Attitude of Grade 12 learners towards Mathematics and English in Ngaka Modiri Molema District of North West Province, South Africa

Title: Attitude of Grade 12 learners towards Mathematics and English in Ngaka Modiri Molema District of North West Province, South Africa.

Margaret Toyin Aboginije – Faculty of Education – Educational Psychology, NWU

Co-author: Dr Noorullah Shaikhnag – Noorullah.Shaikhnag@nwu.ac.za

Id orcid.org/ 0000-0002 1423 7696

Senior lecturer –Deputy Director, North West University, Faculty of Education- Mahikeng campus

B Com (UDW-UKZN), BEd, MED, PhD (Educational Psychology, NWU)

Co-author: Dr Shantha Naidoo – Shantha.Naidoo@nwu.ac.za

ID ORCID:https://orcid.org/0000-0001-8107-6493

North-West University, South Africa: Potchefstroom, North West, ZA

Lecturer: Life Orientation, Sub Area Leader: Edu-HRight (Bio-Psychosocial Perspectives)

MED (Learner Support), PhD (Educational Leadership and Management, UJ).

Co-author: Prof Anna-Marie Pelser ampelser@hotmail.com

iD orcid.org/0000-0001-8401-3893

Research Professor, North-West University, Faculty of Economic and Financial Sciences- Entity Director – GIFT, Mahikeng Campus.

HED (Home Economics, PU for CHE), B Com (UNISA), B Com Hons (PU for CHE),

M Com (Industrial Psychology, NWU), PhD (Education Management, NWU)

Corresponding author: Prof A.M.F. Pelser – ampelser@hotmail.com

Ensovoort, volume 43 (2022), number 7: 4

Abstract

This study examined the attitude of High School Students with respect to academic performance in English language and Mathematics. The study made use of a research design of a cross-sectional survey, and at the same time employed a non-probability, convenience-purposive sampling method. The final sample size of 344 respondents participating in the study met the inclusion criteria and were also willing to participate in the research. Appropriate techniques used for the study were Cronbach’s alpha coefficient values and the multiple linear regression approach. Findings from the study indicate that while learners’ attitude has a significant determining effect in respect of mathematics, the reverse holds in the case of the use of the English language.

It is therefore necessary to correct the pattern in order to enhance good results in the two major subjects.

Key words: Psychosocial factors, Attitude, Mathematics, English Language, academic performance

1. Introduction

Psychosocial factors bear a noticeable influence on learners’ academic performance and success (Fennie et al., 2020:98). Some of the relatively well-documented psychosocial factors affecting learners’ academic performance include: academic motivation, self-esteem, perceived stress, interest, attitude, self-concept, test anxiety, self-efficacy, lecturer and student interaction, academic overload and locus control (Dianabasi et al., 2017:7-8; Kolo et al., 2017:3-4; Sommer & Dumont, 2011:387). Research has shown that behaviour of learners especially in relation to Mathematics and English, was influenced by some psychosocial or non-cognitive factors to a great extent (Dianabasi et al., 2017:6). For example, Porchea et al., (2016:700) established that learners with greater motivation would be more likely to achieve greater achievement in their academics than those with lesser motivation.

Researchers attempted to identify solutions to the problem of low learners’ performance in Mathematics and English, but no significant progress has been achieved. The focus of such initiatives was primarily on cognitive aspects with little or no emphasis placed on psychosocial factors (Ugwuanyi et al., 2020:493). Prediction of success or failure in English or Mathematics may be based on some variables other than the intelligence quotient level of learners. Such predictions are those factors that affect the emotional intelligence of learners (Monica & Ramanaiah, 2019:65). The present study focused on learners’ attitudes among other psychosocial factors that affect their academic performance in Mathematics and English.

