Abstract
Background: Universities in South Africa continue to face persistent challenges in improving student retention. Existing support interventions are often fragmented and reactive, lacking timely mechanisms to identify and assist at-risk students.
Objectives: This study proposes a Big Data framework to support the analysis of educational data for early risk identification and targeted intervention, thereby enhancing student retention in South African universities.
Method: The study adopted an interpretivist qualitative approach, supported by Design Science, integrating qualitative data from interviews with 30 academic staff members across four public universities, along with the analysis of existing institutional retention reports, policy documents, and strategic plans documentation.
Results: Findings of this study revealed fragmented data systems, limited data literacy among staff, and concerns regarding the ethical use of data. Participants expressed the need for predictive analytics, integrated data systems, and personalised support mechanisms as key enablers of improved retention strategies.
Conclusion: The study concludes that Big Data analytics can play a critical role in improving student retention rates in public higher education institutions in South Africa. The findings demonstrate that when institutions integrate diverse student datasets, apply predictive modelling, and use real-time monitoring tools, they are better positioned to identify students at risk of dropping out and implement timely, evidence-based interventions.
Contribution: The proposed framework enables institutions to shift from reactive to proactive student retention management through ethical data governance, integrated data processing, predictive analytics, and personalised interventions.
Keywords: Student retention; Big data analytics; higher education; educational data analytics; learning management systems; predictive analytics; data governance; student success.
Introduction
Public higher education institutions in South Africa continue to grapple with low student retention and throughput rates, a challenge that disproportionately affects students from historically disadvantaged backgrounds (Chiramba & Ndofirepi 2023). Although reforms aimed at widening access to education post-apartheid, the higher education sector still suffers systemic inequalities, manifesting in underpreparedness for university-level study by first-time entering students, limited financial resources, and insufficient academic and psychosocial support (Department of Higher Education and Training [DHET] 2021a). Findings by the Council on Higher Education (CHE) indicate that only about 27% of students are able to complete their degrees within the minimum time, and at least 55% fail to complete at all. Black African students account for the lowest completion rates (CHE 2021). These outcomes underscore deep-rooted disparities in educational readiness and institutional responsiveness.
Although universities have implemented interventions such as extended curriculum programmes, National Student Financial Aid Scheme (NSFAS) support, and academic development services (Makura, Skead & Nhundu 2011; Manik & Ramrathan 2021), these efforts are often fragmented and not systematically linked to the timely identification of student risk (Cele 2021). In this regard, Big Data, with its predictive analytics capabilities, offers universities an important opportunity. Through the use of academic, financial, and engagement data to identify at-risk students earlier, these institutions can move from delayed support for students to a more proactive and targeted intervention. Although Big Data analytics provides the wider infrastructure for integrating and analysing diverse student datasets, predictive analytics is particularly important because it enables early warning, risk profiling, and more responsive decision-making for student retention (Cele 2021).
The world over, higher education institutions are using predictive analytics to strengthen student success strategies (Hollingworth et al. 2012; Umezuruike & Ngugi 2020; Villegas-Ch, Palacios-Pacheco & Luján-Mora 2020). However, in South Africa, adoption lags and is often constrained by fragmented systems, weak data integration, and governance concerns (Cele 2021). This study, therefore, asks and answers the following question: How can the use of Big Data analytics foster increased learner retention rates in public higher education institutions in South Africa? The study aimed to develop a Big Data framework that supports predictive identification of risk, personalised intervention, and broader institutional improvement in student retention.
Literature review
Student retention remains a major concern in South African higher education, where expanded access has not translated into equivalent gains in student success. Enrolment rates have increased since 1994 to date, but throughput and completion rates remain weak, with many students failing to complete within the minimum time and others dropping out before graduation (CHE 2022a; DHET 2021, 2024). This challenge can, to a larger extent, be closely linked to structural inequality, uneven schooling quality, financial hardship, and differences in institutional capacity, particularly for students from historically disadvantaged and rural backgrounds (Hlongwane & Langa 2020; Kemp & Bush 2018; Mokhele 2021; Mouton & Bezuidenhout 2019). This literature shows that the central issue in South Africa is not only access to higher education, but also success within it.
