Abstract
This study is an attempt to look at the academic achievement in three core subjects, such as First Language, English and Arithmetic, of the primary school students studying in classes II and III in External Evaluation and Diagnostic Achievement Test in West Bengal, India. We try to provide an insight into the group disparity in academic achievement of the students in the three subjects through classifying the sample by caste, gender and sector. Besides, we also employ the logistic model of regression to assess likelihood of success of different groups in the minimum level of learning of these three subjects in the said examinations. The study found considerable disparity in academic skills among social groups, males and females and rural and urban students. Performance of the students was quite poor in Arithmetic compared to First Language and English. Findings of this study will enable the policy makers to design appropriate policies, which will enable the society to achieve better performance in education in future.
Keywords
Introduction
Education is one of the most important processes of developing human resources. The amplified human resources through education have enormous influence on an individual’s social behaviour. Better education develops the decision-making aptitude of a person, which enables a person to be a responsible citizen, facilitates him to get better jobs and to achieve better standard of living in the future (Klees et al., 2012). In addition, education improves health, equalises the socio-economic status of the groups defined by different ways of classification of the population that leads to social cohesion and peace, and it also reduces inequality and alleviates poverty (Garriga & Melé, 2013; Oldekop et al., 2016).
Developing countries across the world have poor endowment of human resources due to poor endowment of education, which leads to further poor educational attainment, and a vicious circle prevails. However, almost 80 per cent of world’s children live in these countries (World Bank, 2016). Therefore, poor educational endowment in developing nations deprives a significant proportion of world’s children to achieve a prosperous future life. Like other developing nations, the condition of India was not good initially in terms of educational participation. Poor enrolment at the primary level, and high dropout at this starting level and in the subsequent levels of education were the features of Indian society. Realising the importance of education, the Government of India instituted the policy of universalisation of elementary education under Article 45 of the Indian Constitution. Subsequently, several other policies and programmes have been initiated by the government for the improvement of rate of enrolment and reduction in dropout rate. As a result, during past few years there have been a significant rise in the rate of enrolment and decline in the rate of dropout (the rate of dropout has declined to 4 per cent in 2016–2017 according to the Government of India, 2017, of the Ministry of Human Resource Development (MHRD), Government of India).
However, the consistent quantitative improvement in education through the rise in the rate of enrolment and decline in dropout rate is not possible without any qualitative improvement in education. In addition, improvement in the quality of education is also essential for developing skill and efficiency, which enable a person to achieve desired outcome of education in the labour market (Hanushek, 1995; Hanushek & Kimko, 2000; Hanushek & Woessmann, 2008).
Even though India has achieved better position in terms of quantitative improvement of primary education as per the MHRD, now the number of primary schools in India is around 1.5 million. However, still there are several deficiencies in primary education in the country, which prevent the qualitative improvement of education or system of education. These are insufficiency of financial resources for low levels of budget allocation (Drèze & Sen, 1995; Tan & Mingat, 1992), weak institutional structure of primary schools and weak public monitoring of education and indifference to education in general and primary education in particular (Drèze & Gazedar, 1996). The poor qualitative achievement leads to poor educational quality and poor academic performance (Cox & Jimenez, 1991; Das et al., 2004; De Paola, 2009; De Paola et al., 2013; Heyneman & Jamison, 1980; Heyneman & Loxley, 1980; Kingdon & Teal, 2007; Kremer, 2003).
In West Bengal, both gross and net enrolment rates have increased significantly during last two to three decades, and the percentage of out-of-school children has been declining rapidly during the last decade (Government of West Bengal, 2014). Furthermore, there has been an impressive qualitative improvement of education during the last decade. However, still the existing infrastructure and other parameters of qualitative improvement of education are not up to the mark. The geographic vastness and the huge population of West Bengal as well as huge diversity in socio-economic conditions make it difficult for the top tier of the government to effectively administer all the programmes and policies pertaining to education. Against this backdrop, the first objective of this study is to look at the academic performance of the primary students studying in classes II and III in External Evaluation and Diagnostic Achievement. In this context, we use the minimum levels of learning (MLL) as an instrument for measuring competencies of the students. 1
In the analysis of this study, instead of all subjects included in the curriculum we have taken the scores of First Language, English and Arithmetic of the students for comparability, since these three subjects are common for both classes of interest in the analysis of this study.