The definition of attitude is a learned inclination of an individual to reply positively or negatively towards a situation, an object, a concept, or a person (Mazana et al., 2019:210). It is also an individual’s belief, expressed in their thoughts and feelings, as well as in their actions. A learner’s attitude towards a particular subject will determine his /her performance in that subject. Numerous factors have an impact on attitude. These include among others, type of school, peer groups, society and home environment (Mazana et al., 2019:209). Other factors include class activities, subject content, parental influence, teachers’ emotional support, teachers’ affective support, scarcity of teachers and inadequate resources (Solheim et al., 2018:513-515; Blazar & Kraft, 2017:147). Roberts et al., (2018:2) states that motivation, experience and self-efficacy affect learners’ attitudes and perception towards studying Mathematics and English. The authors further posited that learners’ attitude towards learning is enhanced when they are allowed to accomplish tasks by themselves; get to see, get to feel and get to touch. There is a boosting of a learner’s confidence or self-efficacy when he/she understands something that was previously perceived as extremely difficult to grasp (Roberts et al., 2018:11).

The South African school system, especially in the North West Province, is challenging with many complex problems (Mbiza, 2018; Child, 2018). According to Van Staden and Bosker (2014:2), there is generally a poor performance in Mathematics among South African learners, as revealed in international comparative assessment studies such as PIRLS, the Trends in International Mathematics and Science Study (TIMSS) and the Southern and Eastern African Consortium for the Monitoring of Educational Quality (SACMEQ). Many learners are weak in Mathematics and English, which leads to slow progression, failure or dropout of learners when they enter university (Bokana & Tewari, 2014:259). There is a general out-cry that the standard of education is falling, as well as pervasive moral decadence among learners (Adeyemi & Adeyemi, 2014:97-100). Zhang (2015:254) also reported that there is a widespread lack of academic performance among Mathematics learners, according to the survey. Learners’ lack of dedication to the subject matter is one of the causes of low performance in Mathematics (Sparks, 2011:1).

Experiences gathered in the learning process can either provide positive or negative outcomes depending on learners’ attitude (Laguador & Dotong, 2020:1128). This outcome in learning is what we refer to as academic performance. Negative learning experiences sometimes could be a reinforcement that could also lead to changes in attitude towards a better academic performance.

Attitude, as defined by Olufemi (2012:62) is a positive or negative reaction towards a notable abstract or concrete proposition or object. An individual grows up to acquire strong beliefs and feelings or attitudes towards different things in life. These feelings could be positive or negative. Attitudes are relatively steady but could be changed or modified.

Education is the bedrock of development for any society, and the progress of such a society directly linked to learners’ academic performance (Mushtaq et al., 2012:17; Okunlola et al., 2016:15). Studies have shown that psychosocial factors influence learners’ behaviour, particularly towards Mathematics and the Sciences (Dianabasi et al., 2017: 6-13). Psychosocial factors like learners’ or teachers’ attitude, social interaction and self-efficacy of learners play an important role in learners’ academic performance and are necessary for the delivery of high quality teaching and learning. These above psychosocial factors remain the essential determinants of learners’ academic performance (Kolo et al., 2017:1-3). Several challenges characterize the education system in South Africa. The poor performance of learners in Mathematics and English due to psychosocial factors is becoming worrisome. Van Staden and Bosker (2014:2) reported that South African learners performed poorly in the Third International Mathematics and Science Study Repeat (TIMSS-R) conducted by the Education Achievement International Association for Evaluation of Education Achievement. The World Economic Forum (2012-2013) also revealed that South Africa was rated 143rd in the world in Mathematics, while Villiers (2019) reported that despite significant investment in the educational sector, South Africa has some of the worst outcomes in education (Mathematics) in the world. The Department of Science and Technology (2012) has advocated the need for some strategies and initiatives, to improve high-human capital in order to boost capacity development. When applying and implementing these needed initiatives successfully, Mathematics teaching will improve in high schools.

