishment of teenage pupils" or any other relevant questions have not yet been studied in Tamil Nadu.

There are several methods for gathering quantitative data. From each district, the researcher chose sixteen schools at random. The sort of school and its location were taken into consideration while making the choice. In order to control sampling mistakes and have a systematic variance, a systematic randomization was used since the sample sizes with regard to demographic characteristics were not distributed uniformly. 1120 students from these schools were chosen by a method of systematic random selection. The district name, class, gender, school type, school location, religion, parents' educational background, employment, and monthly income were taken into consideration while doing the systematic sample.

As independent variables with dimensions in the current research, tension, interests, and behaviour deviation were chosen. Community, caste, religion, culture, and regional tension are the aspects of tension. Work-related, religious, social, intellectual, and recreational interests are the aspects of interests. Expectation evasion, revolt, and withdrawal behaviour are the dimensions for behavioural deviation. Adolescent pupils' academic performance at the upper secondary level was regarded as the dependent variable. In the current research, demographic factors such as gender, management style, kind of school, location, religion, parental education, profession, and family's monthly income were also taken into consideration as extraneous variables. As a result, the researcher was able to determine how tension, interests, and behaviour deviation affected the academic success of teenage pupils. Additionally, researchers examined the impact of demographic factors on teenage pupils' academic performance. In the

A number of independent variables and one dependent variable are used in this inquiry. In order to determine the association between tension, interest, behaviour deviation, and academic accomplishment of teenage pupils, multivariate directed hypotheses were required. According to the associated literature, there is a lack of knowledge since there are so few investigations, and even those few studies lack a clear-cut focus. Thus, the current research is a ground-breaking investigation into the relationship between teenage students' academic success, stress, interest, and behaviour deviation. It was determined that extreme care would be used while constructing hypotheses since the nature of the interaction between these variables seems complicated.

The choice of appropriate instruments for the investigation determines the quality of the research in its whole. When choosing tools, one must exercise utmost caution since without the right instrument, one cannot expect to achieve the desired results (Singh, 2008). Therefore, the instruments used for this inquiry were made with the aims and relevancy of the study's purpose in mind. It was chosen to use standardised instruments since they were readily accessible and pertinent to the investigation. Inventory-based data collection was used to gather the information needed for this research; it served as a standardised exam for the pupils. The following are the instruments that were chosen to be utilised for evaluating the variables: I. Comprehensive scale of Tension by Rajeevlochanan Bharadwaj (1990) II. Multiphastic interest inventory by Dr.S.K.Bawa (1971) III. Behaviour deviance scale by N.Chauhan nd Dr.Saroj Arora (1987) IV. Academic achievement –Students examination scores

The science of statistics focuses on technique, which includes the methodical presentation, mathematical analysis, and interpretation of data as well as the drawing of conclusions about the investigated attribute in the relevant populations. Data was gathered, processed, aggregated, tabulated, and statistically analysed using SPSS (16.0 edition) software in order to evaluate the impact of tension, interest, and behavioural deviation on the academic accomplishment of teenage pupils. Regression

Finding the relationships between two or more variables is the goal of correlation analysis. Correlational statistical approaches are then used to analyse the data, which have been collected from many variables. The nature of the link between two or more variables, as well as any potential theoretical models that may be created and put to the test in order to explain these correlations, are among the many things that correlation research looks at. A correlation does not prove a cause. As a result, correlation research can at best allow the researcher to draw tenuous causal conclusions. Correlations may be thought of using a variety of different approaches, and these methods and statistical techniques can be thought of together. A bivariate correlation (contributions by Galton, Edgeworth, and Pearson, 1900) studies the correlation or relationship between two variables at the most fundamental level (hence the terms co-relation and bivariate). In certain circumstances one variable is known as an independent variable (or input variable) and the second variable as a dependent variable (or outcome variable) (or outcome variable). The table reveals the meaning of the letter "r." R is important if the estimated value exceeds the tabular value of N-2 degree of freedom. The significance thresholds that are taken into account are 0.01 and 0.05. The following rule may be used to determine the strength of a relationship between two variable

To verify this theory, the Pearson's coefficient of correlation has been obtained. The importance of the coefficient of correlation between the dimensions of stress and the dimensions of interests of teenage pupils is shown in the table below.