Language is a tool to express our knowledge and views to others in society. Therefore, language is very important in the educational process (Brown, 2000; Di Pietro, 1994). For these reasons, the First Language and English are included in our evaluation process. We include Arithmetic, which is a branch of mathematics, along with the languages as mathematics is the most international of all curriculum subjects. Actually, mathematical understanding can influence the decision-making power of an individual in all areas of life, including personal, social and civil. Moreover, mathematics enables people to make sound decisions and judgements to solve different problems. Furthermore, education in mathematics widens the post school opportunities to the students (Hernández, 2014; Klees et al., 2012). 2 Without good mathematical skill, students are likely to repeat the cycle of poor learning experiences and poor academic success (Gegbe et al., 2015).
One important factor influencing student’s ability to succeed academically is academic motivation (Margolis & McCabe, 2006). This academic motivation significantly influenced by quality of education and parental and family background (Gottfried et al., 1994; Lamb, 1981). From this causality between mathematics skills, academic success, academic motivation and inequality of opportunity, it can be hypothesised that poor opportunity may lead to poor skills in mathematics even at the primary level of education. This hypothesis is tested on the basis of the second objective of this study. According to this objective, we examine group disparity in MLL in West Bengal based upon the marks obtained in Arithmetic along with two languages mentioned earlier. We invoke the logistic model of regression to examine group disparities in MLL in West Bengal.
Caste is the most meaningful way of classifying the Indian population, as it defined the social groups with sharp differences in opportunities. Actually, in India there exist disparities among the social groups defined by caste in the spaces of well-being indicators. Therefore, it is usual that the circumstances endowed by the children from the disadvantaged social groups such as Scheduled Castes (SCs) and Scheduled Tribes (STs) are significantly poor than those of the advantaged groups. Consequently, this opportunity inequality plausibly leads to disparity in the success in MLL amongst the groups defined by caste. For this reason, to execute the second objective of this study, we classify the original sample of population by caste into four social groups, such as Higher Castes (HCs), Other Backward Castes (OBCs), SCs and STs. However, we combine HCs and OBCs and designate the group as ‘others’ and compare the achievement of this group with the disadvantaged groups of SCs and STs.
Along with caste-based classification, we also consider gender and sector as two other ways of classification, and examine the disparity in academic achievement between males and females, and the students of rural and urban areas of West Bengal. As all persons within different social groups, gender groups and geographically segregated groups are not alike, we combine caste and gender, caste and sector, and gender and sector together to classify the sample and evaluate group disparity in academic achievement separately for the three subjects.
The remainder of this article is structured as follows. The second section describes data sources and methodology used in the study. Section 3 discusses the results and Section 4 provides concluding remarks.
Data Source and Methodology
Data Source
The present study on the analysis of the academic performance of the primary school students in West Bengal draws on the data collected by the West Bengal Board of Primary Education (WBBPE), the apex body of government primary schools. The analysis of primary education in West Bengal is based on the performance of students through External Evaluation (EE) and Diagnostic Achievement Test (DAT) at the end of classes II and III, respectively.
School-wise data on the performance of students in EE and DAT have been collected randomly from all the districts in West Bengal. For a comprehensive study of the performance of the students at the end of class II, an equal number of schools (2,898, i.e. 6 per cent of the population) were selected from all the districts in West Bengal in each year for the period from 2007 to 2011.
In case of DAT at the end of class III, data have been collected for all the years from 2007 to 2011 for the work under study. A total number of schools, 2,093 (approximately 6 per cent of the total population), common for all the years 2007–2011, were selected randomly. The data of all the students of these schools were analysed in terms of the performance of the achievement in DAT for the 2007–2011 period.