The single most important and widely used indicator of learners’ academic achievement or success in order to gain admission into a Higher Institution of learning is their academic performance (Sommer, 2013:44). Resulting from this, various research on the academic performance of learners in high schools have been performed to ascertain different factors predicting learners’ academic performance. Some of the factors identified vary from age, gender, race, financial status, socio-economic status, intelligence, and psychosocial factors such as motivation, interaction, self-efficacy, self-esteem, and test anxiety. Interaction between the teacher and the learners is also one of the factors that predicts academic performance.

Interactions between teachers and learners in the high schools can significantly affect learners’ performance. When a teacher and learners interact, their behaviour, feelings and thought would influence each other and result in behavioural changes (Kolo et al., 2017:1-3). Some major steps that could build the teacher-learners relation positively were argued by the author such as teachers asking learners questions regarding their areas of interests and trying to understand as they interact with them. They should also pay attention to learners’ non-verbal responses in the body language emanating from them. They should use self-experience in the classroom setting in order to understand learners’ personal feelings. They should share their personal experience with the learners and build on information from the learners in order for them to feel free in expressing their own worries, stories and interests. In some cases, a teacher should show compassion towards the learner by putting himself/herself in the position of the learner. Extensive improvement follows this interaction process in the form of better teacher’s listening skills, communication and professionalism in a sound effort to know the world of learners, which is capable of opening the door of interaction that brings about positive learner academic performance.

The main aim of this study is to investigate the effect attitude has on English and Mathematics academic performance among Grade 12 learners in the North West Province of South Africa.

The specific objective is to examine the relationship between attitude and leaners’ academic performance in English and Mathematics.

2. Hypotheses

The study will validate the following hypotheses:

  1. There is no significant relationship between attitude and learners’ academic performance in English in the North West Province, South Africa.

  2. There is no significant relationship between attitude and learners’ academic performance in Mathematics in the North West Province, South Africa.

3. Literature review

The categorizing of attitude change takes place under four sub-headings: Learning theories, Consistency theories, Functional theories and Social Judgment theories. Development of these theories took place during the 1950s and 1960s. A major emphasis on these theories was on behavioural response to stimuli. Events in the environment (that is, Stimulus) create an emotional response in a person (Jaiswal, 2017:2). The current study expresses two of these theories.

3.1 Learning Theories

Learning theories of attitude change as developed by Hovland et al. (1953) was the focus in the Yale Communication Research Program in social psychology. It was indeed a central topic of social psychology at that time to the extent that it was locally known as “the attitude-change project” (McGuire, 1996:42). These researchers projected that opinions tend to persist until the individual receives some new knowledge. The Yale researchers stressed the importance of incentives and the drive-reducing components of persuasive communications as mechanisms for reinforcing new beliefs and attitudes, resulting in adoption. The Yale approach to attitude modification emphasizes attention, comprehension and acceptance. A person must pay attention to a particular subject, and understand the teaching before acceptance can happen. When these have taken place, then there can be a change of attitude towards that subject. This theory also places emphasis on incentives. Incentives can be direct financial or physical benefits. A learner who once had a negative attitude towards a subject might likely change his or her attitude when rewarded, perhaps with applause whenever he or she answered a question correctly.

