Students pick up the perception that mathematics is abstract and therefore, the learning of mathematics would yield to them no benefit. With their attitude towards mathematics modelled and their interest for mathematics impacted by this automatic generated perception, they may never again appreciate the beauty of mathematics. In this paper, the researchers used structural equation modeling (SEM), to investigate the variables that affect students’ interest, among the variables, students’ confidence and motivation. The foregoing variables were conceptualized to have a direct effect on students’ interest in mathematics, whilst mathematics anxiety and students’ knowledge of the usefulness of mathematics were conceptualized to have indirect effects on their interest in mathematics moderated by students’ confidence and motivation. The result showed that significantly students’ confidence directly affects students’ interest in the learning of mathematics and there is a direct relationship between confidence and motivation. A student’s knowledge about the usefulness of mathematics indirectly increases the student’s interest in mathematics.
“The absence of academic motivation and lack of interest is also likely to be reflected in students’ neglect of their studies. Research over the last two decades has indicated that adolescents’ academic motivation declines over time. Recent studies show that as children get older, their interests and attitudes toward school in general and toward specific subject areas such as mathematics, art, and science tend to deteriorate (Hidi and Harackiewicz [
Informally, interest is defined in Collins Dictionary as “[
A questionnaire circulated by
In this paper, students’ confidence and motivation were conceptualized to have a direct effect on their interest in mathematics. Anxiety and usefulness of mathematics were conceptualized to have indirect effects on their interest in mathematics, moderated by confidence and motivation. Although significant research has been conducted to identify the factors that influence students’ interest and self-motivation for learning mathematics, the variables such as confidence, motivation, math anxiety, and usefulness conceptualized in this were not thoroughly examined. It is also important to add that no research like this has been done in Ghana.
Musso et al. [
Singh et al. [
Heinze et al. [
According to Adeyanju [
A substantial body of research has accumulated in the last 2 decades works that have examined the correlation between success in academic achievement in general and mathematics and science in particular. Attitudinal and affective variables such as self-concept, confidence in learning mathematics and science, mathematics and science interest and motivation, and self-efficacy have emerged as salient predictors of achievement in mathematics and science. These factors also predict mathematics and science avoidance on the part of students, which affects long-term achievement and career aspirations in the mathematics/science field.
In our study, we use a very unique approach by introducing variables that have not been examined by earlier researchers; we investigate how math anxiety, its usefulness, the confidence of the student, and student motivation affect their interest in mathematics. This study will inform the educational institutions on these variables in the formation of students’ interest.
The research was done in the Brong-Ahafo Region of Ghana. The region’s demography includes the following details: there are 57 public senior high schools (SHS) in the region. A little over two fifths of the population (42.0%) aged six and older have never been to school. The proportion of the population that has attained primary (22.3%) and middle or JHS (23.3%) is almost the same; only 11.2% have attained SHS level or higher. The education attainment is the same for males and females at the preschool level (1.2% each) and the primary school level (22.5% males and 22.0% females). Beyond these two attainment levels, male attainment is higher than that of females at each subsequent level. This low attainment level for females has implication for the economic characteristics of the population as well as fertility behaviour.
Senior high schools in the Brong-Ahafo Region of Ghana constitute the population. The sample of the study includes 275 SHS students from the Brong-Ahafo Region. To promote the generalizability of results from the sample to the population, at least one SHS in each district in the region was chosen for study.
The most important features of scientific studies include measuring and relating variables and revealing causality (if any). However, observable variables such as the students’ age, programme of study, and sex can be directly measured, while latent variables such as anxiety, usefulness, confidence, motivation, and interest cannot be directly measured. In such cases, it is important to establish regression equalities that show how endogenous structures (predicted-endogenous) are linked with exogenous structures (predictive-exogenous) [
In this study, students’ confidence and motivation were conceptualized to have a direct effect on their interest in mathematics, whilst anxiety and usefulness of mathematics were conceptualized to have indirect effects on their interest in mathematics, moderated by confidence and motivation. Therefore, by setting the model given in Figure
Conceptual path.
In Table
Codes for questionnaire items.