Methodology
Initially, for the analysis of the data, we use descriptive statistics. This part of analysis only provides an insight into the overall academic performance in the primary schools in West Bengal. For the execution of the second objective of the study, that is, to analyse group disparity in academic performance in primary education in West Bengal, we consider a categorical response variable having two categories, such as ‘students passed in MLL’ and ‘students failed in MLL’. For this reason, we run the binary logistic or generalised logistic regression. The generalised logistic regression can be written in the following form:
where x is the probability that an individual participates in education;
As already mentioned, the categorical response variable has two categories. One is students passed in MLL and other is students failed MLL. The predictor variables included in the logistic equation are caste, gender and sector. However, as all persons in different social groups across the male and female samples, and rural and urban samples, are not alike, we classify the sample by taking caste and gender, caste and sector and genr and sector together. On the basis of this classification, we have six categories of caste and gder, such as HC-male (males of higher caste group), HC-female (females in the higher caste group), SC-male (males in the SC group), SC-female (females in the SC group), ST-male (males in the ST group) and ST-female females in the ST group). There are six categies while we classify the sample by caste and sector, such as HC-rural (members of the higher caste group living in the rural areas), HC-urban (members of the higher caste group living in the urban areas), SC-rural (members of the SC group living in the rural areas), SC-urban (members of the SC group living in the urban areas), ST-rural (members of the ST group living in the rural areas) and ST-urban (members of the ST group living in the urban areas). Classification of population by gender and sector together provides us four groups, such as rural-male, rural-female, urban-male and urban-female. Therefore, in this analysis we run four logistic regressions, where in the first there are three predictor variables—caste, gender and sector. The second model logistic regression includes sector and combined variable of caste and gender as predictor. Gender and combine variable of caste and sector are included as predictors in the third model. Finally, in the fourth model, we include caste and the combined variable of gender and sector. All categorical predictor variables have been included in the model after estimating the chi-square statistic. 3
Descriptive Statistics
Percentage Distribution of the Students in Different Categories of Percentage of Marks Obtained
Percentage Distribution of the Students in Different Categories of Percentage of Marks Obtained
Evaluation of the Results in First Language
There is no major disparity between STs and SCs while we evaluate the percentage distribution of the population of these groups across the six grades of marks obtained in First Language in EE and DAT. It can be observed that only around 5 per cent of the students of classes II and III got 0–9 per cent of marks in First Language of STs and SCs. The population of these groups almost symmetrically distributed over the grades or range of marks, that is, 0 to 50. However, while we focus on the percentage distribution of the students of ‘others’, then quite different results can be identified. The distribution of the population of this group is asymmetric or negatively skewed compared to the population distribution of the SCs and STs. This reveals a quite different success of the advantaged group compared to the disadvantaged groups in First Language.
Likewise the disparity among the advantaged and disadvantaged social groups, there exists significant disparity between males and females and rural and urban students in the marks obtained in First Language. It is observed from Table 1 and Figures 4–7 that at lower grades, females and students of the rural areas are concentrated more than those of the males and the students of the urban areas. The concentration of males and students from the urban areas are also greater at the higher grades of marks obtained in First Language. This indicates the failure of females and students of the rural areas to compete with the males and the students of the urban areas in West Bengal.
Evaluation of the Results in English
Table 1 and Figures 2, 5 and 8 provide the information on the success of the students from different social groups, gender groups and sector groups in English examination in EE and DAT of classes II and III. Disparity between ‘others’ and STs is sharper than the disparity between ‘others’ and SCs. Unlike the results in the First Language, there exists significant disparity between STs and SCs in the performance in English language. The percentage distribution of the students of these groups reveals the relatively highest concentration of the students of STs and lowest concentration of the students of ‘others’ at lower grades. Opposite is true at higher grades, that is, highest percentage of students of ‘others’ is concentrated at these grades and lowest percentage of students of STs is concentrated at these grades. The percentage distribution of the SCs also follows the percentage distribution of the STs, but the condition of this group is quite better than that of STs.
Numerical figures reported in Table 1 and the figures developed from these reported percentage distribution of population of males and females, and students of rural and urban areas reveal that there is no significant difference between males and females in the performance in English examination in EE and DAT. Even though there is disparity between the students from rural and urban areas in West Bengal in English, this disparity is less than the disparity between the students of rural and urban areas in First Language. In all these cases, the percentage distribution of students follows almost the normal distribution.
Evaluation of the Results in Arithmetic
In Arithmetic test, quite different results can be noticed. At higher grades, the concentration of the population declines rapidly in case of Arithmetic test compared to the First Language and English tests irrespective of the way of classification of the sample population of West Bengal. Therefore, the distribution of population follows a positively skewed distribution.
While we consider caste as the way of classifying population, then disparity between advantaged and disadvantaged groups, that is, disparity between ‘others’ and SCs and STs can be observed, even though for all of these groups the concentration of population declined rapidly at higher grades of marks obtained. Likewise, the First Language and English, in the case of mathematics, the concentration of population of ‘others’ is relatively greater at higher grades of marks obtained in Arithmetic. However, there is no sharp disparity between the SCs and STs in this respect.