3.2 Consistency Theories

Consistency theories of attitude change have a prominent place in social-science history, and they continue to be a significant problem to this day (Kroesen et al., 2017:191). There has been a study done on the attitude-behavior relationship in three different ways. The first is the presence of an attitude-behavior relationship. The second identifies and assess the various moderating factors that affect attitude-behaviour consistency (Kroesen et al., 2017:191). For example, the internal consistency of the attitude change (Fadzil et al., 2019:87), the temporal constancy of the attitude (Szabo & Matar, 2021:4) and the certainty with which the attitude was believed were discovered to affect the degree of attitude-behaviour consistency (Alsaad, 2021:3). All these cover the affective (emotion), cognitive (thought) and behavioural aspect (action) of attitude change. Rosenberg and Hovland (1960) suggested that attitudes are multidimensional, including, the cognitive, affective and behavioural components. They emphasized that such measures would be unable to predict behaviour (Kroesen et al., 2017:191). Resulting from this, the third category of attitude change was proposed, which was the most fundamental and relevant of all. It deals with the question of “how do attitudes guide behaviour?” The expectancy-value model formed the basis of this theory. One of the ways to discover the extent to which learners maximize their experiences in active learning is through a lens of an expectancy-value model (Cooper et al., 2017:1). The theory of the expectancy-value model predicts that learners will put more effort into activities they concurrently perceive to have value and at which they expect to succeed. In relation to learners’ attitudes towards the learning of Mathematics and English, it is very pertinent to understand that learners will perform better if they place more value on the study of these two subjects. Research had shown that learners who hold a higher success expectation than their fellow learners showed better performance in these subjects than those with less success expectation (Schnettler et al., 2020:2). Expectancy explains the developing of an individual’s confidence through his/her ability to succeed at a task, which comprises of his/her effort and ability beliefs. Value, on the other hand, refers to the importance, usefulness or enjoyment a person associates with success in a task (Kosovich et al., 2017:4). Value comprises of four components: such as attainment value (the importance of the task to an individual), intrinsic value (the interest or enjoyment acquired from the task), utility value (the usefulness of a task to a person’s goals), and cost (these are the sacrifices or negative emotions related to the task) (Kosovich et al., 2017:5). The expectancy-value model relates strongly to academic performance because it accounts for achievement behaviours and choices. It also describes various factors that lead to motivation in the form of interest. Expectancy permeates every level of interest development and can theoretically improve or undermine interest (Kosovich et al., 2017:5). Learners with higher expectancy-value grades have probability of reporting higher interest.

3.3 Learners’ attitude and study habits

Learners’ attitudes and their study habits are significant factors that affect or influence their academic performance. Capuno et al., (2019:548) conducted a study on attitudes, study habits and the academic performance of junior high school students in a public high school in the Phillipines. The total of Grade 9 students enrolled in Mathematics forming part of the study was 177. The study assessed their study habits and attitudes to measure their academic performance in Mathematics based on their first-quarter grade. It revealed that there was a negligible positive correlation between the attitudes and academic performance of the respondents in terms of their self-confidence, motivation and enjoyment. The results further revealed that there was a weak positive correlation between the value and their performance in Mathematics.

In another study conducted on conceptual understanding, attitude and performance in Mathematics of Grade 7 students by Andamon and Tan (2018:96) it was revealed that students’ attitude towards their overall Mathematics mean was uncertain, which implied that students were neutral in their attitudes towards Mathematics. Nevertheless, there was a significant relationship between students’ attitude and their academic performance in Mathematics. The regression analysis conducted indicates students’ attitudes towards Mathematics and their conceptual understanding of the subject are the best predictors of their performance. This means that the higher the attitude of learners towards Mathematics, the better their performance in Mathematics. According to Mazana et al., (2019: 207) who investigated students’ attitude towards learning Mathematics in Tanzania, it was discovered that students exhibit a positive attitude towards Mathematics, but their attitude becomes less positive as they move forward to higher levels of education. The study made use of quantitative and qualitative study to collect data from 419 primary school students and 318 secondary school students from 17 schools and colleges respectively. There was also a significant positive weak correlation between students’ attitude and performance in Mathematics.

Whether researchers used quantitative or qualitative research methods or whether the studies conducted were on university students or high school learners in different countries, the results show that the attitude of learners towards studying of Mathematics and English influence their academic performance and this influence can be positive or negative. This was one of the most noticeable assessments of the preceding literature reviewed on attitude for the current study. Forming a positive attitude brings improvement to the performance of learners, while a negative attitude hinders effective learning of Mathematics and English and consequently affects learning outcomes (Mazana et al., 2019:208).

4. Research Methodology

The research design for this study is a cross-sectional survey research design. Research design is the use of evidence-based protocols, procedures, and guidelines that provide the measurements and framework for investigating a research study (Majid, 2018:1). The choice of the research design is a methodological decision made by the researcher at the beginning before submitting the study for ethics review and collection of data. The research design is determined resulting from the research questions, research objectives, population of study, and sampling strategies (Majid, 2018:1). All the aspects above determined the nature of the study.

