Mathematics achievement is core to South Africa’s readiness for digital innovation, yet current pass rates in this subject are below the global average. Simply attributing mathematics performance to intelligence does not fully account for the multifaceted reality of achievement in the subject. The current study investigated the value of both cognitive and behavioural factors in predicting mathematics performance, as well as explored the interactions between these factors. A quantitative, cross-sectional design was employed. Grade 9 learners (^{2} = 0.390,

This study proposes that educators and parents should support curriculum change that encourages positive attitudes towards mathematics and create supportive environments conducive to effective learning, rather than blaming a lack of cognitive potential for the disappointing mathematics pass rate.

It is essential to empower young people with the competence to achieve in mathematics, especially if South Africa wants to position itself as a leader in areas such as artificial intelligence, robotics, genetics, and digital innovations (Baller et al.,

Current realities, however, do not align with these visionary goals as the quality of South African mathematics education is on par with that associated with a low-income country, rather than that of a middle-income nation (Van der Berg et al.,

Attributing mathematics performance to a single factor, such as higher innate intelligence or teaching efficiency, neither motivates nor encourages learners to exhibit any effort nor reflects the multifaceted complexity of mathematical learning (Harris,

It has been established that cognitive ability, reflective of a person’s intellectual potential, is a key determinant of mathematics performance (Abin et al.,

However, Piaget’s stages have been countered by studies that found that cognitive development is a constant acquisition and modification of thought process throughout childhood and adolescence (Bjorklund,

Given the diverse context of South Africa – with multilingualism and inequalities in education opportunities – assessing intelligence fairly is often challenging. Crystallised intelligence can be acquired and learnt, and is therefore influenced by environmental, cultural and social factors (Brown,

Non-cognitive aspects, such as planning and organisation abilities, self-discipline, self-concept, learning routines and habits, stress management, test anxiety and motivation have consistently been found to have an impact on academic performance (Wehner & Schils,

The overall aim of the present study is to determine the predictive value of fluid intelligence and study orientations in a South African Grade 9 sample. In determining each factor’s value, the study is answering the research question of whether mathematics performance can largely be attributed to fluid intelligence, or whether behavioural influences, such as study orientations, also impact observed mathematics performance. These results have theoretical implications for future studies across the country as well as internationally, and also allow for practical suggestions to be shared within the education communities and possibly support curriculum change, allowing for a more focused approach to this national concern.

Following from this aim, the key objectives of this study are to:

Determine the relative dominance weighting of fluid intelligence and each study orientation factor in predicting mathematics performance.

Evaluate the moderating interactions between fluid intelligence and each study orientation factor in predicting mathematics performance.

Objective two was further investigated by a number of hypotheses:

_{O}1:

_{A}1:

_{O}2:

_{A}2:

_{O}3:

_{A}3:

_{O}4:

_{A}4:

_{O}5:

_{A}5:

A non-experimental, quantitative cross-sectional research design was employed to collect data from Grade 9 learners between August and October 2022. Grade 9 learners were targeted since they are in their final year of Senior Phase and at the point of deciding whether to continue pursuing Mathematics or Mathematics Literacy. By the end of the Grade 9 school year, learners should have also demonstrated competence in a variety of mathematical concepts (Department of Basic Education [DBE],

Given the analyses performed, G*Power v3.1 (Faul et al.,

Consequently, upon receiving parental consent, 187 Grade 9 learners registered with these schools provided informed assent and completed both questionnaires for this study. All learners indicated their gender, with girls constituting 60.4% of the sample. The majority of the sample indicated their ethnicity as Black African (47.1%), followed by White (15.5%), Indian/Asian (8.6%) and Coloured (5.3%), fairly representative of the ethnic profile of Gauteng (StatsSA,

Since it was a cross-sectional design, each learner was only assessed once, at a time suitable to them or agreed upon with the school. Upon completion of the questionnaires, participants received an interpretive learner insights report, providing them with development tips based on their cognitive and study orientations profile. The majority of learners also received group feedback to guide their interpretation of these insight reports, and the opportunity for individual feedback was communicated.