The percentage distribution of population across the grades of marks obtained in Arithmetic is sharply different from the First Language and English, as in this case females are relatively in advantageous position. The concentration of females at higher grade of marks obtained is higher than that of males, and concentration of males are quite higher at lower grades of marks obtained than that of females.
If population is classified by taking sector as a way of categorisation, then there is no significant disparity between the cases in Arithmetic and First Language, and Arithmetic and English. Figure 9 depicts relatively higher concentration of the urban population at higher grades of marks obtained compared to rural population.
Comparative Analysis of Marks Obtained in Three Subjects
Figure 10 is the diagrammatic representation of the percentage distribution of population across the grades of marks obtained in First Language, English and Arithmetic. This figure reveals that at the middle grade/s, the concentration of population is greater irrespective of the subject. However, while we move towards the tails of the distributions, it can be identified that the concentration of population is greater at lower grades for Arithmetic compared to First Language and English. This indicates the existence of poor educational opportunities in terms of quality of teacher and pupil–teacher ratio and so on.
Econometrics Results
Variables Taken in the Econometric Analysis
Sample Characteristics and Descriptive Statistics
Results of Logistic Regression (First Language)
Results of Logistic Regression (English)
Results of Logistic Regression (Arithmetic)
Results of Logistic Regression in First Language
In the first model, we include caste, gender and sector simply as regressors. ‘Others’ is taken as reference category for caste, male is taken as the reference category for gender, and rural is taken as the reference category for sector.
It can be observed that both SCs and STs are less likely to pass in MLL in First Language compared to ‘others’. Females are significantly less likely to pass in MLL compared to males, and urban students are more likely to pass in MLL in First Language.
In the second model, we include sector and combined factor of caste and gender as the regressors, where others-male is taken as the reference category. The results indicate that there is no significant difference between others-males and others-females, but members of SC-males, ST-males, SC-females and ST-females are significantly less likely to pass in MLL in First Language. According to the estimation results of model 3, where we include combined factor of caste and sector along with gender as the regressors, others-rural is taken as the reference category. As expected, results indicate that there is no significant difference in the likelihood of pass in MLL of First Language between others-rural and urban-STs. Other results are almost matched with expectation. Members of ‘others’ residing in urban areas are significantly more likely to pass in MLL compared to members of ‘others’ residing in the rural areas. Members of SCs living in rural and urban areas, and the members of STs living in rural areas, are less likely to pass in MLL in First Language compared to the members of ‘others’ living in rural areas. Lastly, the result of model 4 depicts the disadvantages of females irrespective of the geographic disparity in residence.
Results of Logistic Regression in English
As in the case of First Language, we run four models of logistic regression for English. In the first model, we include caste, gender and sector separately as the regressors; in the second model we include sector and the combined variable of caste and gender as the regressors; in the third model we include gender and the combined variable of caste and sector as the regressors; and finally in the fourth model, we include caste and the combined variable of sector and gender as the explanatory variables.
Results of the logistic regression of model 1 in English are quite similar those of model 1 in the First Language. The difference lies only in the estimated coefficient of females, as this result indicates that there is no significant disparity in the likelihood of passing in MLL of English between males and females. This result corroborates the finding of the descriptive statistical analysis.
Results of model 2 reveal no significant disparity in the likelihood of passing in MLL of English between the males of others and SCs. However, females of ‘others’, SCs and STs are significantly less likely to pass in MLL of English compared with males of ‘others’. Likewise, for the First Language, the estimation results of model 3 found no significant disparity to pass in MLL of English between the members of others residing in rural areas and members of STs residing urban areas. Other results of this model display the lagging behind of the rural sector. Finally, results of model 4 indicate that rural males and urban females are equally likely to pass in MLL of English, but rural-females are significantly less likely to pass in MLL of English compared to rural-males, and urban-males are significantly more likely to pass in MLL of English.
Results of Logistic Regression in Arithmetic
The results of logistic regression of model 1 display absolutely similar facts as in the case of First Language, that is, SCs and STs are significantly less likely to pass in MLL of Arithmetic, females are significantly less likely to pass in MLL in Arithmetic, and people residing in rural areas are significantly less likely to pass in MLL in Arithmetic.
