School environmental factors, pupils’ characteristics, and academic performance: The case of junior high school pupils of the Krachi west district of Ghana

Abstract

Background

Data suggest that Ghana has made significant improvements in the educational system, resulting in some accomplishments. Nonetheless, pupils’ academic performance at the junior high school level and in the Basic Education Certificate Examination (BECE) remains poor nationally. Some factors have been identified to influence poor academic performance in some parts of Ghana, but none in the Krachi West district. Thus, it has become imperative to determine the local factors accountable for this trend and identify the most appropriate localised solutions.

Materials and methods

An embedded research design was employed to ascertain the influence of pupils’ characteristics and school environment factors on academic performance. In all, 361 participants (325 BECE candidates, 12 teachers, 12 head teachers and 12 PTA/SMC Chairpersons) were selected using cluster sampling, simple random and purposive sampling across 12 schools in the district. The academic performance of pupils was assessed using scores from six (6) subjects in the standardised district-level mock examination.

Results

Based on the examination scores, half of the pupils performed poorly in the six subjects. Even though from the quantitative study, pupils’ characteristics had no significant influence on academic performance, school location (β = −3.29, p < 0.01), school type (β = 1.15, p < 0.01), and school environmental factors (β = 0.807, p = 0.024) were significant predictors of academic performance in the district. Pupils in private schools were thrice more likely to achieve average academic performance than pupils from public schools (OR = 3.2, CI = 1.06–9.47). Also, schools with good environmental factors were twice as likely to have average academic performance than schools with poor environmental factors (OR = 2.2 CI = 1.11–4.52).

Conclusion

While school environment factors and pupil characteristics have a relationship with academic performance as suggested by the ecological theory, it was established in this study that only school location, school type and school environment factors were the significant predictors of academic performance in the Krachi West District. Therefore, education stakeholders need to consider these predictors when coming up with integrated but local strategies to improve pupils’ academic performance in that district.

1. Introduction

Inarguably, education is a powerful driver of development in any society and pivotal in improving the quality of life. Therefore, the Sustainable Development Goal (SDG) Four (4) seeks to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all [[1], [2], [3]]. With this urgent call to ensure quality education, various countries across the globe, including Ghana, are working assiduously to achieve this. In Ghana, the government has, over the years, rolled out practical measures in the educational sector to support policies and global campaigns like the Free Compulsory Universal Basic Education (FCUBE), the Millennium Development Goal (MDG) two (2), and the SDG 4 [4]. Over the years, practical and strategic measures put in place to achieve these goals and policies include the provision of teaching and learning materials, free uniforms for pupils, the introduction of the capitation grant, and the school feeding programme [[4], [5], [6]]. Despite these, issues of academic performance remain a great challenge.

In Ghana, every pupil who completes basic education is required to take the Basic Education Certificate Examination (BECE), where pupils’ performance is evaluated based on a mandatory, external examination, which is 70 % of the total marks and a school-based assessment (internal examination), which is 30 % [7]. For over a decade now, information from stakeholders in education and sections of the Ghanaian media has been indicating poor academic performance of pupils at the basic level and BECE candidates nationwide [5,7,8]. According to Alhassan [9], over 30 % of pupils who take BECE yearly cannot progress to the next educational level because of poor academic performance.

Although some studies have been conducted to assess the academic performance of pupils in some parts of Ghana [5,[7], [8], [9]], none have been conducted in the Krachi West district of the newly formed Oti region, as revealed by the extant literature. Like the national trend, data from the Krachi West District Education office shows the poor academic performance of most pupils who completed their basic education and could not progress to the next educational level. Several other works have identified several determinants, such as pupils' characteristics, school environmental factors, teacher-related factors and household characteristics [5,[7], [8], [9]] to influence pupils' academic performance in Ghana. In Krachi West, no study has been conducted to identify the factors contributing to pupils' poor academic performance, hence the need for this study. Determining local factors that impact pupils' academic performance has become necessary to develop the best and appropriate solutions for implementation by all relevant stakeholders. This research aimed to assess the relationship between pupils’ characteristics and school environment factors on the academic performance of junior high school pupils in the Krachi West District. The study was guided by the following research questions.

• 1.What school environment factors influence the academic performance of junior high school pupils in the Krachi West District?
• 2.Do pupil characteristics influence the academic performance of basic school pupils in the Krachi West District?

The research hypotheses of the study were.

• 1.H₀: There is no significant association between school environmental factors and pupils' academic performance.

H₁: There is a significant association between school environmental factors and pupils’ academic performance.

• 2.H₀: There is no significant association between pupils' characteristics and academic performance.

H₁: There is a significant association between pupils’ characteristics and academic performance.

• 3.H₀: Type of school, location of school, school environmental factors and pupil characteristics do not affect the academic performance of JHS pupils.

H₁: Type of school, location of school, school environmental factors and pupil characteristics affect the academic performance of JHS pupils.

2. Review of literature

2.1. Academic performance

As noted by Akrofi [8], one key objective of every educational institution is to produce pupils with good academic outcomes. Therefore, in most global educational systems, the academic performance of pupils is an essential element and a significant factor in determining the accomplishment or otherwise of any academic institution [10].

Academic performance, according to Noemy et al. [11], has always been associated with the evaluation of test results. Narad and Abdullah define it as “the knowledge gained which is assessed by marks by a teacher and/or educational goals set by pupils and teachers to be achieved over a specific period” [10](p.1). However, Kumar et al. [12] believe the concept of academic performance is amorphous. Their work gave a genre-specific overview of concepts and meanings given to academic performance over time. They listed that certain factors like skill and ability, achievement-centric, knowledge-centric and a combination of these factors have been used to define academic performance over the years.

Academic performance in education has been and is still very significant today. As such, poor academic performance has been a concern for all relevant education stakeholders. The growing literature on the declined academic performance of pupils has been attributed to several determinants or factors. For instance, several determinants of academic performance have been identified in the basic schools. These comprise pupils'/learners' characteristics, school and home environment/climate, teacher's characteristics and home-based factors like socioeconomic status and educational level of parents [9,11,[13], [14], [15]]. Other published works also group these factors as social, economic, environmental, psychological, and personal factors [16,17]. Throughout the literature, these factors differ from person to person and geographically, that is, from country to country [16,17].

2.2. School environment/climate

The school environment, according to Tapia-Fonllem et al. [17](p. 1), is a “set of relationships that occur among members of a school community that are determined by structural, personal, and functional factors of the educational institution, which provide distinctiveness to schools”. In the evaluation of the academic performance of pupils, the school environment is identified as crucial [18]. Oppong-Sekyere [18] found that the school environment highly influences pupils’ academic performance. Similarly, in a study by Mpiani [13] in the Ashanti region of Ghana, the school environment and other factors affected academic development and performance. The growing body of literature has identified some elements of the school environment, and a few, like teaching and learning materials (TLM), effective supervision, class size and class schedule, and the medium of instruction, are discussed.