While traditional retention interventions, such as first-year experience programmes, tutoring, mentoring, psychosocial support, and financial aid, remain important in improving student persistence, these interventions are often fragmented, labour-intensive, and insufficiently responsive to emerging risk (Manik & Ramrathan 2021; Tinto 1993). Cele (2021) finds that many institutions in South Africa still identify vulnerable students too late for effective intervention, despite the growing availability of student data through learning management systems (LMS), student information systems and administrative platforms.
Within this context, Big Data analytics has emerged as a promising means of strengthening student retention. Defined by volume, velocity, and variety, Big Data enables institutions to collect, analyse, and act on student-level data in real time (Madamshetty, Suresh & Naidu 2019; Philip Chen & Zhang 2014). When applied to education, Big Data can support predictive modelling, risk profiling, and personalised interventions, shifting retention strategies from reactive to proactive (Baker & Siemens 2014; Bichsel 2012). International and local studies suggest that Big Data analytics can support early warning, predictive modelling, risk profiling, and more targeted interventions by drawing on academic, behavioural, and financial data (Baker & Siemens 2014; Bichsel 2012; Daniel 2015; Kumar 2018). Yet the South African literature shows that adoption remains limited by fragmented data systems, weak interoperability, low data literacy, infrastructure constraints, and ethical and regulatory concerns, including compliance with the Protection of Personal Information Act (POPIA) (Cele 2021; Matto 2022). Existing frameworks also tend to emphasise technical processes while paying insufficient attention to governance, organisational readiness, and institutional context. This creates a need for a more context-sensitive framework capable of integrating diverse data sources, supporting timely intervention, and responding to the socio-technical realities of South African universities.
Theoretical framework
This study is underpinned by two complementary theoretical models: Tinto’s Student Integration Model (Tinto 1993) and the Technology–Organisation–Environment (TOE) Framework (Tornatzky & Fleischer 1990). Together, these frameworks offer a dual-perspective approach, addressing the individual student experience and the institutional capacity for adopting Big Data analytics to support student retention. Tinto’s (1993) Model emphasises that student persistence in higher education is primarily influenced by the degree to which students are academically and socially integrated into the institution. This model is especially relevant in the South African context, where high dropout rates persist, particularly among students from disadvantaged backgrounds. Students often struggle to adapt to universities’ academic demands and social environments, making integration a key determinant of retention.
In parallel, the TOE (Tornatzky & Fleischer 1990), provides a structural lens for understanding how institutions adopt and implement new technologies. It considers three critical dimensions, which are: technological readiness, organisational context, and external environmental factors such as policy, regulation, and market forces. This framework helps to assess the institutional conditions that enable or hinder the effective adoption of Big Data analytics in higher education (Baig, Shuib & Yadegaridehkordi 2021; Park & Kim 2021; Salleh, Janczewski & Beltran 2015). Individually, each model offers valuable insights, but when integrated, they provide a holistic foundation for this study. Tinto’s model captures the student-level dynamics essential to understanding retention, while the TOE framework assesses the institutional readiness and system-level enablers or barriers to implementing analytics-based interventions. Matto (2022) suggests that existing educational data analysis frameworks often fall short, particularly in developing countries, as they fail to account for contextual challenges, including fragmented infrastructures, low data literacy, and a restrictive policy environment. To address these gaps, this study proposes a comprehensive and context-sensitive framework that draws on both the Tinto Model and the TOE framework, enabling institutions to identify at-risk students and deploy personalised interventions to improve student retention.
Research methods and design
This research employed an interpretivist qualitative approach, supported by Design Science Research (DSR) methodology. Design Science Research was deemed suitable because the goal was not only to comprehend the problem identified in the study but also to design, demonstrate, evaluate, and refine a framework as an artefact for institutional implementation (Hevner et al. 2004; Peffers et al. 2008). The research problem stemmed from a comprehensive literature review, document analysis, and interviews. An outcome of the research emerged from the thematic analysis of data collected through interviews and institutional policy documents, strategic plans, and reports.
