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MALAYSIAN ONLINE JOURNAL OF
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21
[1]
International Islamic University
Malaysia, Malaysia
[2]
Khulna University,
Bangladesh
Corresponding Author:
Khulna University,
Bangladesh
E-mail: tanvirku05@soc.ku.ac.bd
DETERMINANTS OF ACADEMIC ACHIEVEMENT AT
SECONDARY LEVELS: A STUDY IN MAGURA DISTRICT OF
BANGLADESH
Suraiya Sultana Sumi1, Bijoy Kumar Mondal2, Nusrat Jahan2, Aysha
Seddeque2 & Md. Tanvir Hossain2*
ABSTRACT
The purpose of this study was to identify the factors influencing academic
achievement of secondary students in Magura district of Bangladesh. Using survey
design, this study was carried out in a total of eight educational institutions,
including schools, colleges, and Madrasah, both in urban and rural areas of
Magura district. Administering a self-reported questionnaire (SRQ), segmented
into four different modules, data were collected from 566 students of various
levels of secondary education system following proportionate multistage random
sampling. The exploratory factor analysis (EFA) suggests a four-factor solution, and
the role of teacher’ explained the most variations. The hierarchical regression
analysis show that academic achievement of secondary students was influenced
by track of education (0.173, p < 0.001), education (-0.221, p < 0.001),
socioeconomic status (0.137, p < 0.05), location (0.176, p < 0.01) as well as size of
class (-0.068, p < 0.10), academic stress (-0.071, p < 0.10) and motivation (0.145, p
< 0.001). Despite some limitations, this study has contributed empirically to a
limited literature on academic achievement of secondary students in Bangladesh.
Based on the results, it is strongly suggested that the government should
implement an education system involving all the stakeholders, including students,
parents, teachers, and educational administrators to re-direct future education
policies and strategies to achieve an all-inclusive and equitable education for all.
Besides, the policymakers should address the socioeconomic composition of
schools, colleges and similar educational institutions and need to provide
adequate human, financial and technical resources to improve overall educational
and learning environments to achieve a sustainable education system.
Keywords: Academic performance, Socioeconomic status, Institutional
characteristics, Secondary education, Parents and teachers, Academic
motivation, and stress, Bangladesh
JANUARY 2022, VOLUME 10, ISSUE 1, 21 - 44
E-ISSN NO: 2289 – 4489
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INTRODUCTION
After independence in 1971, Bangladesh set its goal to arouse patriotism and good citizenship to effect planned
social transformation and advancement through trained individuals with competence and ability to achieve
favorable economic development (Ministry of Education, 1974). Hence, all successive governments of Bangladesh,
whether elected or selected, along with their development partners advocated the improvement of education
sector, both human and infrastructural, by implementing numerous policies and strategies, such as ‘subsidized
universal primary education,’ ‘female stipend programs,’ ‘supply of free text books,’ ‘reaching-out of school children
(ROSC),’ ‘school feeding programs,’ ‘education for all (EFA)’ agenda, ‘second chance education programs,’ ‘provision
of appointment and training of female teachers’ etc. (Kono, Sawada, & Shonchoy, 2018; Ministry of Finance, 2017;
Rahman & Islam, 2009). All these initiatives are designed to make sure of quality and equitable education for all,
irrespective of class, caste, location, religion, ethnicity as boasted by the constitution of Bangladesh (Ministry of Law
Justice and Parliamentary Affairs, 2011). These interventions, however, required a huge sum, and over the last three
decades, the public spending in education, indeed, has increased progressively from a meagre 0.3 percent in 1973-
80 to 1.9 percent of the GDP in 2015-2016, and by the fiscal year of 2018-2019 it is expected to rise up to BDT 53,054
crore (Bangladesh Bureau of Educational Information and Statistics, 2016; Habib, 2018).
Hence, Bangladesh has experienced a breakthrough in respect to overall rise of enrolment in primary education with
greater gender parity followed by increased completion rate in the last three decades (Ahmed, 2011; Bangladesh
Bureau of Statistics, 2015b). During the same period, the number of primary schools increased almost three folds,
from 39,914 to 1,08,537, the enrolled students doubled from 8.4 million to 19.5 million, while the sum of teachers
involved in primary education soared from 0.1 million to 0.4 million (Bangladesh Bureau of Statistics, 2015b; Nath,
Chowdhury, & Ahmed, 2015).
With the increased universal primary education, perhaps the most notable change took place in secondary education
– the terminal point of primary education as well as the building blocks to the rest of education levels and
professional skills (Alam, 1994). It is noteworthy that the secondary education in Bangladesh has been divided into
three clusters – junior secondary education (from Class VI to Class VIII), secondary education (from Class IX to Class
X) and higher secondary education (from Class XI to Class XII) (Bangladesh Bureau of Statistics, 2015b; Kono et al.,
2018). The net enrolment rate rose from around 20 percent in 1980s to 47.2 percent in 2013 with greater gender
parity (Alam, 1994; Bangladesh Bureau of Statistics, 2015b). Bangladesh also experienced a sheer growth in numbers
of both educational institutions as well as students. For example, there was less than seven thousand secondary
educational institutions in 1970s educating over a million students. Now, there are twenty thousand dedicated
secondary schools and Madrasahs across Bangladesh, offering education for 10 million junior secondary and
secondary students (Bangladesh Bureau of Educational Information and Statistics, 2017; Nath et al., 2008; Rahaman,
2017; Schurmann, 2009), and an additional two and a half-thousand colleges committed entirely for intermediary
(higher secondary) education (Bangladesh Bureau of Educational Information and Statistics, 2017).
Keeping pace with the increasing number of enrolments together with the intensified quantity of educational
institutions and educators, the performance of secondary students in public examinations also amplified over the
last few decades. According to Bangladesh Bureau of Educational Information and Statistics (2017), more than 0.4
million students participated in secondary school certificate (SSC) and another 0.3 million took part in higher
secondary certificate (HSC) examinations back in 1990, out of which around 32 percent for SSC and 30 percent for
HSC passed the hurdles, respectively. In 2017, however, 1.4 million examinees sit for SSC examination, followed by
approximately 1 million for HSC examination, and surprisingly more than 80 percent of SSC examinee successfully
completed their SSC examination whereas the number was 66 percent for HSC examination. Out of the total students
in SSC and HSC examinations, ‘GPA 5’ – the highest grade achievable by the students – was attained by 97 thousand
students in SSC examination and it was 33 thousand for HSC examination.
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The performance of students – whether in primary, secondary or tertiary – cannot be explained by governmental
and non-governmental policy interventions only. Undeniably, there are some other factors, such as personal,
familial, institutional, or psycho-social issues, that are critically affecting the participation and learning achievement
of students. This study is, therefore, designed to examine how personal attributes, socioeconomic status,
institutional characteristics, and relevant psycho-social factors are influencing the performance of secondary
students in public examination, and to identify the factors critical to explain academic achievement in the context
of Bangladesh.