Construct | Variable code | 12 |
---|---|---|
Interest (I) | I1 | Mathematics is an interesting subject to me. |
I5 | I prefer doing mathematics to other subjects. | |
I23 | Solving mathematics problems require too much thinking. | |
I27 | I use my leisure time to study mathematics. | |
I30 | Mathematics is useful only to those learning mathematics as major. | |
|
||
Confidence (C) | C21 | Mathematical knowledge enables me to think logically. |
C25 | I am confident in learning mathematics. | |
C28 | I can easily follow mathematics lessons. | |
C32 | I am good at using mathematics to solve problems. | |
C36 | I feel good in learning mathematics. | |
|
||
Motivation (M) | M11 | I often learn mathematics on my own. |
M13 | I feel less encouraged to learn mathematics. | |
M16 | I often desire for knowledge in mathematics. | |
M37 | I would like to develop myself further in learning mathematics. | |
M38 | Learning has developed my reasoning ability. | |
|
||
Anxiety (A) | A8 | I do not feel comfortable during mathematics periods. |
A14 | Mathematics is a very difficult subject. | |
A18 | I feel bored during mathematics lessons. | |
A31 | I have always tried to avoid mathematics in my life. | |
A38 | I wish I do not meet mathematics any more during my further studies. | |
|
||
Usefulness (U) | U12 | Mathematics is not an important subject. |
U15 | I do not use mathematics in everyday life. | |
U24 | Knowledge in mathematics helps me to learn other subjects. | |
U33 | Mathematical knowledge is needed to solve almost all life problems. | |
U35 | Mathematics knowledge is useful to all students irrespective of the programme of study. |
The Kaiser–Meyer–Olkin (KMO) test is used to measure the adequacy of a sample. KMO test numbers are between 0 and 1. Zero means that the sum of correlations for parts of them are large in comparison with the sum of all correlations, so factor analysis is likely inappropriate. Kaiser suggests that values greater than 0.5 are acceptable. For this study, we observe from Table
KMO and Bartlett’s test.
KMO and Bartlett’s test | ||
---|---|---|
Kaiser–Meyer–Olkin measure of sampling adequacy | 0.737 | |
Bartlett’s test of sphericity | Approx. chi-square | 36442.277 |
Df | 3321 | |
Sig. | 0.000 |
Bartlett’s test measure whether or not an original correlation matrix is an identity matrix. If a matrix is an identity matrix, all correlation numbers would be zero. From Table
After extracting standard components and rotating them to normalize them, loaded indicators to them were conducted by factor analysis as the main part of exploratory factor analysis. Table
Rotated component matrix.
Variables | Component | ||||
---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |
I1 | 0.704 | ||||
I5 | 0.636 | ||||
I23 | 0.631 | ||||
I27 | 0.606 | ||||
I30 | 0.59 | ||||
C21 | 0.712 | ||||
C25 | 0.701 | ||||
C28 | 0.673 | ||||
C32 | 0.658 | ||||
C36 | 0.65 | ||||
M11 | 0.619 | ||||
M13 | 0.604 | ||||
M16 | 0.6 | ||||
M37 | 0.549 | ||||
M38 | 0.504 | ||||
A8 | 0.575 | ||||
A14 | 0.568 | ||||
A18 | 0.556 | ||||
A31 | 0.549 | ||||
A34 | 0.482 | ||||
U12 | 0.615 | ||||
U15 | 0.606 | ||||
U24 | 0.599 | ||||
U33 | 0.552 | ||||
U35 | 0.517 |
Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. Rotation converged in 6 iterations.
According to the rotated component matrix in Table
Reliability analysis is conducted using Cronbach’s alpha coefficient for internal consistency. The results are summarised in Table
Reliability statistics.
Construct | Cronbach’s alpha | Number of items |
---|---|---|
Interest | 0.802 | 5 |
Confidence | 0.792 | 5 |
Motivation | 0.783 | 5 |
Anxiety | 0.797 | 5 |
Usefulness | 0.753 | 5 |
In Figure
Analysed conceptual path.
We observe from Table
Regression weights for the conceptualized path model.
