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Despite receiving increased attention from researchers in mathematics education, there is still no comprehensive understanding of the current level of data literacy in the teaching and learning of mathematics. To address this gap, this study pre­sents a review of 247 papers selected from the Sco­pus database between 2009 and 2024. The research aims to explore the following: (i) The overall vol­ume, geographic distribution, and development tra­jectory in the literature on data literacy in mathe­matics. (ii) The researchers and research collabora­tions that have had the greatest influence on the literature on data literacy in mathematics. (iii) The sources that have had the greatest influence on the literature on data literacy in mathematics. (iv) The most important topics in the literature on data literacy in mathematics. It was discovered that the number of publications involving data literacy in mathematics increased from 2016 to 2023. Authors from the Netherlands are the most active in the literature on data literacy in mathematics. The Teacher College Record had the highest number of citations. Lastly, the most important topics addressed in the literature on data literacy in mathematics were data use, data literacy, and data-based decision-making. This study has implications not only for mathematics education researchers but also for other stakeholders in the education sector, including school principals, policymakers, and mathematics teachers.

This research explored the predictive power of individual and contextual factors on first-year students' preferences for online versus face-to-face learning environments, examined through a Bayesian framework for analyzing compositional data. Purposefully, it delves into how students' academic performance and geographic location influence their educational modality choices in a post-pandemic context. As quantitative research, the paper employs Bayesian statistical methods, the research analyzed student preferences across varied South African provinces, juxtaposing these with academic performance to uncover patterns and predictors of learning modality preferences. The student learning modalities preferences were collected through questionnaire as a compositional data. The findings revealed a significant correlation analysis which is, that students with higher academic performance and those residing in remote areas show a preference for face-to-face learning modality, challenging the notion that online learning preferences are primarily driven by academic outcomes alone. Adding a novel dimension, this investigation enriches the current understanding of educational preferences by applying a Bayesian approach, revealing that predictive analyses must account for a blend of personal and situational factors. This insight is pivotal for formulating educational policies that are both inclusive and responsive to the diverse needs of the student populace in a dynamically evolving educational terrain.

Challenges to incorporating ICT in the classroom have been debated in various literature over the last few years. It is, therefore, important to seek different strategies to mitigate these challenges. Design thinking (DT) is considered an approach that can help improve teachers’ Technological Pedagogical and Content Knowledge (TPACK). This study therefore examined teachers’ experience when developing lessons using ICT using design thinking. This qualitative study was conducted in a primary school in South Africa. Data was collected from two social science (Geography) teachers using face-to-face interviews. Highlights from the study point to the following benefits namely, teachers can learn in collaboration, they can be supported by their peers, and they take part in high-order thinking practices to solve problems related to ICT integration. Therefore, it is recommended that a design thinking approach needs to be applied when teacher training workshops for ICT integration are offered. This study contributes to the existing literature on developing strategies to incorporate ICT in the curriculum of primary schools in South Africa.

The increasing use of virtual learning environments (VLEs) in recent years has transformed teaching and learning methods. Universities now combine VLEs with traditional classrooms to accommodate hybrid teaching and learning approaches. However, student engagement with VLEs varies, and universities lack the tools to effectively determine and analyse VLE engagement. Consequently, data-driven decision-making regarding VLE usage remains a challenge for universities. This study thus proposed a user-friendly web-based application, using a R shiny framework, to determine and understand student engagement levels in VLEs. In this study, two clustering methods, K-means and Gaussian Mixture Model (GMM) were compared, to identify the most effective method for the proposed application. The results indicated that GMM outperforms K-means by generating more accurate and comprehensive groupings of student engagement levels. One key advantage of the GMM method is its ability to capture uncertainty and provide probabilities of student membership in each level of engagement, which enhances its usefulness for decision-making. Furthermore, the GMM method achieves these outcomes efficiently, saving valuable learning time. This research holds significant implications for education by providing valuable guidance for the development of Educational Data Mining (EDM) applications. Universities can leverage these applications to gain deep insights into VLE usage and enhance their understanding of student engagement. By adopting this web-based application, educators and administrators can make informed decisions and tailor interventions to optimize student learning experiences within VLEs.