Theoretical Framework
Coleman (1988) mainly focused on three major forms of capital: human, financial, and social capital. Human capital
includes ‘skills and capabilities’ that enable individuals to ‘act in a new way’ by bringing desired changes; financial
capital comprised of ‘tangible’ resources, often measured by ‘wealth or income’; and social capital consisted of
‘elements of social structure’ to facilitate ‘certain actions of actors’ through changes in ‘relations among persons’,
and combined with other ‘capitals’, it can produce different ‘system-level-behavior’ and ‘different outcomes’ for
individuals. Coleman (1988) considered all these elements are mutually inseparable as human capital assures a
cognitive environment – with time and intellectual resources – for children to aid learning, financial capital provides
the physical resources – such as home, learning materials and other financial resources – to support academic
attainment, and social capital ensures a broader social network with stronger social relations within and outside
family to offer more conducive and better academic opportunities for individuals.
Coleman (1988), analyzing social capital, delineates three more integral concepts, such as obligations and
expectations, information channels and norms and effective sanctions. In a social structure, individual actors interact
with others and contribute, overtly or covertly, for well-being of others – family, friends, or neighbors – for mutual
dependence (obligations) trusting the recipients will reciprocate the favor in future (expectations). However, the
obligations and expectations, according to Coleman (1988), is subject to ‘degree of affluence’, ‘cultural differences’,
and ‘closure of social networks’. The concept ‘information channels’refers to the sources of information – within
(parents, siblings and relatives) and outside (religious, educational or economic institutions) family – inheres in
social relations that facilitate actions of individuals. Information, in general, are acquired through interaction, and
people often maintain social relations to get information from reliable sources to satisfy personal interests. The
norms and effective sanctions are a set of standardized behavior pattern, supported through external rewards
(social support, status, and honor) or disapproval for actions, that reinforce collective interest over personal gain.
Effective norms not only facilitate certain actions, but it can also obliterate deviant behavior and actions from the
community as well.
Coleman (1988) argued that social capital, both in the family and in the community, plays decisive roles in the
achievement of next generation. There is no denial to the fact that ‘financial’ and ‘human’ capital of parents are two
strong determinants of children’s intellectual development. However, they may become ‘irrelevant’ if children do
not have strong relations with parents. If fact, ‘structural deficiency’ in modern families, such as ‘single-parent’ and
‘dual income’ families where children do not have access to parental human capital – both physical presence and
Human capital
Financial Capital
Social capital
Academic Achievement
Figure 1. Theoretical framework
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attention of guardians, may generate different educational outcomes, including weaker academic achievement and
even dropout. Apart from family, as Coleman (1988) extended, other social relations within community, the social
relations in educational institutions in particular, add some values in the academic progress of young educands.
‘Types of school’, for example, is a useful indicator of social capital. Students from private schools are less integrated
within and outside their schools which, in turn, adversely influence the academic success. Besides, school proximity,
movement of family adjacent to the educational institutions as well as parental involvement in educational
instructions also shape the academic success stories.
LITERATURE REVIEW
There are a handful of studies in Bangladesh assessing the academic achievement of students and the factors
influencing their performance, however, almost all of them focused largely on primary education. Mohsin, Nath, and
Chowdhury (1996), for example, aimed at identifying the socioeconomic factors associated with the academic
competency of children. Administering random cluster sampling, a total of 2,520 children aged 11-12 were surveyed.
The results suggest that basic competency as well as academic skill of an individual was significantly influenced by
sex and education of educands, as well as parental education, economic condition, and location of household, where
male child often outperformed female child and urban children outdone their rural counterparts academically.
An earlier study by Alam (1992) assessed the performance of non-governmental schools in public examinations at
secondary level by constructing four regression models. Results suggest that passing rate in secondary school
certificate examination was influenced by location of school as well as regular salary for teachers. The achievement
of first division was also determined by regular salary for teachers as well as school committee meetings. It is,
however, evident that performance of students was negatively influenced by number of teachers per hundred,
suggesting greater the size of class lesser the number of pass rate.
Asadullah (2005), in his study, attempted find out how the class size influenced the pass rate in national public
examination. Based on a national representative sample on secular secondary schools only, the findings showed that
the performance in public examination at union levels was significantly influenced by class size, number of total
enrolments in Class X and type of school, however, there was no association between shift of schools, single-sex or
co-education system and teacher-student ratio. At district levels, it appeared that students’ performance was
influenced substantially by competition among schools, number of enrolments in Class X, type of school and single-
sex education. However, the academic achievement was negatively related with girls’ only school, whereas boys’
only school was positively related with public examination outcomes.
Following their footprints, Nath (2012) explored the factors associated with learning achievement of primary school
students. The author considered a total of twenty-one variables under three large sets of factors – socioeconomic
factors, school-related factors, and some educational input issues, against a competency test. The results of bivariate
analysis suggest a positive correlation between academic competency and sex, location, parental education, access
to mass media and electricity, participation in supplementary and pre-school courses. Moreover, the characteristics
of teachers, including educational qualification, working experience and professional training, were also positively
correlated with competency test result. In contrast, age and class size were inversely correlated. The results of
ordinary least square (OLS) regressions show that socioeconomic factors, e.g., age, sex, ethnicity, religion, areas of
residence, father’s education etc., followed by institutional characteristics were more crucial to explain academic
performance of primary school students in Bangladesh.
In a study on ethnic minorities in northern Bangladesh, Uddin (2017) examined the association between academic
attainment and ethnic identity together with other socioeconomic factors of primary students. The results of
bivariate correlation suggest significant correlation between ethnicity and academic attainment, where class
enrolment was negatively correlated. The regression results indicate that ethnic identity and household income
were the most significant predictors of primary class enrolment in Bangladesh. The findings also show that the class
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attendance of the individual in primary schools was dependent largely on ethnic identity as well as the punishment
by the teachers. The latter factor is responsible for irregular class attendance, which eventually shape the academic
performance of the young educands.
In a more recent study, Richards and Islam (2018) measured the ability of primary school students in reading and
solving mathematical problems. The authors observed a positive association between grades and academic
competency of primary students, suggesting students’ reading and mathematics performance improve at higher
grades. Besides, parental education and household food affordability were also significantly influencing the
academic competencies of school students. The presence or absence of electricity has no effect, however. But the
results suggest that the reading and mathematical competencies of students from NGOs tend to decline at higher
grades.
There are a number of relevant studies carried out in Asia, Africa and other parts of the world, stressing on various
issues related with academic achievement. Saeed, Gondal, and Bushra (2005), for example, investigated the levels
of achievement of primary school students of Pakistan and the factors, including family background, habits, and
academic elements, shaping the achievement. Their findings show that boys both in Grade 3 and Grade 5 performed
better in mathematics than girls, however, girls outperformed their counterparts in Urdu and life skill test. When
compared geographically, rural students performed relatively better than those of urban areas in all three
assessments. The results also suggest a wide range of factors, including parental, habit, and academic factors, were
associated with academic achievement of both Grade 3 and Grade 5 students of Pakistan, whether negative or
positive. Among them the more pronounced were family size, parental education and motivation, self-motivation
and effort and teacher’s guidance etc.
Salfi and Saeed (2007) carried out a study in the Punjab province of Pakistan to investigate the relationships among
school size, school culture and academic achievement. Following survey research design, covering a substantial
number of schools, as well as head and other teachers of various categories, the authors found a negative correlation
between school size and academic achievement, meaning smaller schools performed better than medium and larger
schools in examinations. School size was also negatively correlated with academic culture of school. On the contrary,
school culture was positively related with the academic achievement of students, suggesting students from schools
with better communication between teacher-student, between teacher-parent, and with qualified teachers,
performed better academically.