Relationships | Estimate | SE | CR |
|
---|---|---|---|---|
Confidence ⟵ usefulness | 0.800 | 0.179 | 4.471 | 0 |
Confidence ⟵ anxiety | −0.271 | 0.086 | −3.158 | 0.002 |
Motivation ⟵ anxiety | −0.090 | 0.058 | −1.542 | 0.123 |
Motivation ⟵ usefulness | 0.587 | 0.149 | 3.952 | 0 |
Motivation ⟵ confidence | 0.372 | 0.099 | 3.773 | 0.001 |
Interest ⟵ confidence | 0.801 | 0.148 | 5.399 | 0.001 |
Interest ⟵ motivation | 0.145 | 0.136 | 1.065 | 0.287 |
SN1I ⟵ interest | 0.775 | 0.110 | 7.040 | 0.001 |
SN5I ⟵ interest | 0.803 | 0.112 | 7.162 | 0.001 |
SN23I ⟵ interest | 0.917 | 0.112 | 8.198 | 0.001 |
SN27I ⟵ interest | 0.713 | 0.115 | 6.200 | 0.001 |
SN30I ⟵ interest | 1.000 | |||
SN38M ⟵ motivation | 0.763 | 0.148 | 5.145 | 0.001 |
SN37M ⟵ motivation | 0.934 | 0.160 | 5.857 | 0.001 |
SN16M ⟵ motivation | 0.760 | 0.145 | 5.245 | 0.001 |
SN13M ⟵ motivation | −0.233 | 0.132 | −1.760 | 0.078 |
SN11M ⟵ motivation | 1.000 | |||
SN36C ⟵ confidence | 0.872 | 0.125 | 6.991 | 0.001 |
SN32C ⟵ confidence | 0.672 | 0.114 | 5.893 | 0.001 |
SN28C ⟵ confidence | 0.698 | 0.111 | 6.275 | 0.001 |
SN25C ⟵ confidence | 1.000 | |||
SN21C ⟵ confidence | 0.897 | 0.130 | 6.906 | 0.001 |
SN34A ⟵ anxiety | 0.801 | 0.138 | 5.805 | 0.001 |
SN31A ⟵ anxiety | 0.848 | 0.144 | 5.905 | 0.001 |
SN31A ⟵ anxiety | 1.000 | |||
SN14A ⟵ anxiety | 0.692 | 0.130 | 5.333 | 0.001 |
SN8A ⟵ anxiety | 0.817 | 0.136 | 5.988 | 0.001 |
SN35U ⟵ usefulness | 0.995 | 0.167 | 5.944 | 0.001 |
SN33U ⟵ usefulness | 1.000 | |||
SN24U ⟵ usefulness | 0.993 | 0.161 | 6.174 | 0.001 |
SN15U ⟵ usefulness | −0.305 | 0.162 | −1.883 | 0.060 |
SN12U ⟵ usefulness | 0.005 | 0.174 | 0.027 | 0.979 |
Several model fit indices were used to evaluate how good the conceptual model fits the collected data. The calculated values and the range of acceptable values for these indices are given in Table
Details of the fit indices are found in the following literature: for CMIN/DF, see Marsh and Hocevar [
After dropping the items that had negative correlations with their constructs, the resulting modified path model is shown in Figure
Modified path.
In Figure
Analysed modified path.
Fit indices concerning the conceptualized path model.
Fit indices | Calculated value | Acceptable range of values |
---|---|---|
CMIN/DF | 2.484 | Between 2 and 5 |
GFI | 0.843 |
|
AGFI | 0.909 |
|
CFI | 0.927 |
|
NFI | 0.822 |
|
RFI | 0.876 |
|
IFI | 0.933 |
|
TLI | 0.995 |
|
RMSEA | 0.071 |
|
We observe from Table
Regression weights for the modified path model.
Relationships | Estimate | SE | CR |
|
---|---|---|---|---|
Confidence ⟵ anxiety | −0.321 | 0.094 | −3.402 |
|
Confidence ⟵ usefulness | 0.684 | 0.133 | 5.133 |
|
Motivation ⟵ usefulness | 0.435 | 0.114 | 3.806 |
|
Motivation ⟵ confidence | 0.373 | 0.083 | 4.473 |
|
Interest ⟵ confidence | 0.677 | 0.119 | 5.681 |
|
Interest ⟵ motivation | 0.188 | 0.138 | 1.366 | 0.172 |
SN1I ⟵ interest | 1.000 | |||
SN5I ⟵ interest | 1.043 | 0.166 | 6.285 |
|
SN23I ⟵ interest | 1.191 | 0.171 | 6.948 |
|
SN27I ⟵ interest | 0.931 | 0.165 | 5.630 |
|
SN30I ⟵ interest | 1.296 | 0.185 | 7.009 |
|
SN38M ⟵ motivation | 1.000 | |||
SN37M ⟵ motivation | 1.174 | 0.228 | 5.242 |
|
SN16M ⟵ motivation | 0.988 | 0.206 | 4.788 |
|
SN11M ⟵ motivation | 1.252 | 0.241 | 5.204 |
|
SN36M ⟵ confidence | 1.000 | |||
SN32C ⟵ confidence | 0.768 | 0.103 | 7.469 |
|
SN28C ⟵ confidence | 0.797 | 0.096 | 8.281 |
|
SN25C ⟵ confidence | 1.143 | 0.163 | 7.469 |
|
SN21C ⟵ confidence | 1.020 | 0.103 | 9.877 |
|
SN34A ⟵ anxiety | 1.000 | |||
SN31A ⟵ anxiety | 1.068 | 0.202 | 5.284 |
|
SN18A ⟵ anxiety | 1.250 | 0.216 | 5.794 |
|
SN14A ⟵ anxiety | 0.858 | 0.178 | 4.812 |
|
SN8A ⟵ anxiety | 1.018 | 0.191 | 5.320 |
|
SN35U ⟵ usefulness | 1.000 | |||
SN33U ⟵ usefulness | 0.989 | 0.167 | 5.917 |
|
SN24U ⟵ usefulness | 0.999 | 0.157 | 6.379 |
|
Table
Standardized regression weights.