The research approach for this study was a quantitative empirical study through a cross-sectional survey research design. Assumptions drawn from data were collected and analyzed. The design, as described by Cummings (2018:2) and Connelly (2016:1) was a smooth and brisk process; all variables collected happened without any manipulation. This design was preferable to determine the relationship of variables during the period of the study. It is a design that attempts to explain such things as the possible relationship between values and characteristics (Makworo et al., 2014:119-121). It is therefore the appropriate design used in determining the attitude of learners’ performance in English and Mathematics at the level of Grade 12 in high schools in the study location.

The location of the study was Ngaka Modiri Molema District in Mahikeng, North West Province of South Africa. The location is easily accessible to the researcher as the sampling for this study is that of convenience sampling.

4.1 Population and sampling

A definition of population in research is all items or people one wishes to understand in the course of the study. The area where the researcher intends to carry out the study is the target area (Majid, 2018:1). It is often not appropriate or feasible to recruit the entire population for study; instead, the researcher selected a portion of the population known as a sample to include in the study, thereafter generalized the study findings from the sample to the entire population of interest.

Sampling is the process of choosing a section of the population for investigation (Rahi, 2017:3). Sampling is an important tool often used in research studies because the population of interest usually comprises of too many persons for any research study to include as participants/respondents (Majid, 2018:3).

The population of all the Grade 12 learners in all the high schools in the Mafikeng area of the district studying English and Mathematics is 2 969 according to the information received from North West Department of Basic Education, Ngaka Modiri Molema District. However, the target population for this study is the seven schools’ Grade 12 learners that have been conveniently selected and who are studying English and Mathematics. The study made use of convenient purposive sampling in selecting seven schools based on the schools that fall under public high schools and independent schools (six schools from public high schools and one school from independent high schools or private schools, seven schools in total). Initially, the researcher wanted to make use of six schools altogether (that is, four public high schools and two private schools), but the response from private schools was low as some of the private high schools do not go up to grade 12, hence the researcher decided to increase the number of public schools to meet the targeted sampling. The researcher made use of public and private high schools to give equal privilege as to participating in the research. Convenient accessibility and proximity determined the selection of schools for the study.

The sample size was 344 learners, using Taro Yamane’s formula of calculating sample size as a guideline (Singh & Masuku, 2014:15). The researcher while doing fieldwork distributed 600 copies of the questionnaires; however only 344 learners volunteered and consented to fill the questionnaires, which constituted the final sample size for the study.

4.2 Sampling method

Sampling is the process of selecting a sample of items from a data set in order to measure the characteristics, attitudes and beliefs of people (Rahi, 2017:3). Non-probability, convenience-purposive sampling was the best option due to time constraints, reduction of cost, as well as factors associated with the scope of this study. Convenience sampling is the process of data collection from a population that is very close and easily accessible to the researcher. The researcher was accompanied by a research expert, who was a neutral person to act as an independent gatekeeper, to hand out the questionnaires to all the Grade 12 learners studying Mathematics and English in the targeted schools who gave their consent to participate in the study. By making use of a neutral person to perform the action the learners were not intimidated. The sampling method used allowed for the appropriate selection and inclusion of available and willing research respondents. Although the respondents needed to meet the inclusion criteria as determined by the researcher, the final sample size of 344 constituted the respondents who met the inclusion criteria and who willingly agreed to participate in the research.

4.3 Data collection and instruments

The data collection was two-fold. The researcher collected the data as well as the Matric results of learners in English and Mathematics for the past three years, from 2017 to 2019 from the schools selected. The percentages passed and failed in these two subjects were collected to show the trend of the learners’ performance in the past years. The promotion results of learners in English and Mathematics from Grade level 11 to 12 collected from schools under study serve the purpose to assess their academic performance. The researcher made use of questionnaires to collect data from the schools. The instrument/questionnaire used in this research was a standardized, already validated one used in South African circumstances; the Tapia and March II (2004) was used to test for the attitude of learners (Ncube & Moroke, 2015:231). The researcher sought advice from experts before making use of these instruments.