Ready (2010) investigated the impact of class attendance on academic achievement with a stress on socioeconomic
variations. The findings suggest that class attendance, both at kindergarten and First-Grade, was influenced by
socioeconomic status, type of family, race, language spoken in household as well as grade repeater. Students from
well-off and English-speaking families were less likely to be absent in the class, both at kindergarten and First-Grade.
On the contrary, students from single-parent families, non-white and non-Asian and grade repeaters were more
likely to be absent in the class. The regression analysis indicates that individual’s early literacy development was
influenced largely by socioeconomic status and class absenteeism. The latter, however, was negatively associated
with both literacy development and academic achievement.
Tayyaba (2010) wanted to figure out the mathematics achievement of government middle school students of
Pakistan and the extent to which personal, family, institutional and regional factors determined the achievement.
Findings suggest that among demographic variables, male students performed relatively better than female
students, similarly urban students were outperformed by their rural counterparts. However, mathematical
achievement tended to increase with an increase in socioeconomic status of the Grade VIII students. Among home
background variables, location (urban) and language (Urdu) seem to have negative relation with mathematical
achievement, while mother’s education, on the contrary, had positive impact. Among homework variables, student’s
frequency, and time to homework as well as help from parents to do home task proved to have positive impact on
the achievement of mathematical test. However, excessive home-based mathematics exercise showed a negative
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effect on the achievement. The results suggest that student’s self-confidence, positive attitude, and preference of
mathematics over other subjects, along with parental communication and expectations, had positive effect on
subject efficiency. It is also appeared that the mathematical mastery was influenced positively by smaller class size,
mixed education system and academic resources as well as physical facilities of the institutions.
Benner, Boyle, and Sadler (2016) assessed to what extent parental educational involvement influence the academic
achievement of school students. The results of bivariate correlation suggest significant correlation among parental
education involvement (at home, at school, expectation, and advice), socioeconomic status of family and
educational achievement, however, parental involvement at home was negatively related with academic
performance. The regression analysis indicates that all the variables, except for home involvement and advice,
together with family SES had positive impacts on grade point average.
Schultz (1993) attempted to find out the effects of socioeconomic status and academic motivation on academic
achievement. The findings show a positive correlation among academic achievement and socioeconomic status with
mathematics and reading achievement. The results of multivariate analysis of covariance also exhibit that student
from better off families outperformed those of less advantaged socioeconomic families. Similarly, Schultz also found
that students with greater academic motivation performed way better than those of less motivated.
In contrast to Schultz (1993), Deb, Strodl, and Sun (2014) wanted to examine the prevalence of academic stress and
examination anxiety among secondary students of private schools in Kolkata, India, and its association with various
demographic, socioeconomic as well as academic parameters. The findings suggest that the academic achievement
depends both on personal factors as well as socioeconomic status. For example, male students had better grades
than female students, on the contrary, female students were more efficient in English than their male counterparts.
Students from well off families reported high academic grades and English proficiency. Similarly, the academic
attainment was upward for students whose parents were more educated and involved in fixed income and white-
collar jobs. About academic stress and anxiety, it appears that students from low socioeconomic families were more
stressed and anxious during examination compared to those of high socioeconomic families. It is also evident that
students having low grades and less English efficiency were suffering from more academic stress, examination
anxiety and often being pressured by their parents for better academic results.
Contrasting to the aforesaid studies, as they assessed the academic achievement of school students mainly, Yousef
(2013) tried to find out the performance of undergraduate business students of UAE in quantitative courses and its
determinants. The author found several factors significantly associated with performance in quantitative courses.
Among the key factors, track of education, sex, age as well as grade point average (GPA) were influencing quality
points in quantitative course. Students of science background in high school performed better than those with art
background. Similarly, female, and younger students outperformed male and older students, respectively. The
Financial Capital
(Father’s income and educational expenditure)
Human Capital
(Parental education and father’s occupation)
Social Capital
Obligations & Expectations
(Age, sex, track, and education)
Information Channels
(School location, distance, size of class &
teacher)
Effective Norms
(PTSAMSi)
SES, size of family & educational expenditure
Academic Achievement
(GPA in final/public examination)
Figure 2. Conceptual framework
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findings also indicated that non-Emirati outfoxed the Emirati students in quantitative courses as did the students
with high GPA over the students with low GPA.
In a similar but more recent study, Yousef (2017) investigated the QP of business students in quantitative courses
with relation to teaching style, English language and assessment methods. By using Likert-scale, the author assessed
the teaching style, English language and assessment methods, and the descriptive results suggest that the Business
students often struggle with technical terminologies, class discussion, inefficiency in English language as well as tasks
assigned in quantitative courses. The regression analysis, however, show that the teaching style and English language
were positively influencing the QP of Business students, whereas assessment methods had negative but not
significant relation.
El Massah and Fadly (2017) examined the relation between academic performance of women in UAE and their
personal attributes, socioeconomic background, and other relevant variables. Among personal and socioeconomic
aspects, as the results show, individual’s marital status, high school grade and mother’s education had positive and
significant impact on academic performance, while age, father’s education and number of siblings exhibit no
significant effect. Among other issues considered in the study, the findings suggest that only class time – especially
at afternoon class – had positive effect on academic achievement of women in higher education at UAE, and the
rests, including language, commute time, engagement in sport, had no significant impact at all.
The literature review reveals a contentious situation as a wide range of factors has been identified by researchers,
whether in Bangladesh or in other places of the world, which are determining the academic achievement of students
at all levels. Mohsin et al. (1996), for example, emphasized on personal attributes as well as socioeconomic status,
others stressed on personal and institutional characteristics only (Alam, 1992; Asadullah, 2005; Salfi & Saeed, 2007;
Yousef, 2017), some paid attention to all three factors, i.e. personal profile, socioeconomic status and institutional
features (El Massah & Fadly, 2017; Nath, 2012; Ready, 2010; Richards & Islam, 2018; Saeed et al., 2005; Tayyaba,
2010; Uddin, 2017; Yousef, 2013) and there are some focused on relevant social, educational, and psychological
issues (Benner et al., 2016; Deb et al., 2014; Schultz, 1993). It is, however, can be assumed from the reviewed
literature that younger students may perform better than the older ones, while male students may outperform
female students. It could be assumed that students from well-off families may outrun their equivalents from less-
privilege families. It can also be expected that student having parental and teachers’ support with more educational
motivation and less academic stress may perform better than those with low parental and teachers’ support and
low academic motivation with greater stress.
Objectives and research questions
This study was designed to identify the extent to which the academic achievement of secondary students can be
explained by their personal and family characteristics, institutional attributes as well as by the support of parents
and teachers and by their own academic motivation and stress. The following research questions were expected to
be answered by this study –
1. To what extent does the personal profile of secondary students explain their academic achievement?
2. What is the magnitude of influence of family background in explaining academic achievement of secondary
students?
3. Does institutional attribute explain the variations in academic achievement of secondary students?
4. How does parent and teachers’ support as well as academic motivation and stress explain the academic
achievement of secondary students?