In the school environment, the availability of teaching and learning materials is recognised as very important. Research has shown a positive correlation between providing teaching and learning materials in schools and pupils' academic performance [7,19]. The lack of teaching and learning materials such as textbooks, teacher's guides and other infrastructural deficits in a school is most likely to affect the academic performance of pupils [5,13,20,21]. Such pupils are also not likely to perform well in internal and external examinations [22].

Furthermore, effective supervision of teaching and learning has proven to be one of the efficient ways to obtain higher performance rates in schools [5,8,16]. Effective supervision ensures that a teacher performs classroom duties as expected to foster effective teaching and learning, as recommended by Neagley and Evans [23]. Poor supervision was identified by Saviour et al. [20] as a significant cause of poor academic performance among BECE candidates.

Additionally, class size and schedule have been identified as important elements of the school environment. The number of pupils per classroom is reported to affect pupils' academic performance [8,24]. The literature shows that students do well in small class sizes as smaller class sizes facilitate a conducive learning environment [18]. On the other hand, larger class sizes are recognised as difficult to manage. Classrooms with smaller numbers of pupils mostly perform better than larger class sizes since the teachers have enough time to attend to the smaller numbers than the larger class sizes. According to Alhassan [9], small class sizes make it possible for teachers to assess students and give feedback. Large class sizes are mostly congested and not conducive to effective teaching and learning [25]. Akrofi [8] identified large class size as a factor that influenced low academic performance according to teacher's perceptions.

Moreover, quite a few studies have shown that schools that follow daily scheduled activities have a positive effect on the academic performances of their pupils [22]. Therefore, school authorities are encouraged to see to it that activities scheduled on the school timetable are judiciously followed by pupils and teachers [9].

Lastly, the medium of instruction in the classroom is recognised as one of the most challenging obstacles to improving the quality of instruction among pupils. Mushtaq and Shabana [16] established that competence in the English language is the most crucial classroom factor that impacts pupils’ academic performance. In most Ghanaian schools, the medium of instruction is the English language [9]. Research has revealed that pupils who are primarily taught in local languages by teachers find it difficult to read and comprehend English language textbooks, and that affects their academic performance in examinations [18].

2.3. Pupils’ characteristics

Pupils or learners play significant and diverse roles in their academic performance, and literature is replete with pupils' characteristics that impact academic performance [8,13,18]. However, discipline, hard work, motivation and school attendance are considered in this study. According to Akrofi [8], the academic performances of pupils are partly attributed to their ability to study effectively through discipline and hard work. Certain negative attitudes of pupils could affect their studies and eventually lead to poor academic performance [26]. Using mobile phones in class without concentrating on lessons being taught and the lack of respect for teachers could affect pupils’ academic performance.

Motivation has also been identified as an essential factor that influences student's academic performance at all different levels of education [12,27]. Mostly, students/pupils do not have the self-motivation for personal studies, but when motivated by others, they put in all their efforts to achieve their goals [5,12]. Motivation from parents/guardians in any form also encourages them to achieve good academic performance [26].

School attendance is another variable that drives academic performance. Pupils who are regular in school mostly perform well academically since they receive the correct and adequate instructions to understand key concepts of the syllabus [8,20]. Studies show that pupils’ regular attendance at school has a significant relationship with academic performance [8,20]. On the other hand, pupils who mostly play truant or leave school without permission miss classes, negatively affecting their academic performance [28].

2.4. Homebased or family factors

Home-based factors like parents' socioeconomic status, parents' level of education and involvement in children's schooling significantly impact academic performance. The socioeconomic status of parents, such as income, type of education and employment, and place of residence, have been linked to the academic achievement of pupils [5,9,14,15]. Research shows pupils from wealthy homes could easily have access to learning aids than pupils from poor homes. Even though the availability of learning aids does not guarantee excellent academic performance, it gives pupils from wealthy homes the upper hand compared to pupils from poor homes.

Besides parents' socioeconomic status, there is a correlation between parents' educational level and the performance of their children in school. Parents having attained higher education have a better appreciation of the importance of their children's education than parents who may not have attended school [5,15]. It is most likely that highly educated parents would easily provide their children's educational needs to make them comfortable to learn for improved academic performance [9]. Therefore, different studies have shown a significant relationship between parents' level of education and pupils' academic performance [9,29].

Studies have shown that children whose parents show keen interest in their education are more likely to perform better in school [5]. Such parents visit their children in school, attend Parent Teachers Association (PTA) meetings [9], and assist them with their homework [14], among others.

2.5. Other important factors

Besides the aforementioned factors, other factors like teacher-related and cognitive factors also impact pupils’ academic performance. Without a doubt, all these factors are very important but only a few of them can be examined in this study.

2.6. Basic education in Ghana

There are three (3) education cycles in Ghana: basic, secondary (senior high) and tertiary. Basic education, as described by Nugba et al. [7], is essential for the development of any country. It is the level of education that develops people's capacity to read, write and calculate [7]. Basic education in Ghana consists of two (2) years of kindergarten, six (6) years of primary, and three years of Junior high education in a public school (government-owned) or a private school (privately owned). It is regarded as a terminal stage [30]. Therefore, in the final year of Junior High School, pupils must write the BECE before progressing to Senior High School (SHS) when they obtain the required pass rate. Over the years, many pupils in Ghana and other West African countries have been unable to make it to SHS due to poor academic performance in the BECE [7,8,13,31]. Even though the GES, with efforts from other relevant stakeholders, continue to work towards improving the teaching and learning environment, there is still much to be desired.

For instance, there is a code of conduct for staff or teachers and students in the pre-tertiary level of education. These codes of conduct guide the behaviour, attitudes, and character of students and teachers/staff to ensure that conditions for effective learning and teaching are created and maintained at the pre-tertiary educational levels [32,33]. The code of conduct for students in the pre-tertiary levels focuses on various aspects such as regular attendance to school, punctuality, examinations, and examination malpractices. According to the code of conduct, students are expected to actively participate in all academic activities and complete classwork or homework without exception. Additionally, the code prohibits teachers from utilising a student's labour in any way, whether with or without the parent or guardian's consent. Moreover, teachers are not allowed to assign students on errands during contact hours or exploit their labour for personal or private purposes with or without the parent or guardian's consent. In summary, no employee of the Ghana Education Service is permitted to subject a student to any form of exploitative labour.

2.7. Academic performance of BECE candidates

Available reports show that the academic performance of pupils at the BECE dwindled from 2008 to 2011 and from 2013 to 2017. In 2008, over 62 percent of 338, 292 pupils passed. In 2009, the pass rate decreased to 50.21 percent. Subsequently, the percentage of candidates who obtained the pass mark and were admitted into senior high schools decreased to 49.12 percent in 2010 and 46.93 percent in 2011 [34].

