Dataset: Impact of social media platforms excessive use on students performance in higher education institutions: A mathematical model approach

Abstract

Dataset Summary This dataset was compiled to support research on excessive social media use among students in higher education institutions. The data are intended for the analysis of social media usage patterns, levels of engagement, and indicators of problematic or addictive social media behaviour among university students. The dataset is suitable for statistical analysis, mathematical modelling, and evidence-based research aimed at understanding the prevalence, determinants, and consequences of excessive social media use. It may be used to investigate relationships between social media behaviour and factors such as academic performance, student well-being, productivity, and social interactions. The data have been organized to facilitate quantitative analysis, parameter estimation, model calibration, and the evaluation of intervention strategies designed to promote healthier digital habits among students. Target Population University students enrolled in higher education programmes. Research Theme Excessive Social Media Use and Its Impact on Student Behaviour and Academic Outcomes Data Type Cross-sectional survey and/or institutional data suitable for descriptive, inferential, and mathematical modelling analyses. Intended Use - Academic research- Statistical and mathematical modelling- Scientific publications- Policy development- Design and evaluation of interventions addressing excessive social media use among university students Auxiliary Population Data To support study design and population estimation, institutional enrollment records from the University of the Free State (QwaQwa Campus) were utilized. These records provide module-level enrollment counts by faculty and academic year. Variables - Campus- Module Code- Module Name- Registered Faculty- Academic Year (2022, 2023, 2024, 2025)- Enrollment Count Modules Included - MATM1534: Calculus- MATM1622: Introduction to Advanced Mathematics- MATM2614: Vector Analysis- MATM2664: Sequences and Series- MATA2654: Ordinary Differential Equations Faculties Represented - Faculty of Education- Faculty of Natural and Agricultural Sciences Enrollment Totals by Year - 2022: 346 students- 2023: 327 students- 2024: 443 students- 2025: 488 students Interpretation The enrollment records represent module registrations by faculty and academic year and serve as a proxy for estimating the population of students eligible for participation in a social media excessive-use study within Mathematics and Mathematics Education programmes. The enrollment data themselves do not contain social media behaviour variables, such as usage duration, addiction scores, platform preferences, frequency of use, or survey responses. Data Organization Strengths - Organized by module and academic year- Faculty-level disaggregation is available- Module totals are provided- Annual grand totals are included Limitations - The raw data are not stored in a tidy analytical format- Formatting inconsistencies are present in some records- Blank rows and fragmented entries occur in the source file- Metadata and variable definitions are not included- No student-level information is available Recommended Analytical Structure Campus | ModuleCode | ModuleName | Faculty | Year | Enrollment Potential Application in Social Media Research The 2025 enrollment total of 488 students may be used as an estimate of the study population size. Module-level enrollment counts can further support sample size determination, stratified sampling procedures, recruitment planning, and model calibration within studies investigating excessive social media use among higher education students.