A Bayesian additive regression tree (BART) is a recent statistical method that blends ensemble learning with nonparametric regression. BART is constructed using a Bayesian approach, which provides the benefit of model-based prediction uncertainty, enhancing the reliability of predictions. This study proposes the development of a BART model with a binomial likelihood to predict the percentage of students retained in tutorial classes using attendance data sourced from a South African university database. The data consist of tutorial dates and encoded (anonymized) student numbers, which play a crucial role in deriving retention variables such as cohort age, active students, and retention rates. The proposed model is evaluated and benchmarked against the random forest regressor (RFR). The proposed BART model reported an average of 20% higher predictive performance compared to RFR across six error metrics, achieving an R-squared score of 0.9414. Furthermore, the study demonstrates the utility of the highest density interval (HDI) provided by the BART model, which can help in determining the best- and worst-case scenarios for student retention rate estimates. The significance of this study extends to multiple stakeholders within the educational sector. Educational institutions, administrators, and policymakers can benefit from this study by gaining insights into how future tutorship programme student retention rates can be predicted using predictive models. Furthermore, the foresight provided by the predicted student retention rates can aid in strategic resource allocation, facilitating more informed planning and budgeting for tutorship programmes.

The advent of technology has shifted higher education students’ educational resource acquisition from traditional printed textbooks to digital formats. This shift is underscored by recent scholarship. The current study extended the discourse on student educational resource acquisition, beyond digital preference. It offers a nuanced view of strategies encompassing digital resources and traditional methods such as library and peer borrowing. The aim of this study was to investigate non-purchasing behaviours of university students in acquiring study material. An online survey link was sent through email to all registered students in the university, out of which a total of 1500 participated – this sample included all students from the four schools. The survey sought to answer the following questions: How do you access books? Besides buying, what other strategies have you employed to access books? Data were analysed using Dirichlet allocation for topic modelling to extract the topics from participants' qualitative responses. The study’s findings reveal a complex interplay of socio-economic factors that influence student behaviour. Digital downloads emerged as a prominent strategy, indicating a reliance on online resources. However, borrowing from libraries and peers was significant, which highlights the role of social learning communities. This study contributes to the understanding of how the cost of textbooks impacts academic choices and success, emphasising the social justice implications of textbook affordability. The rise of alternative media suggests a need to reimagine educational resources. It potentially reflects a generational shift in media consumption. This study’s insights advocate for educational institutions and policymakers to reassess resource provisioning to align with the evolving landscape of higher education students’ academic resource acquisition. This will ensure equitable access to educational material.

This paper analyses Sol Plaatje University's (SPU) progress on increased research activities. The paper employs bibliometric analysis review method to demonstrate the university's transition from being a predominantly teaching-focused to a more research-oriented institution. A novel, data-driven methodology is also adopted in this paper, to identify and examine SPU’s research niche through publications. This paper’s data were collected from the Scopus and Web of Science databases. The paper’s findings reveal that there was an overall significant increase in research outputs, observable on yearly basis for a decade (2014 to 2023). The yearly increase in research output is recorded from diverse research fields, including machine learning, cryptography, environmental research, and public health. Findings further reveal that SPU has built its international research collaborations within the African and European continents. This paper’s findings contribute to literature on higher education development by offering insights into how newly established universities can transition from a teaching-centric focus to becoming research-active. This paper revealed the importance of strategic planning, interdisciplinary research, and international collaboration in the development of a vibrant research environment.

In an era marked by increasing geopolitical strife and societal challenges, the imperative role of education in bridging divides and fostering understanding is unparalleled. This special issue aims to delve into the transformative potential of integrating data analytics within the scholarship of teaching and learning (SoTL), and in crafting a curriculum that transcends borders. Such an endeavour is crucial in effectively navigating and positively contributing to our complex, troubled world. Throughout this issue, we shift our lens to the transformative role of data analytics in reshaping SoTL. The collection of papers in this special issue presents a rich discourse on how data-driven insights can revolutionise curriculum design, teaching methodologies, and student engagement. These insights pave the way for more effective, personalised learning experiences, adeptly responding to the unique needs of learners in a digitally interconnected world.

In this empirical paper, we investigate the influence that print media has on preservice teachers' construction of their teacher professional identity. Preservice teachers are a vulnerable group still undergoing training to shape their teacher identity; it is, therefore, essential to see how the stories told in the print media influence them. This paper is couched in the Social Learning Theory because it focuses on learning through interacting with social and environmental factors. This qualitative paper used focus group discussions with five purposefully sampled third-year education preservice teachers from a South African university. Data generated were thematically analysed. The findings revealed that media created multiple learning opportunities through reflection and engagement. In light of the findings, the study suggests using teacher education programs to deepen preservice teachers' sense of reflection and meaning-making beyond the information they might come across.

