Escalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement.

This systematic review examines the application of learning analytics to enhance user experience within Learning Management Systems in higher education institutions. Addressing a salient knowledge gap regarding the optimal integration of learning analytics for diverse learner populations, this study identifies analytical approaches and delineates implementation challenges that contribute to data misinterpretation and underutilisation. Consequently, the absence of a systematic evaluation of analytical methodologies impedes the capacity of higher education institutes to tailor learning processes to individual student needs. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search was conducted across five academic databases. Studies employing learning analytics within Learning Management Systems environments to improve user experience in higher education institutions were included, while purely theoretical or non-higher education institution studies were excluded, resulting in a final corpus of 41 studies. Methodological rigour was assessed using the Critical Appraisal Skills Programme Checklist. This study revealed diverse learning analytics methodologies and a dual research focus on specific platforms and broader impacts on Learning Management Systems. However, ethical, implementation, generalisability, interpretation, personalisation, and system quality challenges impede effective learning analytics integration for user experience improvement, demanding rigorous and contextually aware strategies. This study’s reliance on existing literature introduces potential selection and database biases. As such, future research should prioritise empirical validation and cross-institutional studies to address these limitations.

This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, investigated the intersection of data privacy, postgraduate educational data analytics (EDA), and sustainability. Existing literature focuses on general privacy concerns in EDA, neglecting the specific data collected and related risks in postgraduate programmes. This review addresses this gap by identifying data types used by higher education institutions in postgraduate initiatives and evaluating the adequacy of current ethical frameworks, particularly for sustainability goals. Recognising the lack of established best practices for balancing data utility and privacy, the review analyses privacy-preserving techniques. Through identifying key data types collected in postgraduate initiatives, evaluating existing ethical frameworks, and exploring privacy-preserving educational data analytics techniques, this study provided practical guidance for higher education institutions to navigate the challenges of balancing data utility and student privacy. The results suggest that higher education institutions can achieve sustainable data use by adopting a comprehensive approach that incorporates best practices, emerging technologies, and continuous monitoring to safeguard student privacy while leveraging the benefits of educational data analytics for achieving the Sustainable Development Goals.

This systematic review investigated how Higher Education Institutions (HEIs) optimise data analytics in postgraduate programmes to enhance student achievement. Existing research explores the theoretical benefits of data analytics but lacks practical guidance on strategies to effectively implement and utilise data analytics for student success. As such, this review aimed to identify data analytics approaches used by HEIs and explore challenges and best practices in their application. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Five databases were searched. Studies that examined data analytics in HEIs postgraduate programmes and their impact on student learning were included. Studies that were solely theoretical or in non-postgraduate settings were excluded. Twenty-six studies were included. Quality assessment using the Critical Appraisal Skills Programme (CASP) Checklist was employed. The review identified various data analytics approaches including descriptive, predictive, and prescriptive analytics, among others. These approaches can improve foundational skills, create supportive learning environments, and optimise teaching strategies. However, limitations (standardised tests, data integration) and privacy concerns were acknowledged. Recommendations include developing a comprehensive evaluation system, equipping educators with the skills to utilise diverse analytics to enhance student achievement, fostering open communication about data use, and cultivating a data-literate student body. While diverse approaches were explored, the review’s lack of specific contextual details may limit the generalisability of findings. To mitigate this, the review categorised techniques and provided references for further exploration.

The potential of data analytics in higher education is well acknowledged. Yet, there is a notable gap in the literature regarding the practical application of theoretical frameworks for its implementation and evaluation. While research has investigated data analytics for personalised learning, student success prediction, and programme assessment, a comprehensive understanding of how these applications align with existing theoretical frameworks is still lacking. This scoping review investigates how theoretical frameworks have been operationalised and applied within empirical data analytics studies. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and guidelines, this review searched nine databases and five journals to yield 41 relevant publications used in this review. This scoping review revealed that several studies inadvertently yielded implicit theoretical insights throughout the research process. However, these insights were often not explicitly acknowledged, systematically explored or intentionally incorporated into the study’s design. Consequently, this oversight hindered the development of a comprehensive and robust theoretical framework to underpin data analytics research in higher education. Nevertheless, the identified diversity of theoretical frameworks employed in the reviewed studies suggests the potential for a more theoretically grounded approach to analysing complex educational phenomena. To advance data analytics research in higher education, researchers should actively integrate established theoretical frameworks to foster a deeper understanding of data and inform targeted interventions. Future research directions include exploring the strengths and limitations of specific frameworks to guide researchers in selecting appropriate theoretical lenses.

Despite the increasing interest in data analytics applications within postgraduate education research, there remains a significant gap in research dedicated to exploring mixed methods research for such investigations. This study undertook to bridge this gap by exploring the application and use of mixed methods research to examine data analytics within postgraduate education research. The research specifically explored the prevalence and characteristics of existing mixed methods research, and compared the unique insights they offer within this specific research domain. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the researchers conducted a systematic search across five databases, resulting in the inclusion of 22 relevant publications. Data were systematically extracted, capturing study characteristics, methodological approaches, data analysis techniques, key findings, and identified limitations for analysis. This study underscored the value and use of mixed methods research in advancing data analytics research within the context of postgraduate education. Thus, by integrating quantitative and qualitative data, this methodology generates robust, triangulated findings to tackle complex research questions. This leads to actionable insights that optimise student learning experiences and support mechanisms, fostering critical research skills, problem-solving abilities, and data-driven expertise.