5. What are the major factors explaining academic achievement of secondary students in Bangladesh?
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METHOD AND PROCEDURE
Study area and sampling
Magura district is a southwestern district under Khulna Division of Bangladesh. Accommodating around 0.9 million
people in an area of 1,039 Km2, Magura is the fifth most densely populated district under Khulna division
(Bangladesh Bureau of Statistics, 2015a, 2015c, 2015d). Out of the total population of Magura district, according to
Bangladesh Bureau of Statistics (2015c), only 13.1 percent lived in urban areas, however, the literacy rate was almost
20 percent higher in urban areas (65.2%) than those of rural areas (48.4%).
Administratively, Magura district is divided into four Upazilas (sub-districts), namely Magura Sadar, Mohammadpur,
Shalikha and Sreepur (Bangladesh Bureau of Statistics, 2015a, 2015c), of which Magura Sadar is the largest
(401.6Km2), most educated (52.4%) and more urbanized (25.9%) Upazila with relatively smaller household size (4.37)
compared to the rests (Bangladesh Bureau of Statistics, 2015a). Among the listed Upazilas, this study was carried
out in Magura Sadar and Shalikha Upazila, largely because of the convenience of the researchers. According to
Education Management Information System (2017) there were 275 educational institutions in Magura district, out
of which 167 were primary and secondary schools, 33 were colleges offering education up to master degree and
another 75 were sectarian educational institutions. In Magura Sadar and Shalikha Upazila, there were a total of 162
educational institutions, including primary, secondary and tertiary schools, colleges and Madrasah (Education
Management Information System, 2017). It is important to note that the secondary education in Bangladesh is
divided into three more categories – junior secondary (Class VI to Class VIII), secondary (Class IX to Class X) and
higher secondary (Class XI to Class XII) (Kono et al., 2018).
For this study, proportionate multistage random sampling was used to select both educational institutions as well
as the participants, and. At the initial stage, four educational institutions, including schools and colleges, were picked
based on their performance in national public examinations, i.e., junior secondary certificate (JSC), secondary school
certificate (SSC) and higher secondary certificate (HSC). In the following step, some specifications were made – e.g.,
the participants must be enrolled in Class IX, Class XI and Class XIII after successful completion of JSC, SSC and HSC
or equivalent examinations, respectively, without repetitions – to develop an inventory list of eligible students. It
must be noted that the secondary students were selected largely because they have perfect understandings about
the issues under scrutiny, i.e. personal, socioeconomic and institutional factors as well as the role of parents,
teachers, their own academic motivation and stresses they faced, either through personal judgment or experience
(Ayodele, Oladokun, & Gbadegesin, 2016), which in turn might help to actualize the social realities of academics in
Bangladesh. Based on the aforesaid criteria, an inventory list of eligible students was made, comprising 1,899
secondary, higher secondary and tertiary students, from registry books of the selected educational institutions.
Finally, a total of 566 students, 300 each from both urban and rural areas, were selected randomly, proportionate
to the number of students from each educational institution. Later, 34 cases were dropped for incompleteness of
the self-reported questionnaire (hereafter SRQ).
Instrument and procedures
To facilitate survey for this study, a structured SRQ in English, containing both open-ended and close-ended question
items, was administered to collect considerable amount of social data for more acceptable generalization. The SRQ,
developed after reviewing relevant literatures, was divided into four sections. The first section focused on the basic
information of the participants, and the items include questions on age, sex, religion, level and track of education,
and academic achievement. The participants were requested to report the grade point average (GPA) attained in
JSC, SSC and HSC examinations. The second section highlighted the items regarding parental as well as household
information, and it included questions on parental education, occupation and monthly income, the latter reported
in Bangladeshi Taka (BDT). Apart from parental issues, there were some items on type and composition of family
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and the financial issues – such as household monthly income and expenditure, and monthly educational expenditure
for the participants. The third section contained questions on the details of educational institutions they were
enrolled in Class VIII, Class X and Class XII, and they answered questions on approximate distance of educational
institution from home, type, location and size of educational institution, size of the participants’ class and the
number of students per teacher. The questions on size of educational institution and class as well as the number of
students per teacher were verified by the Head/Principal of the institutions. The fourth and final section contained
24 five-point Likert-type questions, ranging from ‘1’ – ‘strongly disagree’ to ‘5’ – ‘strongly agree’, measuring the
perception of secondary students about the role of parents, teachers, their academic motivation as well as academic
stress.
Before data collection, the researchers visited the selected educational institutions with an official permission from
the District Education Officer (DEO) of Magura District as well as the Head or Principal of each institution. To maintain
ethical aspects of research as well as to collect reliable data, the researchers verify the identity of the participants
by their class teachers, which in turn helped the researchers to ease the communication with the participants. The
researchers introduced themselves and gave a short briefing about the purpose of the study. In addition to the
researchers, there were six data enumerators, and they were trained extensively about the content of the
questionnaire to maintain uniformity of the survey as well as the anonymity of the participants. After the briefing,
the students were reorganized in a manner to avoid duplication of the answer to maintain the integrity of the data
as well as to extract authentic information. It is important to note that a guarantee of anonymity and confidentiality
was assured by the researchers to both the educational institutions and the participants. The SRQs were collected
after fifteen to twenty minutes of distribution from the participants. It is, however, noteworthy that the SRQ was
pre-tested to eliminate the possibilities of inconsistent and invalid data on 30 participants, 15 each from Magura
and Shalikha Upazilas, who were later dropped from the actual fieldwork. From the feedback of the pre-test, some
modifications were made in the content, style, and language (the SRQ was translated to Bengali from English) to
ease the data collection from the selected educational institutions. Data were collected during January to March
2017, and it was administered in the convenient time – usually during the launch break – of each educational
institution without interrupting their regular academic activities.
MEASURES
Personal profile
Personal profile generally refers to the specific characteristics of an individual. The specifications, however, vary
considering the research issues (Akareem & Hossain, 2016; Tayyaba, 2010; Yousef, 2017). In this study, personal
profile was assessed by the participant’s age (measured in year), sex, religion, education (measured in year), track
of education, where the latter one was based on the grouping of science, business studies and humanities. The
descriptive statistics, presented in Table 1, show that more than half of the participants were male, and around 81
percent were Muslims. Averaging around 16 years of age, the participants had about 10 years of schooling, and a
significant percentage of them studied in humanities other than science and business studies.
Socioeconomic status, family size and educational expenditure
Socioeconomic status is, in general, measured by an individual’s social and economic position with reference to their
financial capacity, educational background and professional record. Hollingshead (1975), for example, advocated
four factors, i.e. education, occupation, sex and marital status, to determine the social position of individuals
occupying in social structure. Snyder, Dillow, and Hoffman (2009), on the contrary, suggest considering a composite
of five components, including father’s education, occupation, mother’s education, family income and other
household items, to explain the socioeconomic status of an individual in educational research. In the present study,
however, socioeconomic status is constructed as a single variable of parental education [where ‘0’ was assigned for
‘no education’, ‘1’ for primary education (Class I to V), ‘2’ for secondary education (Class VI to X), ‘3’ for higher
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secondary (Class XI to XII) and ‘4’ for tertiary education (Class 13 ≤)], occupation of father (‘0’ for irregular job &
salary, and ‘1’ for regular job & salary) and income of father (‘1’ for income equal or less than BDT 20,000, ‘2’ for
BDT 20,001 to BDT 40,000 and ‘3’ for equal or more than BDT 40,001). It is evident that fathers were relatively more
educated than mothers (±9.8 years against ±8.4 years), and like many patriarch societies, fathers were the
breadwinner with an average monthly income of BDT ±19,604. The aggregated score, used for socioeconomic index
(SESi), was ranging from 1 (Low) to 12 (High) with a mean of ± 6.4. The size of family, measured by per by household,
was also considered in this study with a mean of ± 4.9 person per household. Furthermore, the monthly educational
expenditure, generally spent for after-school supplementary education, was used to understand the pattern of
household contribution for educational purpose, and the studied families, on an average, spent around BDT 3,200
per month for the participants.