Krachi West has been battling with poor academic performance at the BECE for some time. A study of the BECE results obtained from the Krachi West District Education office revealed that pupils in the district have been recording poor academic outcomes over the years, which has become a major concern for all stakeholders of education in that district. The district's overall pass rate of BECE candidates who qualified for placement into SHS in 2013 was 28.5 percent. In 2014 and 2015, out of 574 and 644 candidates presented for the BECE, only 20.2 percent and 36 percent, respectively, obtained the qualified grades for SHS. As evidenced by the poor academic performance of these candidates, many were unable to continue their education, which is very concerning. It is, therefore, imperative that factors influencing the poor academic performance of pupils in this district be investigated. The data gathered can help create local and integrated strategies to address this issue [35].

2.8. Theoretical framework

This study used Bronfenbrenner's ecological theory, which posits that the development of the child in society is influenced by several factors [36]. This theory further explains that there are several levels of systems in the environment that influence a child's development. They include microsystem, mesosystem, exosystem, macrosystem and chronosystem (See Fig. 1).

Fig. 1.

The bioecological model of human development by Bronfenbrenner.

Source: Buckley and Budzyna [37].

This theory explains that several levels of systems in the environment influence the child's development (in red). These levels include the microsystem (in gold), the mesosystem (in yellow), the exosystem (in green), the macrosystem (in blue) and the chronosystem (in purple). The microsystem has to do with the child and direct contacts such as parents, schoolmates, and teachers, which influence the personality and development of the child [36,38]. The mesosystem, on the other hand, consists of the connections and interactions between microsystems. It could be an interaction between the parents and the teachers of the child about the academic performance of the child, which could directly or indirectly shape the behaviour and attitude of the child towards personal development [36,38]. The third layer (the exosystem) deals with the larger systems beyond the child's control. The macro system also has a direct and indirect impact on the development of the child. The chronosystem refers to the changes that take place across the various levels or layers of the ecological system over some time [38].

The Bronfenbrenner ecological system theory has educational implications, as it demonstrates how various levels of the theory affect pupils' motivation at all stages of learning [39]. This theory has been used in various studies and has successfully proven to be one of the best theories for understanding the development of the child in the education sector [40]. Therefore, the importance of this theory cannot be overemphasised as it helps to broaden the horizon of researchers in understanding how the academic performance of pupils is impacted by various structures in society [39,40]. Furthermore, this theory informs practical application for the instruction of better educational environments. The researchers chose this theory because it is better suited to studying factors that affect pupils' academic performance in the Krachi West district. The researchers concur with Bronfenbrenner's assertion that students' academic performance in school depends on the interaction between students' attributes and the environments they encounter, emphasising the intricate relationship among these settings.

2.9. Conceptual framework

Building upon the preceding discussion, the bioecological model is introduced in the context of Krachi West District to facilitate predicting pupils' academic performance. To test the model, data from pupils, teachers, head teachers, and PTA/SMC executives were used to evaluate the effects of pupils’ characteristics and school environmental factors on academic performance.

Based on Bronfenbrenner's bioecological model, the proposed model (Fig. 2.) emphasises the importance of interactions between different settings in pupils' educational development and academic performance. At the child's/pupil's level, academic performance is examined by looking at the child's motivation to study, punctuality, and regularity at school. Additionally, the model considers the role of the family or household in improving the child's academic performance. If the child's home conditions are favourable and parents have a positive attitude towards education, it can benefit the child's performance in school. At the school level, the model focuses on factors such as teaching and learning materials, conducive classrooms for teaching and learning, and the availability of teachers. The study also focuses on the impact of the community, including the School Management Committee/Parent-Teacher Association (SMC/PTA) and other community stakeholders, in improving the academic performance of schoolchildren.

Fig. 2.

Conceptual framework adopted from Bronfenbrenner's theory of bioecological model.

Source: Authors construct based on Bronfenbrenner's Model (2020)

Recognising the interconnectedness between a child's home, school, teachers, friends, and community is essential, as these factors shape their behaviour and influence their academic performance. A bidirectional relationship exists between the child (pupil) and the family or household, the school environment (friends, the school, teachers), and the community. Moreover, a conducive school environment can help build the child's confidence and keep them engaged, leading to improved academic performance.

3. Materials and methods

3.1. Design of the investigation

The study used the embedded design to collect quantitative and qualitative data. The embedded design is a mixed method design with one data set providing a supportive, secondary role in a study based primarily on the other data type [41]. Questionnaires were designed to collect data from the pupils through the quantitative approach. Scores of the pupils’ district mock examinations were also gathered from pupils and validated by the headteachers of the schools. The findings from the quantitative data were augmented by qualitative data collected through open-ended questionnaires administered to headteachers and teachers, as well as face-to-face interviews with Parent/Teachers Association (PTA) or School Management Committee (SMC) chairpersons. These approaches helped to avoid any biases that might have been associated with any of the methods [42].

3.2. Study area

The Krachi West District is one of the Two hundred and sixty Metropolitan, Municipal and District Assemblies (MMDAs) in Ghana and forms part of the eight districts and municipalities in the newly formed Oti Region. It has a population of over 61,000 people [43]. The district is a peninsula bounded to the east by the Oti River that separates it from the Krachi East District, to the north by Krachi Nchumuru District, and to the south and west by the Volta Lake, which separates it from the Sene East District of the Bono East Region. It lies between longitude 00 25′ W and 00 20′ E and latitude 70 40′ N and 80 25’ N [43]. The economy in the district is dominated by the agriculture sector, with the commerce and industrial sectors being underdeveloped. The district has one nursing training college, two senior high schools and one community development Technical institute. In addition, there are thirty-two Junior High Schools, with twenty-six (26) being public and five (5) private schools. There are sixty-three (63) primary schools, comprising fifty-three (53) public and ten (10) private schools.

3.3. Participants

The study population comprised junior high school pupils in some selected rural and urban schools, teachers, head teachers, and PTA/SMC chairpersons in the Krachi West District. The participants were recruited using cluster, simple random, and purposive sampling techniques. The district has 32 Junior High Schools (JHS). For this study, these schools were clustered into urban and rural JHS. This categorisation aligns with the Ghana Statistical Service (GSS) classification of urban and rural areas in the district [43]. All the Junior High schools in each cluster had an equal chance of being selected. The urban JHS, named Cluster one (1), had 12 schools with a population of 1, 351, while the rural JHS, named Cluster two (2), had 20 schools with a population of 1442. Due to the COVID-19 pandemic, the researchers could only access the JHS 3 pupils preparing for BECE, so several schools were selected in each cluster to meet the targeted sample sizes. In each cluster, the schools were numbered, placed in a pot, and shuffled to select the schools at random. In all, 12 schools were selected for the survey: five (5) in cluster one (urban JHS) and seven (7) in cluster two (rural JHS). The differences in the number of schools in each cluster were due to the school population in the urban and rural JHS. At the end of the sampling process, the selected schools were a good representation of schools found in the district. With a total population of 2, 793 pupils from 32 Junior High Schools (urban and rural), a sample size of 338 was estimated using Yamane's 1967 formula.

$$n=\frac{N}{1+N{(e)}^2}$$

N is the total population; e is the margin of error, which is 5 %; and a confidence level of 95 %.