The computed "r" values in the aforementioned table demonstrate that there is a considerable correlation between the aspects of student stress and interest. Adolescent pupils' dimensions of stress and interests have a negative, small-scale, but statistically significant 0.01 level association coefficient. According to the negative association, teenage pupils' reported high levels of stress are likely to be accompanied by low or declining levels of interest. The negative correlation between adolescent students' dimensions of tension and their dimensions of interests suggests that if students exhibit high levels of caste, community, religious, cultural, and regional tension, they will not engage in activities related to their professional, religious, social, intellectual, and recreational interests. Analysis therefore indicated that in order to encourage students to channel their energy into worthwhile endeavours, tension has to be removed from the base of their social structure. Thus they may prepare for the future and ensure its prosperity.

Table 3's perusal reveals that the coefficient of correlation between teenage students' dimensions of behaviour deviation and their dimensions of interest is significant at the.01 level of significance. Therefore, it can be stated that behaviour deviation and interests are negatively connected in this sample, which suggests that as behaviour deviation increases, the degree of interests decreases. It makes sense that teenage behaviour may be influenced by their degree of interest. Adolescents who have low levels of professional, religious, social, intellectual, and recreational interests may behave inappropriately. Therefore, it's crucial to keep an eye on teenagers' interests and behaviour to avoid wasting their potential and energy. Table 4's perusal reveals that the correlation coefficient between general stress and general behavioural deviation is significant at the 0.01 level. As a result, it can be said that tensions and behaviour deviance are positively connected in this sample. This means that if caste, communal, religious, regional, and cultural tensions are seen to be greater, then teenage students' behaviour deviance is likely to be higher as well. Parents and teachers should place greater emphasis on group conflicts of a cultural, regional, religious, caste, and communal nature since teenagers are prone to establish groups during this developmental period in order to address the core reasons of deviance. Therefore, it is clear that caste, community, and religious conflict are statuses that affect how opportunities are available and may give birth to beliefs that justify transgression in teenagers.

The degree to which two or more variables are related in any co-relational study will need to be determined only after the variables have been quantified. It may be established how closely and in what direction two variables are connected by calculating their correlation. In this study, the basic correlation method is used to determine the association between one dependent variable and many independent variables. However, this method is not as sophisticated as regression analysis, which is often employed instead. While accounting for the influence of the other independent variables in the model, regression analysis also enables estimation of the relationships between each independent variable and the dependent variable. Regression uses this very potent characteristic to calculate the specific impact of each variable. Although the independent variables are quite flexible in multiple regression, the dependent variable must be evaluated on a continuous scale. They may be assessed using dummy variables to represent continuous, ordinal, or categorical data. It is necessary to ascertain whether independent factors predict the output or the dependent variable, as well as the degree to which each one affects prediction. To determine how much each independent variable in a correlation contributes to the dependent variable, correlation coefficients are determined. A sample of 980 students were used in the current

variables' average scores are regarded as predictor variables. With academic accomplishment as the dependent variable and three independent factors serving as predictors, multiple regression analysis was used to determine the cumulative impact of the study variables on the academic performance of teenage pupils.

The multiple regression by enter technique may be used to test the incremental validity, assess the contributions of predictors above and beyond those that have already been entered, and perform statistical control. The sequential approach of using the enter technique for multiple regression involves adding predictor variables to the analysis one at a time. The order in which the variables are entered into the analysis is based on theory, unlike stepwise regression. There is no "right" way for selecting the order of variable input, but as Kerlinger (1986) remarked, "depth of understanding of the study topic should decide the order of entry of variables in multiple regression analysis." In other words, mechanical model-selection and modification techniques "usually cannot make up for flaws in the data and are no replacement for judgement and thinking," according to Fox (1991). When predictor variables that are connected with one another explain variation on a criterion variable, multiple regression by enter technique is a suitable analytical tool (Pedhazur, 1997). Multiple regression is quite helpful in this situation since correlated variables are often found in social sciences research, and they are particularly common in educational research. After accounting for other factors, a frequent technique for analysing the impact of a predictor variable is multiple regression by entry. In order to account for the increase in variance when each variable (or group of variables) is added to the regression model, this "control" is performed by computing the change in the adjusted R2 at each stage of the study (Pedhazur, 1997).