According to the estimation results of model 2, it is clear that there is no significant difference between others-males and ST-males in the likelihood of passing in MLL in Arithmetic. Results of this model also reveal the less likelihood of females to pass in MLL in Arithmetic compared to males irrespective of their affiliation in social groups. The estimation results of model 3 indicate that individuals residing in the rural areas are equally likely to pass in MLL irrespective of their affiliation to ‘others’ and SCs, but STs living in rural areas are significantly less likely to pass in MLL of Arithmetic compared to the members of ‘others’ and SCs living rural areas. Moreover, urban residents are significantly more likely to pass in MLL compared to the rural residents irrespective of their affiliation to caste groups. In other words, individuals living in the urban areas are more efficient for doing the sums compared to the individuals living in the rural areas.
Lastly, the estimation results of model 4 reiterate the superiority of the urban residents for doing the sums compared to the rural persons. Actually, rural females are significantly less likely to pass in MLL of Arithmetic compared to rural males. However, urban females and males are more likely to pass in MLL of Arithmetic.
This study is an attempt to evaluate the academic performance of the students of primary schools in West Bengal. We have used the data provided by the WBBPE on the marks obtained by the students of classes II and III in the EE and DAT at the end of the respective classes.
The findings reveal that as in the case of between-group inequality in educational achievement and in some other spaces of well-being indicators, there exists inequality among social groups in academic performance in India. The distributions of marks obtained by the students in EE and DAT of three core subjects in the syllabus of the primary schools of West Bengal across the social groups reveal the social group disparity in the grades of marks obtained by students. This group disparity is sharp in the distribution of marks obtained in Arithmetic and English compared to First Language. As in West Bengal a major proportion of schools are Bengali medium schools, and the First Language is Bengali in these schools.
The higher disparity in English and Arithmetic is the outcome of poor performance of the so-called disadvantaged groups in these subjects compared to the advantaged group ‘others’. The reason behind this sharp disparity and poor performance of the disadvantaged groups of SCs and STs is the poor family background of the students. According to the findings of Bourguignon et al. (2007), Ferreira and Giganoux (2007, 2011), Checchi and Peragine (2010) and Singh (2012), family background can influence educational achievement. Moreover, Smith (2007) and Amadi and Opuiyo (2018) found that family background can motivate students towards education, which leads to better academic performance and obtainment of better marks in examinations. Caste is the most meaningful way of the classification of the Indian population, which defines four social groups: HCs, OBCs, SCs and STs. 4 According to data availability, we combine HCs and OBCs in this study and designate the group as ‘others’. Initially, this age-old classification was done on the basis of occupational status. The so-called disadvantaged social groups SCs and STs were concentrated at the bottom of the occupational hierarchy. Therefore, members of SCs and STs are inherently endowed with poor economic status and family background in terms of parental education and occupation. 5 Therefore, students from these disadvantaged groups are motivated to education as the students from the advantaged group ‘others’. Moreover, parents from the former groups do not take education of their children properly, which leads to poor academic achievement in these difficult subjects and prevents these students to continue education after a certain level.
The disparity between individuals residing in rural and urban areas is typical, as the students from the former areas get relatively poor opportunities in the context of their education. Pradhan et al. (2000) and Hnatkovska et al. (2012) found significant disparity between rural and urban areas in space of education, occupation, income and consumer expenditures or economic status. The poor economic status of major proportions of the households in rural areas and poor opportunities in the rural sector leads to better performance of rural students in EE and DAT compared to the students in urban areas.
Rustagi (2005) and Das and Pathak (2012) found sharp disparity between males and females in India in occupation, income, educational status and so on. The foremost reason behind this gender disparity is the within-household deprivation of the female child in terms of the allocation of household resources for human capital accumulation due to the asymmetry in the parental incentives of educational attainment of males and females (Rosenzweig & Schultz, 1982). The finding this study corroborates the findings of the existing studies on poor educational performance of females in India.
It is clear from the results of model 2–4 of logistic regressions that all persons across the social groups are not alike in terms of academic performance. All females and males are not alike, and all residents of rural and urban areas are not alike. Some important findings in this respect are: (i) equal likelihood of males of ‘others’ and SCs in passing MLL in English; (ii) equal likelihood of the members of ‘others’ and STs in rural and urban areas in qualifying in MLL of First Language; (iii) equal likelihood of the males from ‘others’ and STs in Arithmetic; and (iv) equal likelihood of ‘others’ and SCs in rural areas in Arithmetic. As expected, other findings corroborate the findings of earlier studies on social group disparity, gender disparity and sectoral disparity.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.