The sample size of each cluster was proportionate to the population size of the cluster. To obtain the proportionate cluster random sample, the sample size was divided by the population and multiplied by the cluster size [44]. Thus, the sample size for cluster one (urban JHS), 338, was divided by 2793 and multiplied by 1351 to obtain 163 as the sample size for cluster one. Going by the same formulae, the sample size for cluster two (rural JHS) was 175. These samples determined the number of school pupils selected for each cluster.

In all, there were 325 pupils from 12 schools across the urban JHS (187, 57 %) and rural JHS (138, 43 %). Due to the higher population of the urban schools, the targeted sample in that category exceeded by 25. However, the targeted sample could not be met for the rural schools because of the smaller population size. Additionally, 12 headteachers, 12 SMC/PTA chairpersons and 12 teachers were randomly selected from each sampled school. Eighty-nine percent (89 %) of the participating schools were public, with only 11 % being privately owned. A summary of the demographic characteristics of the pupils is presented in Table 1.

Table 1.

Demographic characteristics of JHS pupils in the Krachi West district.

Attribute	Frequency	Percentage
Sex of pupil
Male	168	51.7
Female	157	48.3
Total	325	100
Age
11–15 yrs.	69	21.4
16–20 yrs.	246	76.2
21–25 yrs.	8	2.4
Total	323	100.0
Parents/Guardians Pupils live with
Both parents	160	49.7
Father	34	10.6
Mother	52	16.1
Other	76	23.6
Total	322	100
Location of School
Rural	138	42.9
Urban	184	57.1
Total	322	100
School Type
Private school	36	11.1
Public school	288	88.9
Total	324	100

Many participants were males (52 %) and between the ages of 16 and 20 years (76.2 %). From the age distribution of the participants, the majority were above the basic school-going age (over age), which could be attributed to late enrolment and other factors such as repeating a grade/class due to low performance [45]. Even though most (49.7 %) of the participants lived with both parents, a good number (26.7 %) lived with a single parent, while 23.6 % lived with other relations.

3.4. Measures/instruments

Data was collected using two (2) instruments: questionnaires and interviews. Quantitative data was collected using a structured questionnaire consisting of 32 items, which included questions on sociodemographic characteristics, school environment factors and pupil-related characteristics measured on a five-point Likert scale. The scale was in the following order: strongly agree (1), agree (2), undecided (3), disagree (4) and strongly disagree (5). The instrument was subjected to a reliability test using the McDonald's Omega (ω) test, considering its robustness and accurate reliability scores. The test yielded an overall reliability score of 0.69, and the reliability of each pupil-related characteristics and school environment factors ranged from 0.60 to 0.69 (Appendix 1), suggesting that both the overall and subscales were reliable.

In addition, the study employed face and content validity through expert judgment by assessing individual questions on the questionnaire. On the other hand, the qualitative data was collected using a semi-structured questionnaire and face-to-face interviews with the help of an interview guide.

3.5. Procedure/data collection method

The structured questionnaire was administered to 325 pupils to elicit responses to specific questions bordering on factors that influence their academic performance. Also, semi-structured questionnaires were administered to 12 headteachers and 12 SMC/PTA chairpersons of the sampled schools to seek their views on quality education and pupils' academic performance, among other factors. In addition, a member of the PTA/SMC executive of the schools was engaged in a face-to-face interview using an interview guide, which allowed for flexibility. Therefore, the participants could largely express their views and experiences on specific factors affecting pupils’ academic performance.

The secondary data for this study were pupils’ self-reported scores from the district-level mock examinations, which the headteachers of the various schools validated. Therefore, performance scores formed the basis of the present analysis and were reported from the most recent district mock examinations. Writing the district-level mock examinations (a standardised test) is very important in Ghana. These mock examinations serve various purposes, such as assessing the readiness of the candidates to take the BECE and keeping them alert in their preparations. Table 2 shows the current grading system and interpretation used for the BECE and the district-level mock examinations.

Table 2.

The grading system and interpretation of the Basic Education Certificate Examination (BECE) in percentages.

Grade	Equivalent/Numerical value	Percentage	Interpretation
A1	1	(75–100)%	Highest
B2	2	(70–74)%	Higher
B3	3	(65–69)%	High
C4	4	(60–64)%	High average
C5	5	(55–59)%	Average
C6	6	(50–54)%	Low average
D7	7	(45–49)%	Low
E8	8	(40–44)%	Lower
F9	9	(0-39)%	Lowest

3.6. Data analysis

Quantitative data was coded, entered, and cleaned with Microsoft Excel and further analysed with Statistical Package for the Social Sciences(SPSS) (version 20). Academic performance was purely measured by pupils’ scores in the most recent district-level mock examination. Test scores were grouped based on the grading system of the Ghana Education System (GES) and presented using simple descriptive statistics in the form of frequency distribution, means, standard deviations and charts. Cross-tabulation and the Chi-Square (χ²) test were used to analyse the relationship between the variables. For the cross-tabulation, the test scores were categorised and re-coded as high (65%–100 %), average(50%–64 %) and low(0–49 %) based on the GES grading system (see Table 2).

Further inferential analysis was done using a multinomial logistic regression analysis to establish the combined influence of independent variables on the dependent variable and the relative contribution of each independent variable to the prediction of the dependent variable. The dependent variable in the multinomial logistic regression model was the pupils’ test scores in the most recent district-level mock examinations. The test scores were entered, categorised, and re-coded as high (65%–100 %), average (50%–64 %) and low (0–49 %) based on the GES grading system (see Table 2). Data collected was analysed and tested at a 0.05 level of significance.

The qualitative data obtained from the interviews was analysed using thematic analysis. The views expressed by the participants were classified into patterns and themes to make the analysis easier. To maintain the anonymity of the interviewees, their identities were coded using their roles (such as a teacher, a head teacher, or a PTA/SMC chairperson) to avoid linking a quote to a particular individual. The purpose of the qualitative data was to support the findings from the quantitative data.

3.7. Ethical considerations

Permission was sought from all the institutions: the Centre for Social Policy Studies, the University of Ghana, and the Krachi West District Education Office in the Oti region of Ghana. Informed consent was obtained from all the participants (and their guardians when under 18 years old), and permission to record conversations was obtained before the face-to-face interviews. All the participants were assured of the confidentiality of the data collected. Throughout the study, the researcher did nothing that adversely affected any participants physically, psychologically, and emotionally. Participation in this study was purely voluntary, and no participant was compelled in any form to take part in it.

4. Results

4.1. Pupils’ academic performance

The academic performance of pupils was assessed using results from standardised tests across various subjects in the district-level mock examination. Fig. 3 presents the distribution of student scores in the district mock examinations in six subjects: mathematics, English language, science, social Studies, Religious and Moral Education (RME) and Information Technology (IT).