Attributes of educational institution
The attributes of educational institution refer to the salient features of the schools/colleges/universities etc. The
characterization of educational institution, like personal profiling, also subject to the research interests as some may
include the class and school facility related items (Asadullah, 2005; Nath, 2012; Tayyaba, 2010), while others may be
interested to include the details of class, school and faculty altogether (McCoach & Colbert, 2010; Salfi & Saeed,
2007). In this study, the attributes of educational institution were measured by distance from home, types, location,
class size and student per teacher. Table 1 suggest that the participants were largely enrolled in private or semi-
government schools, and they had large class size as the teachers had to deal with around 80 students per class.
Parent-teacher support and academic motivation-stress inventory
In addition to providing the basic information about personal attributes, socioeconomic status and characteristics of
educational institution, the participants were asked about their perception regarding the role of their parents and
teachers along with how they evaluate their academic motivation and stress in relation to their academic
achievement. They responded to an inventory – parent-teacher support and academic motivation-stress (PTSAMSi)
– of twenty-four questions adjusted to a five-point Likert-scale – ranging from ‘1’ strongly disagree to ‘5’ strongly
agree – divided equally into four indices. The overall alpha reliability coefficient of the inventory was α = 0.821. After
reviewing relevant literature (Eccles & Harold, 1996; Keith, Reimers, Fehrmann, Pottebaum, & Aubey, 1986; Kohl,
Lengua, & McMahon, 2000; Nguon, 2012; Park, Byun, & Kim, 2010; Régner, Loose, & Dumas, 2009; Zhang, 2011), a
total of six questions were formed to assess the role of parents index and it was determined by asking the
participants about the involvement and activities of parents to support and maintain the academic performance and
this dimension has the alpha[
1
] reliability coefficient of 0.822. Likewise, the role of teachers index also assessed by
six relevant questions developed following the review of literature (Johnson, Johnson, & Anderson, 1983; McCoach
& Colbert, 2010; Régner et al., 2009; Tse, 2014; Zhang, 2011), and these questions measured the extent of care given
by the teachers at school or at private tuition to facilitate children’s academic attainment, and this dimension has
the alpha reliability coefficient of 0.872. Following the aforesaid indices, the third index – the academic motivation
– was developed by using the past research works (Caleon et al., 2015; Lang & Fries, 2006; Vallerand et al., 1992),
and this index measured the commitment and enthusiasm for academic excellence by the participants, and it has
the alpha reliability coefficient of 0.727. The fourth and final index – Academic stress – developed after a careful
review of relevant literature (Fioravanti-Bastos, Cheniaux, & Landeira-Fernandez, 2011; Renner & Mackin, 1998;
Spangler, Pekrun, Kramer, & Hofmann, 2002) and through this index the anxiety of the participants about academic
activities exerted by expectations and social strains was measured, and it has the alpha reliability coefficient of 0.720.
The exemplary items are – “My parents make a congenial environment at our home where I can study” (item 1),
[
1
] The Cronbach’s α was calculated for parent-teacher support and academic motivation-stress inventory (PTSAMSi)
to provide indications of the reliability and internal consistency of results (α – 0.821). The highest achievable score
is 1, thus, an alpha score of 0.7 is considered normal, and anything below 0.6 is regarded as non-usable (DeVellis,
2003)
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“My teachers review my academic performances and report to my parents regularly” (item 12), “I cannot give up
when I study, because I enjoy studying my lessons” (item 14), and “I feel anxious about the examinations, even if I
work harder” (item 21). Among the dimensions of PTSAMSi with a Cronbach’s α 0.824, presented in Table 1, it
appears that the secondary students acknowledged the role of their parents and teachers in shaping the academic
performance. They agreed upon the significance of academic motivation while skeptic about academic anxiety.
Academic achievement
Academic achievement has been measured by the researchers from different perspectives globally. Some studies
measured academic achievement by a collection of assessment methods, including teacher ratings or marks, school
grades and test scores (Baumann & Harvey, 2018; Chowa, Masa, Ramos, & Ansong, 2015; Tayyaba, 2012; Westerman
& La Luz, 1995), while there are some others who assessed the academic achievement not only by grades or marks,
rather by regular school attendance and concertation on learning by responding in answer-question session in the
class (Bandura, Barbaranelli, Caprara, & Pastorelli, 1996; Uddin, 2017). In this study, however, the academic
achievement of students was measured, as El Massah and Fadly (2017), Nguyen (2016), Salfi and Saeed (2007) did
in their respective studies, by the grade point average (GPA) achieved in preceding national public examinations,
including JSC, SSC and HSC. The GPA, in contemporary education system of Bangladesh at school and at college
levels, ranges from the lowest letter grade ‘F’ (0-32 marks with grade point 0.00) to the highest letter grade ‘A+’ (80-
100 marks with grade point 5.00). The GPA of the participants ranged from 5.00 to 1.80 with an average of ±4.1.
Table 1
Descriptive information
Variables
% (N)
M (SD)
Max - Min
Possible range
(Max-Min)
Personal attributes
Age
15.8 (1.9)
20 – 13
Sex
Male
53.7 (304)
Female
46.3 (262)
Religion
Sanatan
18.7 (106)
Islam
81.3 (460)
Education
10.3 (1.5)
13 – 9
Track of education
Humanities
52.5 (297)
Science & business studies
47.5 (269)
GPA in public examination
4.1 (0.7)
5.00 – 1.80
Socioeconomic status
Education of father
9.8 (5.4)
17 – 0
Occupation of father
Irregular job & salary
39.2 (222)
Regular job & salary
60.8 (344)
Income of father (in BDT1)
19,634.8 (12,134.7)
78,000 – 2,000
Education of mother
8.5 (5.3)
17 – 0
SES
6.4 (3.1)
12 – 0
12 – 1
Size of family
4.9 (1.5)
13 – 2
Educational expenditure (in BDT)
3,195.7 (3,056.3)
12,000 – 100
Attributes of educational institutions
Location
Rural
49.8 (282)
Urban
50.2 (284)
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Note: M. Mean; SD. Standard deviation; Max. Maximum; Min. Minimum; BDT. Bangladeshi Taka;
GPA. Grade point average; Km. Kilometer
1. 1 BDT = US$ 0.012
Data analysis
Data were analyzed in five consecutive stages. At the very first stage, several key characteristics about the
participants, including age, sex, religion, education, track, parental and school background information, were
illustrated by descriptive statistics using percentage, mean, standard deviation and range. At the second stage,
exploratory factor analysis (EFA) was applied to determine major dimensions to formulate the PTSAMSi. At the third
stage, the relation between academic achievement and other independent variables (personal attributes,
socioeconomic status, institutional characteristics and PTSAMSi) was explained quantitatively by applying Pearson’s
bi-variate correlation to sort out the variables for regression analysis. The GPA, attained by the participants, was a
continuous variable, therefore, the decision of executing stepwise multiple regression was made. A total of four
models were built following a stepwise backward deletion approach as it considers all independent variables at the
beginning, then start to delete one at a time if not contribute significantly in the regression equation (Tabachnik &
Fidell, 2013). Finally, a hierarchical regression was executed as it weighs the values added by independent variables,
entered in blocks, after controlling other predictors at its own point of entry (Pallant, 2011; Tabachnik & Fidell, 2013).