Thirty interviews were conducted at four South African universities: Fort Hare University, Walter Sisulu University, the University of South Africa, and Rhodes University. The participants consisted of academic personnel, student support professionals, and key institutional stakeholders focused on student success. Thematic analysis was used to analyse the collected data. Triangulation among interviews, documents, and existing literature informed the design of the framework. Finally, expert review represented a formal stage in the DSR evaluation, enhancing the relevance, clarity, and practical applicability of the developed framework.
Ethical considerations
Ethical clearance to conduct this study was obtained from the Inter-Faculty Human Research Ethics Committee of the University of Fort Hare (IFHREC) (Ref. No. PID011SMAI01). Participants provided informed consent in compliance with ethical research protocols.
Results
Across the data analysed in the study, several interrelated themes emerged. These include student vulnerability and attrition risk, fragmented institutional support, limited analytics readiness by university institutions, and ethical and governance concerns. These themes were triangulated against documentary and literature sources. These themes are discussed in detail in the sections that follow.
Vulnerability and attrition risk
This study’s findings suggest that under this theme, student retention results from a combination of factors. These factors are academic, financial, psychological, and social, rather than simply based on academic performance. It engaged participants associating dropout of students with financial difficulties, inadequate preparedness and mental health challenges. One participant (Participant 4, an academic) said that students ‘face immense pressure, yet the support systems in place are either inaccessible or insufficient’, and Participant 7, a student assistance in the Teaching and Learning Department (TLC) said ‘first-year students often feel out of place, making it hard for them to engage with campus life fully’. These views highlight the complexity of retention as a multifaceted problem in the school that cannot be solved by an academic mechanism alone.
Fragmented institutional support
The second theme focused on the reactive and fragmented responses of current institutions. While participants acknowledged tutoring, mentoring, financial assistance, and counselling as beneficial resources, they also highlighted overstretched support mechanisms and inconsistent service provision across different institutions. As one participant articulated:
‘Limited resources prevent us from providing the level of support that students genuinely require’. (Participant 2, Learning Assistant)
Another commented that:
‘analysing enrolment and student engagement data with their course helps us pinpoint problem areas’. (Member 2, Academic)
This implies that institutions recognise the importance of data but have yet to implement it systematically. Collectively, these views suggest that the challenge lies not only in the availability of support but also in ensuring coordinated efforts with timely risk identification.
Limited analytics readiness
A third theme emphasised barriers – both institutional and technological – to utilising analytics for improving retention rates. Participants pointed out issues such as poor interoperability, siloed data systems, limited analytical capacity, and inconsistent collaboration between departments. Nevertheless, some organisations had started employing local tools to identify at-risk students. One participant shared that:
‘we utilise systems like the Directorate of Learning and Teaching tracking system to identify at-risk students and propose intervention strategies.’ (Participant 3, Academic)
Another highlighted the significance of early detection by stating that:
‘monitoring academic progress over time enables us to spot at-risk students before they reach a point of no return.’ (Participant 9, Male Academic)
These findings indicate that the core issue is not a lack of relevant data but rather an absence of integrated systems and processes designed to convert such data into reliable early warning signals and intervention strategies.
Ethical and governance concerns
The fourth theme addressed ethics, fairness, and governance regarding student data usage. While participants supported leveraging analytics to enhance retention rates, they expressed concerns about privacy issues, consent requirements, transparency standards, and potential biases of these systems. One participant warned against using data in ways that could disadvantage any group:
‘Data should be employed equitably across all students; we must avoid reinforcing biases leading to unjust treatment.’ (Participant 9, Male Academic)
Another added that:
‘the Protection of Personal Information (POPI) Act should govern how we manage and safeguard student behavioural information.’ (Participant 3, xxx)
These insights illustrate that any analytics-driven retention framework must rest not only on technological capabilities but also on robust governance structures and ethical protections.
These findings indicate that understanding student retention within South African higher education involves both considerations related to student success as well as challenges pertaining to institutional data capabilities.