To assess fluid intelligence, the South African, electronic version of the Raven’s Standard Progressive Matrices (SPM) was administered. The non-verbal nature of the questions provides users with a culturally fair, relatively language-free gauge of the participant’s fluid intelligence and abstract thinking ability, making it more applicable for our diverse, multilingual South African learner population (Taylor,

The Study Orientation Questionnaire in Mathematics (SOM) is a 76-item South African-developed assessment written in English for learners from Grades 7 to 12. The assessment measures study attitude (14 items), mathematics anxiety (14 items), study habits (17 items), problem-solving (18 items), study milieu (13 items), and information processing (16 items – only for Grades 10, 11 and 12) (Maree et al.,

The analyses on the data set of 187 learners were performed using Jamovi version 2.2.5 (The Jamovi Project,

Pearson correlation coefficients were calculated using the

To achieve the first objective of this study, of determining whether fluid intelligence and study orientations predict mathematics performance, a linear regression was conducted. Fluid intelligence and each of the study orientation factors were added into the linear regression model as independent predictor variables. Additionally, dominance analysis was used to assess the relative importance of each of these predictor variables in explaining variance in mathematics performance (Braun et al.,

The second objective of this study was to explore the interaction between cognitive (fluid intelligence) and behavioural (study orientations) factors. Moderation analysis examines how a relationship between a predictor and outcome variable is influenced by a third variable, known as the moderator. The results of such analysis can determine whether the relationship between predictor and outcome variables weakens, strengthens, or exists at all in the presence of the moderating variable (Hair et al.,

Prior to any interaction with learners, ethical clearance from the Research Ethics Committee from the University of Pretoria (HUM035/0721) and permission from the Gauteng Department of Education were obtained. Thereafter, principals in the Gauteng region were contacted and interested schools then assisted the researchers in communicating the purpose of the study and the voluntary nature of participating to learners and parents. Learners wanting to participate communicated their interest to their teachers or the researchers directly, and suitable times for test administration were allocated. Physically signed copies of both parental consent as well as learner assent were required before learners could complete the questionnaires. All questionnaires were completed electronically under the supervision of the researcher, which minimised the risk of checking peers’ answers or incorrect data capturing. The researcher and the assessment provider have a legal obligation to keep the collected information for a period of 7 years, in line with the Health Professions Council of South Africa’s guidelines.

Correlations between mathematics marks, fluid intelligence, and study orientation factors.

Variable | Mathematics mark | SA | MA | PSB | SH | SM | |
---|---|---|---|---|---|---|---|

0.39 |
- | - | - | - | - | - | |

SA | 0.51 |
0.27 |
- | - | - | - | - |

MA | -0.36 |
-0.12 | -0.25 |
- | - | - | - |

PSB | 0.47 |
0.29 |
0.75 |
−0.19 |
- | - | - |

SH | 0.46 |
0.23 |
0.76 |
−0.20 |
0.79 |
- | - |

SM | 0.41 |
0.29 |
0.49 |
−0.46 |
0.36 |
0.44 |
- |

M | 57.00 | 42.30 | 38.00 | 15.20 | 46.20 | 39.90 | 42.0 |

SD | 6.00 | 7.40 | 9.10 | 8.80 | 11.60 | 11.50 | 6.8 |

,

,

,

Study attitude reflected a statistically significant, strong, positive relationship with mathematics marks (

Given the only negative statistically significant moderate correlation, between mathematics anxiety and mathematics marks (

The relationship between study habits and mathematics marks is statistically significant, moderate, and positive (

Problem-solving behaviour displayed a statistically significant, moderate, positive relationship with mathematics marks (

Study milieu also had statistically significant positive correlations with both mathematics marks (

^{2} = 0.390,

Linear regression and predictor ranking on mathematic performance.

Predictor | Estimate | Standard error of the estimate | 95% confidence interval |
Standardised dominance statistic | Rank | |||
---|---|---|---|---|---|---|---|---|

Lower | Upper | |||||||

Intercept | 18.853 | 8.497 | 2.086 | 35.621 | 2.219 | 0.028 | - | - |

0.512 | 0.135 | 0.245 | 0.778 | 3.789 | < 0.001 | 0.193 | 2 | |

SA | 0.378 | 0.177 | 0.030 | 0.727 | 2.141 | 0.034 | 0.217 | 1 |

MA | −0.375 | 0.120 | −0.612 | −0.138 | −3.123 | 0.002 | 0.150 | 5 |

SH | 0.137 | 0.146 | −0.151 | 0.425 | 0.941 | 0.348 | 0.155 | 4 |

PSB | 0.135 | 0.144 | −0.149 | 0.420 | 0.938 | 0.350 | 0.163 | 3 |

SM | 0.144 | 0.176 | −0.203 | 0.491 | 0.821 | 0.412 | 0.122 | 6 |

In considering the dominance of these predictors, study attitude is seen to be the most dominant predictor in the model, contributing 21.7% towards the total variance explained. Fluid intelligence is ranked as the second-most dominant predictor, with a contribution of 19.3% towards the total variance explained. While mathematics anxiety is ranked as the second-lowest dominant predictor out of the six variables, it is the only other statistically significant predictor, contributing 15.0% towards the total variance explained.