RESULTS
Exploratory factor analysis
To identify the important components influencing academic achievement, a total of 24 items were used for EFA. To
assess the suitability of the data for EFA, a preliminary analysis was done through principal component analysis (PCA)
and the sample adequacy was assessed by Kaiser-Meyer-Olkin (KMO) value and Bartlett’s test of sphericity. The KMO
value was 0.896, signifying the sampling adequacy as the benchmark was 0.600 (Pallant, 2011; Tabachnik & Fidell,
2013) and the Bartlett’s test of sphericity was significant as well (χ2 (276) = 5020.380, p < 0.000). The decision of
determining the number of components for EFA was guided, as suggested in the works Pallant and Bailey (2005) and
Pallant (2011), by three decision rules, namely the Kaiser’s criterion (Kaiser, 1960), Cattell’s scree plot test (Cattell,
1966) and Horn’s parallel analysis (Horn, 1965). The eigenvalues suggested a five-factor solution, whereas the scree
plot suggested a four-factor solution. The suitability of a four or five-factor solution was further assessed by
comparing the eigenvalues from the extracted eigenvalues generated from the same size of random data set, and it
also suggested a four-factor solution as the first four factors with the eigenvalues exceed the values from randomized
data (Horn, 1965; Watkins, 2000). However, the pattern coefficient of ≥ 0.40, and an internal consistency of ≥ 0.70
(DeVellis, 2003) were considered for a meaningful and consistent factor structure. The items were sorted and
grouped by size of loading, and four items were deleted as their loadings were under 0.40, while another one was
deleted for high cross-loading (Hair Jr., Black, Babin, & Anderson, 2014). Later, another item was deleted from the
extraction as three of them had low commonalities (≤ 0.30).
Table 2 represents the loading patterns in the rotated factor component matrix. The first principal component,
explaining about 15%t of the total variation, entails the factors associated with teachers’ role, and it includes the
ways teachers instruct to ensure quality education, extend their helping hands to comply with the demands of
educational institutions as well as examinations, motivate their pupils for higher academic goals, their hardship to
Distance from home (in Km)
4.1 (6.8)
50 – 0
Type of educational institutions
Public
41.5 (235)
Private/semi-government
58.5 (331)
Size of class
209.2 (110.5)
350 – 50
Students per teacher
77.9 (90.5)
240 – 18
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educate and train their followers, their depth of knowledge about the subjects they taught and their meaningful
feedback to ensure better performance of the students, academically and socially. The second principal component,
clarifying just over 13% of the total variation, highlights the roles of parents to ensure better performance in
academia, including parents’ advice about the methods of studying and ways of maximizing academic grades, their
monitoring and suggestion to boast academic activities by getting help from supplementary education, their
communication with teachers to get feedback about academic performance of their children in educational
institutions. Explaining 10% of the total variations, the third component depicts the academic pressure and anxiety
of the secondary students, including their anxiousness and fear of failing in examination and their feelings expressed
through both emotionally and physicality. The last component, explaining just around 10% of the variations, refers
to the academic motivation of the secondary students, and this component portrays their self-confidence of
successfully completing the assigned academic works, including homework and examinations, their enjoyment and
involvement in academic activities and their potentiality to comply with difficult issues given by teachers as well as
the recommended readings.
Based on the results of the EFA, the scores of the four-factors were measured by unit-weighted items together. The
Cronbach’s α coefficients for the four-factors were ‘teachers’ support’ – 0.822, ‘parents’ support’ = 0.872, ‘academic
stress’ = 0.737 and ‘academic motivation’ = 0.725, respectively, and the overall coefficient was 0.786 (see Table 2).
Table 2
Results of exploratory factory analysis (N = 566)
Items
Components
h2
Teachers’
support
Parents’
support
Academic
stress
Academic
motivation
9
0.707
0.586
11
0.662
0.502
10
0.618
0.463
8
0.616
0.522
7
0.550
0.383
12
0.471
0.356
4
0.786
0.765
3
0.749
0.612
5
0.674
0.651
2
0.620
0.598
23
0.732
0.558
24
0.687
0.489
20
0.552
0.311
21
0.550
0.325
13
0.676
0.512
14
0.590
0.405
17
0.546
0.400
18
0.519
0.317
% of variance
15.072
13.415
10.156
9.997
Cronbach’s α
0.822
0.872
0.737
0.725
Note: h2. Communalities
Bivariate correlation
To investigate the relationship between academic achievement and independent variables – personal information,
SES, institutional characteristics, and the dimensions of PTSAMSi – Pearson’s bi-variate correlation was executed
(see Table 3). Among the personal attributes, age, sex, track, and education were significantly correlated with
academic achievement, where age (r = -0.339, p < 0.01) and education (r = -0.356, p < 0.01) had negative correlation.
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On the contrary, science and business studies as a track of education had moderate but positive relation (r = 0.362,
p < 0.01) with academic achievement. Among other background information, SES (r = 0.509, p < 0.01) and
educational expenditure (r = 0.451, p < 0.01) were positively correlated with academic achievement, however, size
of family had negative relation (r = -0.177, p < 0.01).
There was a mixed relationship found between academic achievement and institutional attributes, where location
of institution had moderate but positive correlation (r = 0.429, p < 0.01) with academic achievement and the rests,
except students per teacher, had negative but significant correlation with academic performance of students. It
suggests that students covering long distance, enrolled in private or semi-government school and in overcrowded
class had lower GPA compared to those covering short distance, enrolled in public schools and in optimum class size.
Among the dimensions of the PTSAMSi, parents’ as well as teachers’ support and academic stress were moderately
correlated with academic achievement, however, only the latter had negative (r = -0.313, p < 0.01) relation.
Academic motivation (r = 0.289, p < 0.01) had a low but positive correlation.