Fig. 3.

Pupils' scores and performance in six (6) subject areas in the district mock examination.

The figure keys show the grading and interpretation of pupils’ scores in the district mock examination used in the BECE. Where 75–100 % = grade A (highest); 70–74 % = grade B2 (higher); 65–69 % = grade B3 (high); 60–64 % = grade C4 (high average); 55–59 % = grade C5 (average); 50–54 % = grade C6 (low average); 45–49 % = grade D7 (low); 40–44 % = grade E8 (lower), and 0–39 % = grade F9 (lowest).

From Fig. 3, the general performance of the BECE candidates was not encouraging. Only a few pupils had Grade A1 (75-100 marks) across the six (6) subjects. Even for the subject with the highest performance, RME, 27.3 % of the pupils “failed” the mock exams (0-39). In addition, nearly half of the participants “failed” mathematics.

Pupils’ performance in the English language was not markedly different from that of mathematics (see Fig. 3) as a considerable percentage of them (38.7 %) “failed” this subject. Similarly, pupils performed poorly in the science mock examination, with over 40 % failing this subject. While the performance in science was equally not encouraging, it is important to note that the percentage of candidates who achieved Grade A1 (75–100), B2 (70–74), and B3 (69-65) was relatively higher compared to mathematics and English. This possibly accounts for the relatively high mean score for science (Appendix 3). Overall, the mean performance of the pupils was low across these three subjects, as shown in (Appendix 3). The highest mean score (M = 6.27, SD = 2.82) was recorded for science, with pupils recording scores between 50 and 54 marks.

For further analysis, the academic performance along the six courses was re-categorised into three levels: High (65–100)%, Average (50–64)% and Poor (0-49)%, as shown in Table 3.

Table 3.

General performance levels of pupils in six subject areas of the district mock examination in the Krachi West district.

Level of performance	Frequency	Percentage
High	26	11.6
Average	86	38.4
Poor	112	50.0
Total	224	100.0

The data in Table 3 confirms that many of the pupils (50 %) performed poorly with scores between 0 and 49 marks, although 11.6 % did better by scoring between 65 and 100 marks.

4.2. Pupils’ performance across the urban and rural schools

In comparing pupils’ performance in the mock examination between the rural and urban schools, more pupils in rural schools (72.7 %) appeared to fall in the lower tier of the performance categories (poor) compared to their counterparts from the urban schools (35.6 %) (Table 4) using the crosstabs.

Table 4.

A cross-tabulation of the nature of residence (location) and general performance of pupils in six subject areas during the district mock examination in the Krachi West district.

Nature of residence	General performance in six subjects			Total
	High	Average	Poor
Rural	1 (1.1 %)	23 (26.1 %)	64 (72.7 %)	88 (100 %)
Urban	25 (18.5 %)	62 (45.9 %)	48 (35.6 %)	135 (100 %)
Total	26 (11.7 %)	85 (38.1 %)	112 (50.2 %)	223 (100 %)
		χ2 = 33.935, df = 2, ap < 0.001

^aSignificant at 95 % confidence level.

Furthermore, a Chi-square test showed a statistically significant association between pupils' general performance in six subjects and the nature of residence (location), as shown in Table 4. Using participants’ scores in three subjects, maths, English, and science from the district mock exams, the study compared the performance of rural schools to urban schools using cross-tabulations(Table 5).

Table 5.

Relationship between the nature of residence (location) and scores in mathematics, English language, and science during the district mock examination in the Krachi West district.

Subject	Nature of Residence	Scores of Mock Examinations									Total
		75–100	70–74	65-69	60-64	55-59	50-54	45-49	40–44	0-39
Maths	Rural	1 0.8 %	0 0.0 %	2 1.6 %	4 3.1 %	4 3.1 %	5 3.9 %	19 14.7 %	10 7.8 %	84 65.1 %	129 100.0 %
	Urban	8 5.3 %	9 5.9 %	8 5.3 %	17 11.2 %	7 4.6 %	21 13.8 %	18 11.8 %	14 9.2 %	50 32.9 %	152 100.0 %
	Total	9 3.2 %	9 3.2 %	10 3.6 %	21 7.5 %	11 3.9 %	26 9.3 %	37 13.2 %	24 8.5 %	134 47.7 %	281 100.0 %
English	Rural	1 0.9 %	2 1.8 %	3 2.7 %	6 5.4 %	5 4.5 %	9 8.0 %	10 8.9 %	10 8.9 %	66 58.9 %	112 100.0 %
	Urban	6 3.60 %	11 6.5 %	9 5.3 %	19 11.2 %	20 11.8 %	27 16.0 %	19 11.2 %	16 9.5 %	42 24.9 %	169 100.0 %
	Total	7 2.5 %	13 4.6 %	12 4.3 %	25 8.9 %	25 8.9 %	36 12.8 %	29 10.3 %	26 9.3 %	108 38.4 %	281 100.0 %
Science	Rural	0 0.0 %	3 3.0 %	1 1.0 %	4 4.0 %	7 6.9 %	12 11.9 %	7 6.9 %	8 7.9 %	59 58.4 %	101 100.0 %
	Urban	23 14.6 %	13 8.3 %	12 7.6 %	21 13.4 %	13 8.3 %	15 9.6 %	11 7.0 %	5 3.2 %	44 28.0 %	157 100.0 %
	Total	23 8.9 %	16 6.2 %	13 5.0 %	25 9.7 %	20 7.8 %	27 10.5 %	18 7.0 %	13 5.0 %	103 39.9 %	258 100.0 %

From Table 5, differences in pupils' performance from the rural schools compared to their urban classmates are observed. Most pupils from the urban schools performed relatively better in Mathematics than their rural counterparts, and the difference was statistically significant [χ² = 44.492; df = 8, p < 0.001](Appendix 4). This shows a significant association between pupils’ performance in mathematics and the nature of the residence.

Again, while the performance in the English language was poor, pupils in urban schools performed better. Approximately 58.9 % of the candidates from the rural schools “failed” English compared to 24.9 % from the urban schools. Similarly, more candidates from the urban schools scored higher marks in English. Using the chi-square analysis, a significant difference existed in pupils’ performance in this subject [χ² = 37.035, df = 8, p < 0.001](Appendix 5) between the urban and rural schools.

Likewise, pupils from the rural schools performed poorer in the science examinations compared to pupils from the urban schools [χ² = 46.03, df = 8, p < 0.001] (Appendix 6). A significant association was observed between pupils’ performance in the science subject and the nature of residence.

4.3. School environment factors and academic performance

Table 6 shows the results of pupils’ assessment of school-related factors like availability and adequacy of Teaching and learning materials (TLMs), textbooks, trained teachers, conducive classrooms, and school desks. From the responses, nearly 93 % of the pupils revealed that they feel happy at school, even though the majority reported that TLMs (51.7 %) and textbooks (54.1 %) were inadequate in their respective schools(Table 6).