Euclidean geometry provides an opportunity for learners to learn argumentation and develop inductive and deductive reasoning. Despite the significance of Euclidean geometry for developing these skills, learner performance in mathematics, particularly geometry, remains a concern in many countries. Thus, the current study examined the nature of learners’ errors in Euclidean geometry problem-solving, particularly regarding the theorem for angle at the centre and its applications. Van Heile’s theory of geometric thinking and teacher knowledge of error analysis were used as conceptual frameworks to make sense of the nature of learners’ errors and misconceptions. Using a participatory action research approach, the study was operationalised by five mathematics teachers from four secondary schools in Motheo district in the Free State Province of South Africa and three academics from two local universities. The study analysed 50 sampled midyear examination scripts of Grade 12 learners from four schools. The findings of this study revealed that most learner errors resulted from concepts on Van Heile’s operating Levels 0 and 1, while the questions mainly required Level 3 thinking. The study recommends that teachers determine their learners’ level of geometric thinking and integrate this knowledge in their lesson preparations and material development.

This paper analyses Sol Plaatje University's (SPU) progress on increased research activities. The paper employs bibliometric analysis review method to demonstrate the university's transition from being a predominantly teaching-focused to a more research-oriented institution. A novel, data-driven methodology is also adopted in this paper, to identify and examine SPU's research niche through publications. This paper's data were collected from the Scopus and Web of Science databases. The paper's findings reveal that there was an overall significant increase in research outputs, observable on yearly basis for a decade (2014 to 2023). The yearly increase in research output is recorded from diverse research fields, including machine learning, cryptography, environmental research, and public health. Findings further reveal that SPU has built its international research collaborations within the African and European continents. This paper's findings contribute to literature on higher education development by offering insights into how newly established universities can transition from a teaching-centric focus to becoming research-active. This paper revealed the importance of strategic planning, interdisciplinary research, and international collaboration in the development of a vibrant research environment.

While the importance of incorporating technology in teaching and learning is acknowledged by several scholars, there are still serious challenges in using technology to enhance the teaching and learning of science and mathematics. In this quantitative study, the technological, pedagogical content knowledge (TPACK) framework was used to analyse factors that serve as predictors for pre-service mathematics and science teachers to use technology effectively in their teaching. The sample for this study consisted of 416 final year Bachelor of Education students from two South African universities. The sample included 243 (54%) females and 175 (42%) males. Questionnaires were used to collect data. The results of the study show that the content knowledge (CK), pedagogical knowledge (PK), pedagogical content knowledge (PCK), technological content knowledge (TCK) and technological pedagogical knowledge (TPK) were the significant predictors for pre-service teachers to incorporate technology in their mathematics and science classrooms.

The utilization of learning analytics to identify at-risk students for early intervention has exhibited promising results. However, most predictive models utilized to address this issue have been based on non-probabilistic machine learning models. In response, this study incorporated probabilistic machine learning for two reasons: (1) to facilitate the inclusion of domain knowledge, and (2) to enable the quantification of uncertainty in model parameters and predictions. The study developed a five-stage, probabilistic logistic regression model to identify at-risk students at different stages throughout the academic calendar. Rather than predicting a student’s final or exam mark, the model was focused on predicting the at-risk probabilities for subsequent assessments—specifically, the probability of a student failing an upcoming assessment. The model incorporated student engagement data from Moodle, as well as demographic and student performance data. The study’s findings indicate that the significance and certainty of student engagement and demographic variables decreased after incorporating student-performance variables, such as assignments and tests. The most effective week for identifying at-risk students was found to be week 6, when the accuracy was 92.81%. Furthermore, the average level of uncertainty exhibited by the models decreased by 60% from stage 3 to 5, indicating more reliable predictions at later than earlier stages. The study highlights the potential of a probabilistic machine learning model to aid instructors and practitioners in identifying at-risk students, and thereby to enhance academic outcomes.

Euclidean geometry provides an opportunity for learners to learn argumentation and develop inductive and deductive reasoning. Despite the significance of Euclidean geometry for developing these skills, learner performance in mathematics, particularly geometry, remains a concern in many countries. Thus, the current study examined the nature of learners' errors in Euclidean geometry problem-solving, particularly regarding the theorem for angle at the centre and its applications. Van Heile's theory of geometric thinking and teacher knowledge of error analysis were used as conceptual frameworks to make sense of the nature of learners' errors and misconceptions. Using a participatory action research approach, the study was operationalised by five mathematics teachers from four secondary schools in Motheo district in the Free State Province of South Africa and three academics from two local universities. The study analysed 50 sampled midyear examination scripts of Grade 12 learners from four schools. The findings of this study revealed that most learner errors resulted from concepts on Van Heile's operating Levels 0 and 1, while the questions mainly required Level 3 thinking. The study recommends that teachers determine their learners' level of geometric thinking and integrate this knowledge in their lesson preparations and material development.