The questionnaire contained three sections. Section A: to capture the learners’ demographic status such as Gender, Name, School, Age and Race; section B addressed questions testing learners’ attitude towards English while section C tested for questions regarding the same learners’ attitude towards Mathematics. The questionnaire made use of a 4-point Likert scale, ranging from strongly disagree, disagree, to strongly agree and agree. The consent forms signed by learners emphasized the fact that responses of learners would be kept confidential and that their names requested were only to correlate their attitude with their academic performance. The questionnaire was a self-administered one and it was thorough and effective. The data collection lasted for one month.

The data collection was during the period of COVID-19. The targeted schools allowed the researcher access to the schools to collect data, after the lockdown eased. In order to manage the risk associated with the pandemic and its impact on the respondents and staffs, the following precautions such as, screening for COVID-19, wearing of masks, hand sanitizing, and maintaining social distancing were put in place. The researcher, the independent/gatekeeper person, the school principals, the teachers and the learners, all strictly adhered to COVID-19 procedures and protocols.

4.4 Analysis of data

This study is purely quantitative in nature. Quantitative research method is primarily about collecting numerical data to explain a certain situation in a mathematical way and in a statistical manner (Muijs, 2011:1). In this study, the researcher used the quantitative method even though the attitude to measure is not in a quantitative form. Nevertheless, questionnaires were used to ask learners to rate statements which were then converted into quantitative data. This method is the best option for this study as the researcher made use of hypothesis testing to validate the variables under study.

Academic performance of learners in English and Mathematics was scored as 80% and above to be 7, 70-79% was rated to be 6, 60-69% to be 5, 50-59% was rated to be 4, 40-49% to be 3, 30-39% to be 2 and any mark below 30% was rated to be 1. The questionnaires made use of a a 4-point Likert scale. The value 1 – strongly agree, 2 – strongly disagree, 3 – agree and 4 -strongly agree was used.

Data analyzing was performed using Multiple Linear Regression (Astivia & Zumbo., 2019:1) with the Statistical Package for Social Science software (SPSS) version 25, at the significance level of 0.05. Using regression analysis, the researcher was able to test the hypotheses that the variables under study are contributory factors affecting academic performance in English and Mathematics. The researcher also made use of the ANOVA associated with the regression analysis to show the multiple correlation coefficient test, which helped to establish the relationship between the variables.

5. Data analysis and interpretation of results

Different statistical techniques completed the analysis of the questionnaires. The description starts with an analysis and interpretation of demographic information by means of frequencies and percentages. The reliability and validity of the questionnaires given out was also calculated by means of Cronbach’s alpha and Factor analysis respectively to determine whether the instruments were reliable and valid (Taber, 2018:1275; Yong & Pearce, 2013:80), thereafter, the hypotheses were considered using the appropriate statistical techniques and the multiple regressions. The Statistical Consultation Services of the North West University, Potchefstroom Campus assisted with statistical analyses.

The table below conveys the demographic information of the Grade 12 learners.

Table 5.1: Demographic Information: Type of school

Type of school

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Public high school

321

93.3

93.3

93.3

Independent high school

23

6.7

6.7

100.0

Total

344

100.0

100.0

5.1 Reliability

This section gives a brief description of how the reliability of the questionnaire used to collect data for this research study was determined. Reliability is the extent to which a test, questionnaire, observation or any measurement procedure provides the same results every time (Bolarinwa, 2015:198). In other words, in administering the instrument (questionnaire) multiple times at different times, the results should be the same. To ensure reliability the questionnaire used in this study yielded the same results if utilized elsewhere and needed Cronbach’s alpha coefficient calculating the two factors of the attitude scale. The Cronbach’s alpha for calculation of the questionnaires was used separately for English as well as for Mathematics. The table 5.2 below displays the results of the Cronbach’s alpha coefficients.