Direct effects and moderation models: Fluid intelligence and study orientations.

Predictor | Estimate | Standard error of the estimate | 95% confidence interval |
|||
---|---|---|---|---|---|---|

Lower | Upper | |||||

0.596 | 0.131 | 0.340 | 0.852 | 4.563 | < 0.001 | |

SA | 0.773 | 0.106 | 0.566 | 0.981 | 7.312 | < 0.001 |

0.010 | 0.015 | −0.020 | 0.040 | 0.662 | 0.508 | |

0.757 | 0.138 | 0.488 | 1.027 | 5.502 | < 0.001 | |

MA | −0.568 | 0.116 | −0.795 | −0.341 | −4.904 | < 0.001 |

−0.003 | 0.016 | −0.034 | 0.029 | −0.159 | 0.874 | |

0.633 | 0.136 | 0.366 | 0.899 | 4.653 | < 0.001 | |

SH | 0.543 | 0.085 | 0.377 | 0.709 | 6.409 | < 0.001 |

−0.005 | 0.011 | −0.027 | 0.017 | −0.425 | 0.671 | |

0.602 | 0.141 | 0.326 | 0.878 | 4.270 | < 0.001 | |

PSB | 0.538 | 0.086 | 0.369 | 0.707 | 6.242 | < 0.001 |

0.002 | 0.011 | −0.020 | 0.025 | 0.198 | 0.843 | |

0.703 | 0.139 | 0.431 | 0.976 | 5.060 | < 0.001 | |

SM | 0.822 | 0.146 | 0.535 | 1.109 | 5.620 | < 0.001 |

0.044 | 0.017 | 0.011 | 0.077 | 2.600 | 0.009 |

From

Considering moderated relationships, however, only study milieu is seen to have a significant interaction effect with fluid intelligence (_{O}1, H_{O}2, H_{O}3, and H_{O}4. However, the results support a rejection of the null hypothesis H_{O}5, in favour of the alternative hypothesis, H_{A}5.

Simple slope analysis: Fluid intelligence and study milieu interaction effect.

Variable | Estimate | Standard error of the estimate | 95% confidence interval |
|||
---|---|---|---|---|---|---|

Lower | Upper | |||||

Average | 0.703 | 0.141 | 0.428 | 0.979 | 5.000 | < 0.001 |

Low (-1SD) | 0.405 | 0.160 | 0.091 | 0.720 | 2.530 | 0.010 |

High (+1SD) | 1.001 | 0.201 | 0.606 | 1.396 | 4.970 | < 0.001 |

SD, standard deviation.

From

The relationships between both fluid intelligence and study orientations, and mathematics marks suggested that both cognitive and behavioural factors influence mathematics performance in Grade 9 learners. The relationship between fluid intelligence and mathematics performance was expected and replicated a number of previous studies (Brandt & Lechner,

The linear regression indicates that fluid intelligence, study attitude, and mathematics anxiety are statistically significant predictors of mathematics performance. Additionally, study attitude was found to be the most dominant predictor, followed by fluid intelligence. Despite mathematics anxiety being a significant predictor, it was not ranked as a dominant predictor. These findings contradict Erasmus (

Finally, the moderation models shed additional light on the interactions between fluid intelligence and study orientations, in a way that the correlations could not do. Despite the significant relationships between the factors, it was found that, with the exception of study milieu, study orientation does not moderate the effect of fluid intelligence on mathematics performance. Instead, study orientation independently and directly predicts mathematics performance. The implications of these findings are significant, in that they indicate that mathematics achievement is not reliant on fluid intelligence alone. A learner that has a positive study attitude, is confident in their mathematics abilities (low mathematics anxiety), consistently follows through on their effective study practices, and reflects on their problem-solving style is as able to achieve a mathematics pass as a learner with higher fluid intelligence. In considering the significant interaction effect between study milieu and fluid intelligence, it should also be noted that each factor also independently predicts mathematics performance. In saying this, a learner who may not inherently be higher on fluid intelligence may benefit more from a supporting learning environment. However, the findings also express that all learners’ mathematics performance may be enhanced with a supporting learning environment.