Table 6.

Pupils’ assessment of school environment-related factors in the Krachi West district.

School factors	S. Agree	Agree	Undecided	Disagree	S. Disagree
I feel happy at school	197 (60.6 %)	105 (32.3 %)	5 (1.5 %)	14 (4.3 %)	4 (1.2 %)
Adequate TLMs at school	54 (16.7 %)	95 (29.4 %)	7 (2.2 %)	116 (35.9 %)	51 (15.8 %)
Adequate textbooks at school	52(16.1 %)	90 (28.0 %)	6 (1.9 %)	111(34.5 %)	63 (19.6 %)
Adequately trained teachers	194(60.4 %)	96 (29.9 %)	4 (1.2 %)	21 (6.5 %)	6 (1.9 %)
Conducive classrooms for T&L	102(32.0 %)	121 (37.9 %)	5 (1.6 %)	64 (20.1 %)	27 (8.5 %)
Adequate school desks	67 (20.7 %)	92 (28.5 %)	6 (1.9 %)	78 (24.1 %)	80 (24.8 %)
Work for teachers during class hours.	35 (10.9 %)	28 (8.7 %)	3 (0.9 %)	121 (37.7 %)	134 (41.7 %)

S. Agree = strongly agree; S. Disagree = strongly disagree.

For further analysis, pupils' assessment of the school environment factors was further re-categorised into two sets (Good and poor). The 5-point Likert scale (Strongly agree, agree, undecided, disagree and strongly disagree) was re-conceptualised. All positive statements, strong agreements and agreements were categorised as “Good environment”, and all disagreements and indecisions were categorised as “Poor environment”. Moreover, pupils’ academic performance was also re-categorised into high and low performance to test for the existence or otherwise of any relationship between these two variables. A cross-tabulation of school environment factors and performance is shown in Table 7.

Table 7.

The relationship between pupils’ academic performance and school environmental factors in the Krachi West district.

Performance	School Environment Factors		Total
	Poor	Good
High	28 (75.7 %)	9 (24.3 %)	37 (100.0 %)
Low	92 (51.4 %)	87 (48.6 %)	179 (100.0 %)
Total	120 (55.6 %)	96 (44.4 %)	216 (100.0 %)
χ2 = 7.320; ap = 0.007

^ap is significant at a 95 % confidence level.

Relating school environment factors to the performance of pupils, a statistically significant association (χ2 = 7.320; df = 1, p = 0.007) was found. This shows that the observed differences between performance and school environment factors were significant. The inadequacy of teaching and learning materials (54 %) and textbooks (52 %) reported by pupils in the respective schools was supported by the qualitative data gathered from the headteachers and teachers. A stock of the textbooks available in the schools is presented in Appendix 7.

In addition, when headteachers were asked to mention facilities lacking in their schools, they mentioned the lack of a computer laboratory and a library, inadequate desks for pupils, congested classrooms invaded by bats, and human and vehicular noise because schools are closely sited by roads, among others. This assertion was further supported by the records on the stock of textbooks in the respective schools (Appendix 7).

Below is the remark by one headteacher:

A school should have a computer laboratory with functioning computers that will help record-keeping and research. In addition, the school should have a library where children can go and read during leisure. I expect the school to have a sound system, a television, or [a] projector to aid in the teaching process especially when teachers want to show video content to learners to further help in lesson delivery but unfortunately, we do not have all that, which makes teaching and learning difficult. (A head teacher of one of the surveyed schools).

4.4. Pupil characteristics and academic performance

The factors explored under pupils' characteristics included punctuality and regular school attendance, personal study time at home, motivation, supplementary reading materials, and other basic school needs. All the pupils’ characteristics examined in this study were given favourable assessments by the study participants, as shown in Table 8.

Table 8.

Summary of pupils’ characteristics as assessed by pupils in the Krachi West district.

Pupil characteristics	S. Agree	Agree	Undecided	Disagree	S. Disagree
Regular at school	234 (72.4 %)	75 (23.2 %)	3 (0.9 %)	10 (3.1 %)	1 (0.3 %)
Come to school before the morning assembly	218 (67.7 %)	91 (28.3 %)	2 (0.6 %)	8 (2.5 %)	3 (0.9 %)
I understand and enjoy my teachers' lessons	121 (37.3 %)	176 (54.3 %)	7 (2.2 %)	20 (6.2 %)	0
I have someone who helps me with my homework or studies at home	63 (19.6 %)	101 (31.4 %)	10 (3.1 %)	82 (25.5 %)	66 (20.5 %)
I have enough time at home to do my homework	134 (41.4 %)	128 (39.5 %)	6 (1.9 %)	38 (11.7 %)	18 (5.6 %)
I am motivated to study at home	140 (43.3 %)	133 (41.2 %)	4 (1.2 %)	33 (10.2 %)	13 (4.0 %)
I attend extra classes	113 (35.3 %)	105 (32.8 %)	6 (1.9 %)	60 (18.8 %)	36 (11.3 %)
I am always provided with breakfast before I go to school	98 (30.3 %)	103 (31.9 %)	6 (1.9 %)	55 (17.0 %)	61 (18.9 %)
I have supplementary readers at home provided by my parents/guardian	96 (29.9 %)	117 (36.4 %)	5 (1.6 %)	57 (17.8 %)	46 (14.3 %)
My parents provide me with basic school needs	183 (56.7 %)	94 (29.1 %)	2 (0.6 %)	26 (8.0 %)	18 (5.6 %)

S. Agree = strongly agree; S. Disagree = strongly disagree.

From Table 8, most of the pupils reported they understood and enjoyed their teacher's lessons, had enough time at home to do homework and had support with doing their homework as well.

Also, 46 % of the pupils had no help with their homework or studies at home.

Even though the study participants(pupils) gave favourable self-assessments about their performance, the headteachers and the teachers interviewed gave contrary views. A maths teacher in one of the schools had this to say:

Some of them are just lazy and have no time for their books. I think a lack of self-motivation to learn is one of the factors that contribute to their poor performance. (A maths teacher in one of the schools)

Another head teacher also gave the following remarks about the poor performance of the pupils:

Some of them are truants and others come to school late because they have to go to farm, fishing or perform other house chores before coming to school. Some of them are also indiscipline and have no respect for authorities. They use mobile phones in class whilst lessons are ongoing. How can they concentrate?

These assertions by the headteacher and the teacher were corroborated by one of the PTA chairpersons when he was interviewed:

From the interaction I had with the headmaster, he told me the pupils do not learn. They like watching television and roaming in the market on market days and they use mobile phones in class even when lessons are ongoing. Some of them, especially the girls, were into relationships and had no time for their books. Because of that, even in 2018, just about 6 boys and 1 girl were able to pass well. Some of the girls too became pregnant.