Discussion
The research results reveal that student retention in South African universities is influenced by a complex interplay of academic, financial, social, and institutional elements. Analysis of interviews and documentary evidence indicates that students face a heightened risk of dropping out because of issues such as lack of preparedness, financial difficulties, insufficient engagement, and inadequate institutional support. These results underscore that retention is not merely a single-factor issue but a multifaceted challenge that requires coordinated institutional approaches.
The study further discovered that existing institutional responses tend to be reactive, disjointed, and poorly coordinated. Although participants recognised tutoring, mentoring, counselling, financial assistance, and student support services as crucial interventions, these were often reported as inconsistently applied or disconnected from early identification processes. Concurrently, participants highlighted the potential usefulness of data on academic performance, LMS activity, attendance records, financial aid status, and engagement metrics for identifying at-risk students sooner. However, this information frequently resides in separate systems and is not consistently acted upon in a timely manner.
In addition, it was found that obstacles to analytics-driven retention strategies are not solely technical but also organisational and ethical in nature. Participants found issues such as outdated infrastructure, weak interoperability among systems, isolated departments, low levels of data literacy among staff, poor coordination efforts, and concerns regarding privacy rights and compliance with POPIA regulations. These insights suggest that the challenge lies not just in the lack of student data but also in the absence of an integrated framework governed by ethical standards for utilising such data effectively in retention strategies.
Guided by the TOE framework alongside Tinto’s (1993) model, the proposed framework in this study was directly informed by the empirical themes identified throughout the research. These themes were translated into concrete institutional and analytical requirements. Table 1 illustrates how key themes derived from existing literature were both validated and expanded upon through the study’s findings, thereby establishing a theoretical and empirical basis for designing the proposed framework.
| TABLE 1: A summary of literature findings and empirical evidence from the study. |
The Big Data framework
This section presents the proposed Big Data framework for analysing student data in higher education for improved retention.
The framework directly addresses the issue highlighted in this study: the disjointed use of student data, isolated support initiatives, and limited predictive capabilities within universities. This proposed framework is structured into four interconnected layers:
- Contextual layer: This layer of the framework outlines the conditions necessary for the adoption and sustainability of analytics. Derived directly from the TOE framework, it encompasses technological, organisational, and environmental elements. These elements collectively influence institutional preparedness, including infrastructure, leadership endorsement, policies, staff competency, regulations, and pressures from national higher education systems.
- Data layer: This layer focuses on the foundational aspects required for effective analytics within institutions. This layer emphasises integrating data across multiple systems, along with data governance and other external drivers that shape how institutions handle data.
- Analytics layer: This layer serves as the engine of the framework. It involves managing extensive and varied datasets. It includes predictive analytics capabilities to identify students at risk, dashboards and visualisation tools for interpretation support as well as real-time monitoring for prompt interventions. Together, elements in this layer convert raw student data into insights that inform institutional decision-making.
- Outcome layer: The output from the analytics capability layer is acted upon in this final layer. The layer concentrates on how insights from the Analytics layer are implemented through tailored retention strategies. These strategies encompass targeted academic assistance, social support initiatives, and risk-based actions taken by institutions to enhance student success and retention.
Each layer’s subcomponent, including its contribution to the framework, is explained in Table 2.
| TABLE 2: A summary of the proposed Big Data framework for student retention. |
Table 2 summarises the various layers and components of the framework. In Table 3, the rationale for each element is provided by linking it to its theoretical roots, relevant literature, and empirical evidence.
| TABLE 3: Framework elements linked to theoretical, literature and empirical findings. |
Framework interpretation
The proposed Big Data framework depicted in Figure 1 is substantially enriched by its combination with two well-established theoretical models: Tinto’s Student Retention Model and the TOE Framework. This combination ensures that the framework is conceptually sound and practical for institutional use. Again, the combination of these theories guarantees that the framework is both educationally valid and feasible for institutional implementation. Tinto’s (1993) model underscores the importance of academic and social integration in fostering student persistence. The proposed framework promotes academic integration through predictive analytics and tailored tutoring interventions that assist students before they begin to struggle. Social integration is supported by tracking student engagement, enabling timely, personalised peer mentoring and initiatives to enhance social inclusion. Institutional commitment, another essential aspect of Tinto’s model, is actualised through robust data governance practices and appropriate resource allocation, thereby reinforcing a culture centred on student care.