Table 5.2 Cronbach’s alpha coefficient values

Questionnaire Names

Cronbach’s Alpha (α)

English

Mathematics

  1. Attitude

1.1 Difficulty

0.89

0.89

1.2 Enjoyment

0.70

0.72

In Table 5.2 above, all the Cronbach’s alpha is up to 0.7 and higher and therefore assumes that the instruments are reliable.

5.2. Interpretations of the results

This section presents the results and the interpretations of the analyses done to answer the research questions and the hypotheses of the study. The researcher considered the attitude of the learners towards English separately from their attitude towards Mathematics.

Hypothesis 1: There is no significant relationship between the attitude and learners’ academic performance in English in North west Province, South Africa.

In order to test this hypothesis, the system used one way ANOVA associated with regression. The following results describe the relationship.

Table 5.3: Regression analysis of the relationship between attitude and learners’ academic performance in English.

Model R R2 Adjusted R2 SE N

.186 .035 .006 1.572 344

Analysis of variance (ANOVA)

Sum of squares df Mean square F Sig Ho

Regression 29.552 10 2.955 1.196 .292 Accept

Residual 822.681 333 2.471

Total 852.233 344

  1. Dependent Variable: Performance in English

  2. Predictors: (Constant), att10, att1, att9, att7, att8, att4, att6, att5, att3, att2

The result in the table 5.3 showed linear regression analysis of the relationship between attitude and learners’ academic performance in English. The linear regression produces a coefficient of 0.186 while R2 is 0.035. This means that attitude has a low relationship with learners’ academic performance in English. Based on the R2 value of 0.035, it indicates that the relationship of attitude only explains 3.5% of the variation in learners’ academic performance in English.

To determine if the relationship is significant or not, the researcher employed analysis of variance (ANOVA) associated with regression. The calculated F-value of 1.196 (which is not significant at 0.292) is higher than the chosen level probability of 0.05. F (10, 333) = 1.196 and p> 0.05. Hence, the null hypothesis is accepted. This implies that attitude is not significantly related to the learners’ academic performance in English.

Table 5.4: Regression analysis of the relationship between attitude and learners’ academic performance in Mathematics.

Model R R2 Adjusted R2 SE N

.251 .063 .035 1.573 344

Analysis of variance (ANOVA)

Sum of squares df Mean square F Sig Ho

Regression 55.441 10 5.544 2.241 .015 Reject

Residual 823.765 333 2.474

Total 879.206 343

  1. Dependent Variable: Performance in Mathematics

  2. Predictors: (Constant), att10, att4, att8, att7, att6, att1, att5, att9, att3, att2

The result in table 5.4 showed linear regression analysis of the relationship between attitude and learners’ academic performance in Mathematics. The linear regression indicates a coefficient of 0.251 while R2 is 0.063. This means that attitude has a low relationship with learners’ academic performance in English. Based on the R2 value of 0.063, it indicates that the relationship of attitude only explains 6.3% of the variation in learners’ academic performance in Mathematics.

To determine if the relationship is significant or not, the researcher employed analysis of variance (ANOVA) associated with regression. The calculated F-value of 2.241 (which is statistically significant at 0.015) is less than the chosen level probability of 0.05. F (10, 333) = 2.241 and p< 0.05. Hence, the researcher rejects the null hypothesis. There is a significant relationship between learners’ academic performance in Mathematics and their attitude.

6. Conclusion

The findings of this study showed that attitude of learners indeed have some impact on academic performance of grade 12 learners in high schools. The attitudes of learners in Mathematics produce a significant impact on the academic performance of learners. Improved attitudes lead to an increase in the performance of learners in Mathematics. However, the empirical findings show that the attitude of learners with respect to English Language was not statistically significant. It therefore becomes imperative for the management and teachers through a concerted effort to change the status quo and consequently promote appropriate attitudes in learners towards achieving desirable performances in the two key subjects.

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