For further analysis, pupils' characteristics were re-categorised into two sets (Good and poor) from the 5-point Likert scale (Strongly agree, agree, undecided, disagree and strongly disagree). Positive statements like strong agreements and agreements were categorised as "Good pupils' characteristics". In contrast, disagreements and indecisions were categorised as "Poor pupils' characteristics". A cross-tabulation of pupils' characteristics and academic performance (re-categorised into high and low performance) was conducted. However, no statistically significant association (χ2 = 1.647; df = 1, p = 0.199) was found.

4.5. Influence of school environmental factors, pupils’ characteristics, sex, school type and school location on academic performance

To further examine the effect of predictors such as school type, sex, location, school environment factors, and pupil characteristics on academic performance in the Krachi West District, a multinomial logistic regression analysis was performed, and the result of this analysis is presented in Table 9. The overall model is statistically robust and a good fit for the data (χ² = 41.30, df = 34, p = 0.18), and the deviance chi-square value (p = 0.09), which was not significant implies that data used for the model did not over-disperse. A Nagelkerke R² of 0.26 shows that the set of predictors for the model correctly predicts 26 % of the variance in academic performance.

Table 9.

Summary of multinomial logistic regression analysis estimating the effect of school type, sex, location, school environmental factors and pupil characteristics on academic performance in the Krachi West district.

Independent Variables	β	S. E.	Wald	Exp(β)/(odd ratio)	95 % C. I. for EXP(β)
					Lower	Upper
High Academic Performance
Type of School
Private	1.41	0.746	3.572	4.097	0.949	17.683
Public (Reference Category)
Sex
Male (Reference Category)
Female	0.563	0.554	1.033	1.756	0.593	5.199
Location
Urban (Reference Category)
Rural	−3.289	1.065***	9.529	0.037	0.005	0.301
School Environment Factors
Good	1.06	0.58	3.337	2.887	0.926	9.008
Poor (Reference Category)
Pupil Characteristics
Poor	0.287	0.558	0.264	1.332	0.446	3.976
Good (Reference Category)
Intercept	−2.001	0.678	8.713
Average Academic Performance
Type of School
Private	1.151	0.56*	4.231	3.161	1.056	9.467
Public (Reference Category)
Sex
Male (Reference Category)
Female	−0.342	0.347	0.97	0.71	0.36	1.403
Location
Urban (Reference Category)
Rural	−1.058	0.366***	8.374	0.347	0.17	0.711
School Environment Factors
Good	0.807	0.358*	5.072	2.241	1.11	4.521
Poor (Reference Category)
Pupil Characteristics
Poor	0.212	0.354	0.361	1.237	0.618	2.474
Good (Reference Category)
Intercept	−0.401	0.402	0.995

Note: R² = 0.26 (Nagelkerke); Model Fit χ² = 107.62 ***; Goodness-of-Fit χ² = 41.30 (df = 34), p = 0.18, Deviance = 45.79 (df = 34), p = 0.09.

*p < 0.05.

4.6. Poor academic performance was used as the reference category

When comparing students who performed well academically to those who performed poorly, only the location of schools, whether in urban or rural areas, was a significant predictor of academic performance. The likelihood of obtaining high academic performance compared to poor academic performance is lower among students attending schools in rural communities than those in urban areas (β = −3.29, p < 0.01). Specifically, pupils in rural schools were about 27 times less likely to achieve high academic scores than those in urban schools.

In comparing students who performed at an average level to those who performed poorly, pupil characteristics and sex were not significant predictors. However, the likelihood of performing averagely compared to poorly was higher among students from private schools than those in public schools (β = 1.15, p < 0.01). Pupils from private schools were approximately three times (OR = 3.2, C. I. = 1.06–9.47) more likely to achieve average academic performance than poorly when compared to their colleagues from the public schools.

Further, the likelihood of recording an average academic performance than a poor academic performance decreases among pupils in rural schools than those in schools located in urban communities (β = −1.06, p < 0.01). Compared to urban schools, pupils in rural schools were about three times less likely to perform on average than poor. School environmental factors were also identified as a significant predictor of academic performance when comparing pupils who performed averagely to those who performed poorly. The results in Table 9 show that pupils in schools with good environmental factors were about twice as likely to perform average than those with poor environmental factors (OR = 2.2, CI = 1.11–4.52).

5. Discussion

The study sought to examine the association between the academic performance of junior high school (JHS) pupils and school environment factors and pupil characteristics in the Krachi West district. Using the district-level mock examination, a standardised test, the general performance of the pupils, who are BECE candidates, was not satisfactory. Overall, a third of the sampled population failed in mathematics, science, and English language.

National data from the 2020/21 academic year revealed that 75 % of JHS pupils scored below 44 % in English [46], indicating that proficiency in the English language might have affected the academic performance of pupils in the district, as asserted by Mushtaq and Shabana [4]. This is rightly so, as the English language is used as the medium of instruction in junior high schools. Although pupils performed relatively better in English than in mathematics, it is concerning that a third of these candidates had the lowest or “failed” grade in mathematics. The decline in the national average for mathematics from 54.1 % in the 2019/20 academic year to 49.3 % in the 2020/21 academic year [46] suggests that stakeholders must make greater efforts to address this chronic underperformance in mathematics. On a positive note, a good number of pupils did excellently in science, with approximately 2.5 % achieving the highest grade. This performance was better compared to mathematics and English language.

The study also found that pupils from rural schools performed poorly in all subjects compared to their counterparts from urban schools. This was supported by a statistically significant association between pupils’ general performance and the nature of residence (location). While the association does not imply causality, it does explain the observed variations in the examination scores between pupils from rural and urban schools.

One of the main objectives of this study was to examine school environment factors that influence the academic performance of basic school pupils in the Krachi West District. Generally, the study revealed that school environment factors had a positive association with pupils' performance. Specifically, the data showed that inadequate supply of TLMs, textbooks, and school desks influence pupils' academic performance. Previous research has demonstrated a positive correlation between providing TLMs in schools and pupils' academic performance [6,19]. Therefore, the lack of TLMs and textbooks was found to have a negative impact on academic performance. During the interviews, headteachers and teachers consistently reported that inadequate teaching and learning materials in their schools made teaching and learning extremely challenging. This probably explains the poor performance of some pupils in the district, which is consistent with the assertions by Mpiani [12], Adam [18] and Saviour et al. [20]. The findings also support the claim made by Mushtaq and Khan [15] that there is a positive correlation between the availability of learning facilities in schools and pupils' academic performance. It is widely acknowledged that pupils’ effective use of school facilities enhances their learning styles and boosts their academic performance.

The pupils reported that they understood and enjoyed their teacher's lessons, had enough time at home to do homework, and received support with their homework. However, despite these positive assessments, it did not reflect in their academic performance as expected. Additionally, almost half of the pupils (46 %) did not receive support with their homework or personal studies at home, which could affect their academic performance, as suggested by other studies [18,21]. This lack of support could be attributed to the low educational level of the parents or guardians. About a quarter of the parents/guardians have had no formal education, while only a third have completed JHS (refer to Appendix 8). As a result, pupils from households where parents lack formal education may have to study without parental support or guidance. Nonetheless, further investigation into the influence of parental involvement is required to draw a definitive conclusion.