The findings of the study's regression analysis, which had to be developed (entry technique), are shown below with a straightforward correlation matrix and regression coefficients. The correlation matrix between the factors examined for teenage pupils is shown in Table-5a.

The teenage pupils' aspects of tension (communal, caste, religious, cultural, and regional Tension) are shown to be significantly correlated in the table above. Adolescent students' tension is observed to be significantly correlated with their communal and caste tension, communal and cultural tension, communal and religious tension, communal and regional tension, religious and cultural tension, regional and religious tension, and cultural and regional tension. Regression that was determined to be suitable using the entry approach made up the second part of the investigation. The relative contributions of each independent variable to the dependent variable may be calculated using this technique. Here, a regression analysis's findings are provided (Table-5b-5d).

The regression equation for predicting teenage students' academic success using variables related to tension is shown in the above table. These variables include regional tension, cultural stress, religious tension, communal tension, and caste tension. The aforementioned table demonstrates that the dimensions of stress variable account for 15% of the variation in teenage pupils' academic success, or an R2 value of 0.145. The adjusted R2 value accounts for both the sample size and the number of predictors. The adjusted R2 value of 0.141 in this research for five dimensions of stress with 980 as the sample size shows that 14% of variation in teenage pupils' academic success. The entire regression is statistically significant at the p0.01 level, according to Table-5d on the ANOVA. (F14,965 = 5,974) The estimated standardised regression co-efficient is shown in Table 5d as "Beta" () values that represent the percentage of variance that each predictor variable directly contributed to. Higher scores on each of the tension scores are thought to be associated with worse academic success among teenage students since the signals for the dimensions of tension (communal tension, religious tension, caste tension, cultural tension, and regional tension) are all negative. According to the beta value, an effect's intensity may be divided into three categories: strong effects (beta > 0.5), weak effects (beta 0.2), and moderate effects (beta between 0.2 and 0.5). (chin et al 2003, Cohen 1977). For all categories of regional, cultural, religious, communal, and caste conflict, the percentage of variation explained in total tension is low. Using the values of the t and sig columns in the table, the statistical significance of the variance explained by each predictor is evaluated. All of the predictors, with the exception of caste and cultural friction, are shown to be significant. Therefore, it is also believed that regional conflict is the main element influencing teenage pupils' academic success. The regression equation for predicting teenage students' academic success using variables related to tension is shown in the above table. These variables include regional tension, cultural stress, religious tension, communal tension, and caste tension. The aforementioned table demonstrates that the dimensions of stress variable account for 15% of the variation in teenage pupils' academic success, or an R2 value of 0.145. The adjusted R2 value accounts for both the sample size and the number of predictors. academic success. The entire regression is statistically significant at the p0.01 level, according to Table-5d on the ANOVA. (F14,965 = 5,974) The estimated standardised regression co-efficient is shown in Table 5d as "Beta" () values that represent the percentage of variance that each predictor variable directly contributed to. Higher scores on each of the tension scores are thought to be associated with worse academic success among teenage students since the signals for the dimensions of tension (communal tension, religious tension, caste tension, cultural tension, and regional tension) are all negative. According to the beta value, an effect's intensity may be divided into three categories: strong effects (beta > 0.5), weak effects (beta 0.2), and moderate effects (beta between 0.2 and 0.5). (chin et al 2003, Cohen 1977). For all categories of regional, cultural, religious, communal, and caste conflict, the percentage of variation explained in total tension is low. Using the values of the t and sig columns in the table, the statistical significance of the variance explained by each predictor is evaluated. All of the predictors, with the exception of caste and cultural friction, are shown to be significant. Therefore, it is also believed that regional conflict is the main element influencing teenage pupils' academic success.

To the best of the researcher's knowledge, the current study is the first to try to analyse the impact of communal, caste, religious, cultural, and regional conflict, as well as the dimension of interest and behaviour deviance, on the academic performance of teenage students. These significant difficulties have an impact on the dependent variable and collectively decide the academic achievement of the pupils. Finally, the researcher strongly urges everyone involved in children's education to take corrective action to lessen the academic pressure on kids. In conclusion, educational institutions may assist in enhancing the academic performance of schoolchildren by working together with the school, instructors, and parents.

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PhD Student, Kalinga University, Raipur