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Table 3
Bivariate correlation between academic achievement and all independent variables
Variables
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
1
GPA
1.000
2
Age
-0.339**
1.000
3
Sex
0.108*
-0.141**
1.000
4
Religion
-0.004
-0.097*
0.019
1.000
5
Education
-0.356**
0.912**
-0.103*
-0.127**
1.000
6
Track of education
0.362**
-0.072
-0.011
-0.015
-0.005
1.000
7
SES
0.509**
-0.414**
0.087*
-0.012
-0.375**
0.328**
1.000
8
Type of Family
0.051
-0.088*
0.029
0.009
-0.062
0.059
0.123**
1.000
9
Size of Family
-0.177**
0.168**
0.006
0.056
0.133**
-0.088*
-0.239**
-0.631**
1.000
10
Educational Expenditure
0.451**
-0.215**
0.001
-0.026
-0.188**
0.417**
0.719**
0.074
-0.209**
1.000
11
Location
0.429**
-0.049
-0.010
-0.016
0.006
0.488**
0.549**
0.033
-0.129**
0.676**
1.000
12
Distance from home
-0.211**
0.465**
-0.052
-0.109**
0.513**
0.020
-0.276**
-0.044
0.127**
-0.054
0.060
1.000
13
Type of Institution
-0.283**
-0.091*
0.020
0.000
-0.159**
-0.383**
-0.297**
-0.008
0.041
-0.374**
-0.840**
-0.151**
1.000
14
Size of class
-0.164**
0.355**
-0.272**
-0.154**
0.350**
-0.040
-0.096*
0.021
0.070
-0.001
0.064
0.194**
-0.302**
1.000
15
Students per teacher
-0.014
0.499**
-0.068
-0.012
0.523**
0.250**
-0.152**
-0.041
0.081
0.036
0.503**
0.313**
-0.628**
0.205**
1.000
16
Teachers’ support
0.309**
-0.121**
0.065
-0.075
-0.095*
0.307**
0.473**
0.080
-0.161**
0.528**
0.461**
0.051
-0.277**
0.165**
-0.055
1.000
17
Parents’ support
0.350**
-0.030
0.029
-0.051
0.001
0.309**
0.568**
0.130**
-0.198**
0.571**
0.582**
0.106*
-0.421**
0.198**
0.139**
0.579**
1.000
18
Academic stress
-0.313**
0.204**
0.014
0.041
0.169**
-0.203**
-0.396**
-0.025
0.128**
-0.411**
-0.401**
0.097*
0.281**
0.092*
-0.019
-0.264**
-0.275**
1.000
19
Academic motivation
0.289**
-0.042
0.021
0.006
-0.048
0.147**
0.292**
0.080
-0.153**
0.302**
0.268**
0.057
-0.147**
0.110**
-0.053
0.472**
0.456**
-0.097*
1.000
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Regressions
Significant factors from bivariate correlation (r) were retained in the ordinary least square regression analysis to
investigate how these factors were influencing the academic achievement of secondary students in Bangladesh.
Table 4 presents the four models explaining academic achievement with relation to personal attributes, SES,
institutional characteristics and PTSAMSi. Model 1 shows the personal profile related predictors of academic
achievement. Among the four variables considered, findings suggest that sex, track, and education significantly
influenced the academic achievement of secondary students. Female students performed better than male students
and students of science and business studies outperformed their counterparts from humanities track, whereas
education was negatively associated with academic performance, suggesting young students performed better in
public examinations than their older counterparts. However, all three variables explained 26% of the variations in
academic achievement.
Model 2 represents the predictors relevant to background information, including SES, size of family and educational
expenditure. Of the three variables, the model took two, while size of family had no contribution. SES and
educational expenditure together explained just over 27% variations in academic achievement of secondary
students. Among the aforesaid variables, SES made the most contribution, suggesting students from better
socioeconomic background performed better than students from poor socioeconomic households. Likewise,
students from high spending families outdone academically students from low spending families.
Model 3 embodies institutional predictors, and the model took three out of five variables. Among the significant
predictors, location of institutions made the highest contribution in the model. Except for location of institution,
which suggests a positive relation between geographical position with academic achievement, in this case the urban
areas, the rests indicate negative relationships. The independent variables together explained just over 26% of the
variations in academic performance.
Model 4 shows three out of four dimensions of PTSAMSi as predictors of academic achievement of secondary
students. Academic stress and parents’ support made the highest contribution in explaining the variations followed
by teachers’ support and academic motivation. Academic stress was negatively associated with the academic
performance, whereas parents’ and academic motivation had positive and significant effects on students’ academic
excellence. The dimensions of PTSAMSi explained about 20% variations of academic achievement.
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Table 4
Stepwise-backward multiple regression predicting academic achievement
Note: β. Standardized coefficient; SE. Standard error
***. Significant at 0.001% level
Table 5 presents the results of hierarchical regression predicting academic achievement of secondary students in
four models. Step 1, where control variables were added, was significant, F (3, 562) = 66.736, p < 0.001, R2 = 0.263,
suggesting that this model collectively explained 26% of the variance in the academic achievement. Adding SES and
educational expenditure in Step 2 increased R2 by 9.4% with the overall model remaining significant, F (2, 560) =
41.121, p < 0.001, R2 = 0.357 (an increase from 0.26 in Step 1), suggesting SES and educational expenditure played a
decisive role in explaining the academic achievement of secondary students. Adding the attributes of educational
institutions (location, distance from home and size of class) in Step 3 increased R2 by 2.3% with overall model
remaining significant, F (3, 557) = 6.736, p < 0.001, R2 = 0.380 (an increase from 0.357 in Step 2), indicating attributes
of educational institutions yielded key role in explaining academic success of secondary students. Finally, adding the
dimensions of PTSAMSi (parents’ support, academic stress as well as motivation) in Step 4 yielded R2 by 2.4% with
overall model remaining significant, F (3, 554) = 7.445, p < 0.001, R2 = 0.404 (an increase from 0.380 in Step 3),
signifying that the PTSAMS inventory significantly influenced the academic performance of secondary students.
Variables
β (SE)
p value
Model 1: Personal attributes
Track of education
0.361 (0.049)
< 0.001
Education
-0.347 (0.017)
< 0.001
Sex
0.076 (0.050)
< 0.05
R2
0.263
F
66.736***
Model 2: Socioeconomic status, size of family and educational expenditure
SES
0.382 (0.011)
< 0.001
Educational expenditure
0.176 (0.000)
< 0.01
R2
0.274
F
106.278***
Model 3: Attributes of educational institution
Location
0.452 (0.050)
< 0.001
Distance from home
-0.209 (0.004)
< 0.001
Size of class
-0.152 (0.000)
< 0.001
R2
0.263
F
66.887***
Model 4: Parent-teacher support and academic motivation-stress (PTSAMS) inventory
Parents’ support
0.206 (0.007)
< 0.001
Academic stress
-0.240 (0.008)
< 0.001
Academic motivation
0.172 (0.011)
< 0.001
R2
0.197
F
45.826***
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Table 5
Hierarchical multiple regression predicting academic achievement
Variables
Model 1
Model 2
Model 3
Model 4
β (SE)
β (SE)
β (SE)
β (SE)
R2 (∆ R2)
0.263
0.357 (0.094)
0.380 (0.023)
0.404 (0.024)
∆ F
66.736***
41.121***
6.736***
7.445***
Step 1 (Control variables)
Track of education
0.361*** (0.049)
0.224*** (0.051)
0.172*** (0.053)
0.173*** (0.052)
Education
-0.347*** (0.017)
-0.231*** (0.017)
-0.232*** (0.20)
-0.221*** (0.020)
Sex
0.076* (0.050)
0.065⸸ (0.047)
0.056 (0.048)
0.052 (0.047)
Step 2 (Socioeconomic status and educational expenditure)
SES
0.234*** (0.011)
0.189*** (0.012)
0.137* (0.012)
Educational expenditure
0.140** (0.000)
0.053 (0.000)
0.023 (0.000)
Step 3 (Attributes of educational institution)
Location
0.213*** (0.067)
0.176** (0.069)
Distance from home
-0.041 (0.004)
-0.062 (0.004)
Size of class
-0.048 (0.000)
-0.068⸸ (0.000)
Step 4 (PTSAMSi)
Parents’ support
0.036 (0.008)
Academic stress
-0.071⸸ (0.007)
Academic motivation
0.145*** (0.009)
Note: β. Standardized coefficients; SE. Standard error
***. p < 0.01; **. p < 0.01; * p < 0.05; ⸸. p < 0.10
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DISCUSSION
This study was aimed at identifying the factors influencing the academic achievement of secondary students in
Bangladesh. In response to the first research question, it appeared that two variables of personal profile, out of five,
were significantly associated with academic achievement of secondary students. Findings indicated that track of
education has positive relation with academic achievement. In Bangladesh, despite the dearth of qualified teachers
and well-equipped laboratories the outstanding students are in general enrolled in science, followed by business
studies, while humanities courses are preferred for the students who struggle academically (Ashraf, 2008). In
contrast, education was negatively related with academic achievement, meaning younger students performed
better in public examination than their older counterparts, and this result contradicts with studies such as Mohsin
et al. (1996), however, confirms the findings of Tayyaba (2010). In Bangladesh, younger students are strictly
supervised and monitored by both parents and teachers, at home or at school, and they have least options to be
deviated from the academic activities. On the contrary, older students often get involved in non-academic and co-
curricular activities, including student politics, voluntary works etc., and they are not over-watched by parents or
other guardians. Moreover, there are other means, such as internet addiction, that distract their attention from
academic activities (Golder, Jabbar, Alam, Hossain, & Chandra, 2017; Mamun & Griffiths, 2019).