The qualitative study revealed that teachers, headteachers and PTA chairpersons identified students' lazy attitude, lack of self-motivation, punctuality to school, indiscipline and distractions from phones and TV as contributing factors to their poor performance. The findings on pupils' punctuality are consistent with Adam's [18], Allen-Meares et al. [27] and Akrofi's [7] claim that regular school attendance has a significant association with academic performance. Pupils who regularly miss school are likely to perform poorly in academics. In Ghana, there is a code of conduct for students at pre-tertiary levels, which outlines specific behaviours, characters, and attitudes. These include regular attendance at school, punctuality, completion of academic work and examinations, and avoiding examination malpractices, among others [31]. Although students are expected to abide by this code of conduct, some do not. In addition, this finding affirms the views of Kumar et al. [11] and Ali et al. [26], who argue that parental motivation plays a significant role in encouraging students to strive for academic excellence. Without motivation from parents or guardians, pupils' desire to study and perform well academically may diminish.

When comparing students who performed well academically to those who performed poorly, the only significant predictor of academic performance was the location of the schools, whether they were in urban or rural areas. The study results suggest that the likelihood of obtaining high academic performance compared to poor academic performance is lower among pupils in rural communities than their peers attending urban schools. In comparing students who performed at an average level to those who performed poorly, pupil characteristics and sex did not emerge as significant predictors. Interestingly, pupils from private schools were approximately three times more likely to achieve average academic performance compared to their colleagues in public schools.

Furthermore, the data suggests that the likelihood of achieving average academic performance decreases among pupils in rural schools compared to those in urban communities. In contrast, pupils in rural schools were about three times less likely to perform at an average level than performing poorly. School environmental factors were identified to play a significant role in predicting academic performance, as students in schools with good environmental conditions were approximately twice as likely to perform at an average level compared to those performing poorly.

6. Conclusion

The relationship between students' characteristics, school environment factors, and academic performance has been a focal point of educational research for several decades. Our study, conducted in the Krachi West district, offers additional evidence to support the argument in the literature that school environmental factors play a critical role in shaping students' academic performance. The study has revealed that academic performance in the six subject areas assessed was low, with Mathematics, English, and Science showing poor results compared to other subjects. The data also showed a statistically significant association between students’ performance and their place of residence. Urban students performed better in Mathematics, English, and Science than their rural counterparts. While this association does not imply causation, it provides valuable insights into the observed differences in student scores between urban and rural schools.

Moreover, several school environmental challenges were identified in most of the schools in the district, including inadequate desks, computer laboratories, and libraries, as well as overcrowded classrooms and distracting human and vehicular noise. The study also found a statistically significant association between school environmental factors and students’ academic performance, supporting previous research that effective learning facilities in schools can enhance learning styles and academic performance. The multinomial regression analysis also confirmed that pupils attending urban and private schools were more likely to perform better academically than those in rural and public schools.

Furthermore, the findings largely agree with the ecological perspective that wider social systems influence academic performance. While school environmental factors and pupil characteristics have a relationship with academic performance as determined by theories such as the ecological theory, in this present study, it was established that the location of the schools (that is, urban or rural), the type of school (public or private) and school environmental factors (poor or good), were significant predictors of academic performance in the Krachi West District.

As the findings prove, establishing an environment that fosters learning at home and school would positively impact students' academic achievements. Given the importance of such factors in predicting academic success, stakeholders in education ought to prioritise the provision of educational facilities and resources and the enhancement of existing ones within the district's schools to create an ideal climate that boosts academic performance in the area. These findings would serve as a good source of information for all the relevant stakeholders to re-strategise appropriately by targeting integrated but local strategies to improve academic performance in the district.

The study's findings on the academic performance of Junior High School (JHS) students, as measured by district-level mock exams in this paper, raise significant concerns about educational outcomes in the Krachi West district and districts with similar characteristics. The results indicate a high prevalence of unsatisfactory performance, with only a small fraction of students achieving high scores in all six subjects. Notably, challenges like the unavailability of TLMs, textbooks, and school desks have been highlighted for relevant stakeholders' attention.

This study contributes to the literature by showing that various factors can significantly influence a student's academic performance. These include pupils' characteristics like motivation, punctuality or regular attendance at school, indiscipline and distractions from phones and TV. It has been affirmed in this paper that these factors, in combination with the school location, school type and school environment, play a crucial role in determining a student's success. The findings of this study highlight the importance of educational stakeholders focusing on public schools (which are in the majority in the district)and those in rural areas by providing them with necessary teaching and learning materials (TLMs) and relevant facilities to enhance pupils' academic performance at the basic level. In addition to the government's initiatives to provide these facilities, parents and communities can also contribute to their efforts.

It must also be emphasised that the quality of the school's learning environment and parental involvement are influential at the basic school level. Thus, a comprehensive approach involving teachers, parents, and students working together to enhance student performance is needed.

7. Limitations and future research

The study sought to sample pupils from junior high schools in the district. However, because of the closure of schools because of the COVID-19 pandemic, JHS 1 and 2 pupils were not in school at the time of the research. Even though this led to the inclusion of all JHS 3 pupils in each cluster who were present at the time of the research, the researchers believe sampling JHS 1 and 2 students would have given a better insight. Furthermore, while every aspect of the Bronfenbrenner model is vital to students' motivation, this research primarily concentrated on students’ traits and the school environment. Not all systems could be examined thoroughly.

Regarding future research, a sequential exploratory design where the qualitative approach precedes the quantitative would help in gaining deeper insights into the various factors that influence academic performance. Additionally, widening the scope to include more districts would also provide more insights and possible contrast in the factors that affect academic performance, given the fact that the conditions could differ widely across these districts. Such a study could also adopt more advanced statistical tools like confirmatory factor analysis, structural equation modelling and regression analysis.

Ethics statement

This study was approved by the Centre for Social Policy Studies, the University of Ghana, and the Krachi West District Education Office in the Oti region of Ghana. Written informed consent was obtained from all the schools and the participants (and their guardians when under 18 years old). Participation in this study was purely voluntary and no participant was compelled in any form to take part in it. Participants could opt out of this study whenever they felt uncomfortable.

Data availability statement

All data, except for individual examination scores of pupils, are presented in the manuscript. The authors believe the data presented are sufficient to support the conclusion of this study. However, we are available to provide additional data upon request.

Funding

The authors received no funding for this research.

CRediT authorship contribution statement

Richard Nyankomako Codjoe: Writing – review & editing, Writing – original draft, Investigation, Conceptualization. Linda Ama Owusuaa Amoah: Writing – review & editing, Writing – original draft, Formal analysis, Data curation. Solomon Kofi Amoah: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following is the Supplementary data to this article.