 |
FIGURE 1: The proposed Big Data framework to enhance student retention in higher education. |
|
In addition to Tinto’s model, the TOE framework provides a systems-level perspective on the implementation of Big Data analytics within education. The technological component of TOE encompasses the infrastructure and tools necessary for extensive data collection, integration, and analysis. This includes platforms that facilitate real-time data processing and user-friendly dashboards designed to engage staff effectively. The organisational context highlights the need for strong leadership within institutions, collaboration across departments, and thorough staff training, all crucial for incorporating analytics into routine academic and administrative decision-making processes. Furthermore, the environmental context ensures that the framework adheres to ethical standards and legal requirements; compliance with the POPI Act is particularly vital in building stakeholder trust while ensuring that data usage respects students’ privacy and upholds institutional integrity.
The implications of this framework are both practical and transformative. One of its most significant applications lies in early risk detection. By utilising machine learning alongside institutional data such as attendance records, assessment results, and engagement with support services, universities can proactively identify students who may be at risk of dropping out. These insights facilitate targeted interventions such as academic help, mental health resources, or financial aid guidance tailored to individual circumstances. In addition, the framework aids in strategic resource allocation by directing limited institutional resources, whether financial support, staffing needs, or programme assistance, towards students and departments that require them most urgently.
Again, this framework enhances institutional decision-making by translating complex data into understandable formats that can be acted upon. Visual dashboards enable university leaders, advisers, and faculty members to monitor progress, pinpoint areas in need of attention, and assess the effectiveness of various support strategies. The information generated directly informs policy development within institutions, ensuring that approaches aimed at promoting student success are not only responsive but also grounded in evidence and forward-thinking.
These interpretations confirm the framework’s capacity to revolutionise how South African universities address student retention. By embedding principles from Tinto’s Student Retention Model and the TOE Framework into a scalable, context-aware data infrastructure, institutions can transition from reactive crisis management to proactive strategies focused on sustainable student success, grounded in equity and informed by data.
Conclusion
This research finds that applying Big Data analytics can enhance student retention at South African universities. Using the TOE framework and Tinto’s (1993) model, the findings indicate that student dropout is driven by academic, financial, social, and institutional factors. In addition, the successful application of analytics depends on the preparedness of organisations, the capacity of staff, and adherence to ethical data handling. Therefore, the proposed framework helps in promoting better early detection, intervention strategies, and decision-making. For university leadership, the findings imply that student retention should be viewed as a shared endeavour at the level of the institution, supported by data integration, employee training initiatives, and collaboration among university departments, information and communication technology (ICT) services, and student support services. In terms of policy, this study highlights the need for governance mechanisms, equity considerations, and compliance with POPIA as tools for analytics to optimise student success. Thus, further research should investigate how such a framework can be applied across a variety of institutional contexts and how analytics-driven interventions can be effectively adopted and sustained over time.
Acknowledgements
This article is based on research originally conducted as part of Ganizani Mainoti’s doctoral thesis entitled, ‘A Big Data framework for analysing educational data to enhance student retention at universities’, submitted to the University of Fort Hare. The thesis is currently unpublished and not publicly available. The thesis was supervised by Roxanne Piderit. The thesis was reworked, revised and adapted into a journal article for publication. The author confirms that the content has not been previously published or disseminated and complies with ethical standards for original publication.
During the preparation of this article, the authors used Grammarly, version V1.2.210.1786, for language correction. The content was reviewed and edited by the authors, who take full responsibility for its accuracy.
Competing interest
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
CRediT authorship contribution
Ganizani F. Mainoti: Investigation, Methodology, Writing – original draft, Writing – review & editing. Roxanne Piderit: Supervision. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.
Funding information
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data availability
The data that support the findings of this study are available from the corresponding author, Ganizani Mainoti, upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. They do 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.
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