Estimate | CR | AVE | |
---|---|---|---|
SN1I ⟵ interest | 0.476 | 0.857 | 0.720 |
SN5I ⟵ interest | 0.489 | ||
SN23I ⟵ interest | 0.580 | ||
SN27I ⟵ interest | 0.414 | ||
SN30I ⟵ interest | 0.589 | ||
|
|||
SN38M ⟵ motivation | 0.325 | 0.846 | 0.630 |
SN37M ⟵ motivation | 0.382 | ||
SN16M ⟵ motivation | 0.331 | ||
SN11M ⟵ motivation | 0.392 | ||
|
|||
SN36C ⟵ confidence | 0.680 | 0.891 | 0.761 |
SN32C ⟵ confidence | 0.483 | ||
SN28C ⟵ confidence | 0.539 | ||
SN25C ⟵ confidence | 0.452 | ||
SN21C ⟵ confidence | 0.655 | ||
|
|||
SN34A ⟵ anxiety | 0.485 | 0.847 | 0.665 |
SN31A ⟵ anxiety | 0.503 | ||
SN18A ⟵ anxiety | 0.662 | ||
SN14A ⟵ anxiety | 0.427 | ||
SN8A ⟵ anxiety | 0.510 | ||
|
|||
SN35U ⟵ usefulness | 0.547 | 0.996 | 0.985 |
SN33U ⟵ usefulness | 0.514 | ||
SN24U ⟵ usefulness | 0.591 |
A look at the values of the goodness-of-fit indices in Table
Fit indices concerning the modified path model.
Fit indices | Calculated value | Acceptable range of values |
---|---|---|
CMIN/DF | 2 |
Between 2 and 5 |
GFI | 0 |
|
AGFI | 0 |
|
CFI | 0 |
|
NFI | 0 |
|
RFI | 0 |
|
IFI | 0 |
|
TLI | 0 |
|
RMSEA | 0 |
|
In this study, the effects of students’ confidence and motivation on their interest in mathematics are examined and having the variables, mathematics anxiety and the students’ knowledge, on the usefulness of mathematics conceptualized to have indirect effects on students’ interest. This study is conjectured on the hypotheses that confidence and motivation have influence on students’ interest and also, mathematics anxiety and students’ knowledge on mathematics’ usefulness has an indirect influence on students’ interest.
We observe from our study that confidence of a student has the biggest (0.677,
However, the effect of students’ motivation on their interest in mathematics was still not significant. Our findings disagree with the study of Musso et al. [
Confidence directly affects students’ interest in the learning of mathematics, and there is a direct relationship between confidence and motivation. Also, students’ motivation to study mathematics depends largely on their knowledge of the usefulness of mathematics. A students’ knowledge of the usefulness of mathematics also modifies his/her confidence level in pursuing mathematics. The perception on the usefulness of mathematics affects students’ confidence level. While a good knowledge of the usefulness of mathematics increases the confidence level in students, the lack of confidence among students in learning mathematics leads to anxiety among students.
The teaching of mathematics should be geared towards making students understand and see the usefulness of mathematics. Mathematics teachers should therefore introduce students to real-world application of mathematics to increase their knowledge of its usefulness that would indirectly increase their interest in mathematics by directly fuelling their confidence and motivation to learn mathematics.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.