About the second research question, it is evident from the results – of correlation and regression – that the
performance of secondary students in public examinations was positively influenced by SES. Studies both in
developed and developing countries often report a positive association between SES with children’s academic
achievement (Ataç, 2017; McConney & Perry, 2010), and Bangladesh is not an exception (Uddin, 2017). Generally,
children from families ‘more capable’ to spend money for educational purposes, for ‘shadow education’ or after-
class education (Nath, Chowdhury, Ahmed, & Choudhury, 2014), and sometimes for ‘question paper leakage’ (Dhaka
Tribune, 2018; Mahtab, 2017) herein Bangladesh, often achieve unprecedented educational feat.
Apart from personal and household characteristics, it is also evident that institutional features are an important
catalyst of academic achievement of secondary students. Location of institution appeared to be positively associated
with academic performance, and such result confirms previous studies (Ansong, Ansong, Ampomah, & Adjabeng,
2015; Vidyattama, Li, & Miranti, 2019). In Bangladesh, location of educational institution is a critical issue, because
in rural or remote areas in general, there are still shortages of quality secondary educational institution, and this
scenario worsen further if the students seek quality science or business teachers. Students, therefore, compelled to
commute longer distance, which in turn consume time as well as energy, and subsequently the students could not
reflect their efforts in academic transcripts. Besides, class size has negative effects on academic achievement.
Studies, whether in Bangladesh or elsewhere, suggest that students from smaller class size often perform better
than those of larger class size (Asadullah, 2005; Nath, 2012; Tayyaba, 2010). Because smaller class size with fewer
students per teacher allow the educator to pay attention to individual demands and requirements of each student.
On the contrary, greater number of students per class make it difficult for teachers to know the problems each
student is dealing with, subsequently the teachers and students both could not fulfill their educational prerequisites.
For the fourth research question, it is apparent that all only two dimensions of PTSAMSi were significantly associated
with academic achievement of secondary students. Findings indicate that individual’s academic motivation and
stress were significantly influencing their performance in secondary public examinations, however, the latter has
negative influence. Previous studies suggest that students with greater academic motivation produce higher levels
of academic achievement than those with lower academic motivation (Baumann & Harvey, 2018; Guay & Vallerand,
1996). In contrast to the positive effects of motivation on academic achievement, past studies endorse that academic
anxiety and stress, in various forms, had negative effects on the overall performance of students, whether at schools
or at universities (Deb et al., 2014; Jan, Anwar, & Warraich, 2016; Schultz, 1993; Spangler et al., 2002).
When put together all the explanatory variables, the results suggest a wide range of variables explaining the
academic performance of secondary school students in public examination, including the personal (education and
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track), SES, location of institution as well as academic motivation and stress. However, personal characteristics were
the most decisive ones in explaining academic disparity followed by socioeconomic and educational predictors, and
such findings corroborate previous studies (Ataç, 2017; Nath, 2012; Tayyaba, 2012).
This study, however, has certain limitations. For example, the study was carried out in only eight educational
institutions of two Upazilas of Magura district, one of many districts of Bangladesh, thus, the results should be
generalized with caution. The number of participants was also relatively small considering the bulge of secondary
students of Bangladesh. Moreover, data were collected from students, aged between 13 to 20 years, who
successfully completed the secondary examinations, i.e., JSC, SSC and HSC, exempting those who failed and those
of others in various classes/grades of secondary levels. The response of the participants also may have been biased
considering their age and perception of ‘right answers’ for the questions, specifically, the Likert-scale items. The
academic achievement, in this study, was measured only by the GPA, and did not consider other procedures, such
as class attendance, proficiency test etc., which may have some internal inconsistencies. Therefore, reader’s
discretion is strongly suggested, and not to generalize these findings in the context of Bangladesh as a whole. Despite
these short falls, the study is expected to contribute substantially to the limited literature of academic achievement
of secondary students in the context of Bangladesh as it marked by some intriguing findings.
RECOMMENDATIONS AND CONCLUSION
The study, aimed at addressing the determinants of academic achievement of secondary study in Bangladesh, found
a range of factors influencing the academic achievement, including the personal (track and education), SES,
institutional (location and size of class) as well as psycho-social issues (academic motivation and stress). Bangladesh,
like many developing countries, is thriving for both human and knowledge development through education.
However, to achieve the sustainable development goals by 2030, Bangladesh needs to improvise its education
strategies and policies for ensuring equitable and quality education for all.
1. Bangladesh needs to devise a unified monitoring system, engaging students, parents, teachers, and educational
administrators, to overwatch the educational development of individuals and advise possible remedies to
improve academic learning and performance.
2. The educational institutions need to develop socioeconomic profiling of the students to understand the trends
and patterns of academic learning and performance of students regarding their socioeconomic status, and
suggest necessary steps involving students, parents, and teachers for the betterment of the educands as well as
the educational institutions.
3. Government should address the requirements of the stakeholders – students, parents and teachers – at the
grassroots and increase budget allocation for improving transportation and communication system at remote
and rural areas, establish government schools and colleges to ensure optimum utilization of limited resources
through increased competition among schools and avoid monopolization of education by appointing qualified
and trained teachers and technicians, especially, for science courses and laboratories, considering the class and
school size.
4. Government should organize compulsory pedagogical trainings and seminars for teachers with an emphasis on
ethical aspects of education, and it must institutionalize the private tuition or shadow education with certain
conditions. The latter should be taxable; therefore, it will increase government income as well as minimize
pressure on household overall expenditure for education, and make sure of better performance of students in
public examination without the risk of ‘question leakage.’
5. Educational institutions in a collaboration with government health services should monitor the nature and extent
of academic anxiety and pressure among students, and by appointing educational psychologists, they should
advise solutions for academic and examination related anxiety and pressure and aspire students for better
performance and achievement, both academically and socially.
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Besides, more empirical research should be directed by both government and non-government organizations based
on nationally representative sample to find the potential causes of academic achievement inequalities in
broaderspectrum to offer insightful ideas into educational outcomes to redesign and implement equitable and all-
inclusive education for all.
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