One of the key strengths of this study is that it gives practical guidance to the education system on what to focus on to improve mathematics performance in the country. The study was able to evaluate the role of both intelligence and behaviour in predicting mathematics performance. The results are able to spread hope to those who are not inherently able to deal with abstract concepts, such as those commonly discussed in mathematics. By actively and consistently working at improving one’s mathematics knowledge, one is able to develop a more positive, confident attitude towards the subject. The findings can also be preliminarily used to advocate for changes to the curriculum to make it more practical and engaging for learners. The results also suggest that the value of supportive learning environments should not be overlooked, and educators should be held to such standards that they are able to provide such support to learners.

However, the study is not without limitations. The study was limited to a relatively small, Quintile 5 sample of Grade 9 learners in the Gauteng province, who completed the study during Term 3 of the academic year when fatigue has set in. Having only a single indication of a learners’ mathematics achievement and study orientation, while cost-effective, is not ideal. Noting the number of relationships between variables, there are still unanswered questions relating to the stability of study orientations over an academic year, when it is expected that a learner’s mathematics performance does fluctuate somewhat. While it is noted that Term 2 mathematics marks were requested, some learners may have had subsequent mathematics tests post their mid-year examinations, and it cannot be said with certainty that they responded to the questionnaires with their Term 2 performance in mind. Additionally, the study primarily relies on self-report measures for study orientations. Self-report measures can introduce bias, as participants may provide responses they believe are socially desirable or may not accurately reflect their behaviours. Furthermore, examining the mediating role of these variables is also an aspect that has not been explored at all for the current study, but can add an additional layer of interpretation and understanding of the interaction between these constructs.

To enhance the generalisability of findings to advocate for curriculum change and psychometric profiling within schools, while also providing context-specific recommendations where possible, it is recommended that future research encompasses a more diverse and representative participant pool. Additionally, given the reliance on self-report measures for study orientations in the current study, future research should explore alternative assessment methods, such as parent and teacher ratings, to mitigate potential biases. It is also recommended that a longitudinal study, across a number of years, at regular intervals within an academic year, be conducted to comprehensively identify at which stage of the learners’ scholastic career study attitudes become more negative, or when mathematics anxiety starts crippling performance. Such a longitudinal study can also provide insights to enable educators and parents to actively manage negative study orientations before they have long-term negative implications on mathematics performance. In this light, research that includes a pre- and post-intervention assessment of study orientations, for a more pointed approach towards the factors that have the greatest impact on mathematics performance, beyond the study milieu, is also valuable. Additional studies could also explore specific aspects of milieu, and include teacher attitudes, parent socioeconomic status, and cultural influences.

The current study reiterated that mathematics performance cannot be solely attributed to cognitive abilities. This study concludes with the proposal that a holistic approach to mathematics achievement is needed. The change needs to start at a curriculum level, to make the subject more practical and engaging. Furthermore, educators need to be trained to provide a safe, judgement-free environment that is not only conducive to learning, but that develops a learner’s resilience towards mathematics. Educators and institutions should not only focus on academic content but also consider and address the psychological and environmental factors that impact learners’ mathematics performance. By creating supportive study environments, parents and teachers should focus on encouraging realistic, yet challenging study habits that learners can gain comfort in following through. Continuous practice not only will reduce nervousness over time, but will also build confidence and a positive attitude towards this essential skill. As learners practise more, thereby implementing their routine study habits, they will also likely become more comfortable with identifying which strategies need to be used with which types of mathematics problems and, in doing so, build their problem-solving behaviours. Implementing targeted interventions and creating a positive, supportive learning environment can contribute significantly to improved mathematics performance for Grade 9 learners, during a time when they are particularly vulnerable as they make subject-choice decisions that will have long-lasting implications not only for their future careers, but for the country at large.

The authors wish to thank Prof. Kobus Maree for his support throughout this study and use of the Study Orientations towards Mathematics, developed by him and distributed by JVR Psychometrics.

This article is partially based on P.R.’s thesis entitled ‘Identifying psychological factors that improve mathematics achievement in Grade 9 pupils from Gauteng’ towards the degree of Doctor of Philosophy in Psychology, University of Pretoria on 04 September 2023, with advisor Dr Benny Motileng available at:

P.R. is employed by JVR Psychometrics, the official South African suppliers of the psychometric assessments used in the current study.

This article is an output from P.R.’s doctoral thesis, which B.M. supervised.

The anonymised data set is available on request, and is in the University of Pretoria’s archives.

The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.