Arts Student Success: A Multi-Pathway Analysis

This groundbreaking study represents the most extensive student success analysis conducted in the Faculty of Arts and Social Sciences at Stellenbosch University. We tracked 340 students through multiple academic pathways, moving far beyond the traditional 'graduate versus dropout' binary to understand the nuanced reality of what actually distinguishes successful students from those who struggle or leave.

Using the comprehensive SUBSIFY framework, we examined 48 different wellbeing factors across mental health, physical wellness, mindset, relationships, purpose, and daily habits. Our revolutionary multi-pathway approach identified three distinct student trajectories: Degree Completers (188 students), Unsuccessful Persisters (89 students), and Leavers (63 students). This approach reveals that different struggles require different types of support.

Our analysis conducted 4,224 statistical tests with rigorous False Discovery Rate correction, ensuring that our findings represent genuine patterns rather than statistical accidents. From 266 initial significant results, only 22 survived our conservative multiple comparison correction - demonstrating robust, reliable differences in wellbeing factors between successful and struggling students.

Critical success factors emerged consistently across multiple demographic groups, suggesting universal importance for student success. Mental health and happiness factors appeared as foundational for academic success across all groups. Holistic wellness approaches combining physical and mental health showed consistent importance. Most significantly, evidence-based interventions can now be precisely targeted to specific student populations and situations.

This methodology can transform how we support students in Arts and Social Sciences, moving from guesswork to evidence-based practice that addresses the real factors that determine student success.

Can wellbeing measures taken at university entry really predict academic outcomes years later?

This presentation addresses a fundamental question underlying all student success research: the temporal stability of non-academic measures. Using extensive longitudinal research evidence, we examine whether the 49 wellbeing, mindset, and behavioral factors in our SUBSIFY framework demonstrate sufficient stability over time to enable meaningful long-term prediction. Key findings reveal that psychological constructs show exceptional stability, wellbeing measures demonstrate strong temporal consistency, and even behavioral measures maintain meaningful stability over multi-year periods. Importantly, these patterns persist naturally but remain malleable through targeted intervention. Without temporal stability, early identification would be impossible—with it, we can confidently predict student trajectories and intervene early with evidence-based support to change outcomes before problems emerge.

The results presented on this page are based on data collected through SUBSIFY (Stellenbosch University Baseline Survey for Incoming First-Years), a comprehensive assessment framework that measures 49 validated factors across four major categories: Flourishing Index Components (20 factors), Wellness Habits (14 factors), EPOCH Well-being Measures (6 factors), and Mindset Measures (9 factors). To better understand what these wellbeing factors represent and how they are measured, watch the introductory video below, which provides a detailed overview of each category and the specific measures within SUBSIFY. This framework goes beyond traditional academic predictors to capture the broader range of non-academic factors that may influence student success, providing the foundation for the faculty-specific analyses presented here.

This presentation reveals significant achievement gaps within the Faculty of Arts & Social Sciences that demand urgent attention and evidence-based intervention. Through comprehensive analysis of 340 SUBSIFY participants tracked longitudinally from 2021, we document substantial disparities in degree completion rates across demographic groups. Academic preparation emerges as the strongest predictor with a 28.1 percentage point gap between highest and lowest performing groups, while racial achievement gaps show a 19.4 percentage point disparity between White students (60.4% success) and Black African students (41.0% success). The analysis reveals a notable 14.8 percentage point gender gap favoring female students, alongside meaningful socioeconomic and first-generation student disparities. Moving beyond documentation, the presentation demonstrates how intersectional disadvantages compound barriers to success and why traditional single-factor interventions prove insufficient. The analysis connects these patterns to our wellbeing research findings, showing how 22 statistically validated factors - particularly "Have Realistic Goals" as the universal predictor and 17 significant factors for CBIA students - provide evidence-based pathways for closing these gaps through targeted interventions addressing modifiable wellbeing factors rather than relying solely on academic remediation.

This video demonstrates the exact statistical procedures we followed throughout our entire Arts & Social Sciences study, making it essential viewing for readers who want to understand how we arrived at our main findings or need clarification on our methodological approach. Through an analysis of 4,224 individual t-tests across 88 demographic scenarios involving 340 Arts students tracked from enrollment to graduation, we reveal how advanced statistical techniques—including False Discovery Rate correction and effect size analysis—distinguish genuine predictive factors from statistical noise. Using "Have Realistic Goals" as our primary example, the presentation walks viewers through the complete analytical process from raw survey data to evidence-based intervention recommendations, showcasing how medium but consistent effects (Cohen's d = 0.49-0.58) translate into meaningful population-level improvements in Arts student outcomes. This rigorous methodology established realistic goal-setting as our strongest statistical finding and Priority #1 universal intervention target for Arts students, serving as a template for identifying reliable wellbeing predictors that can guide institutional decision-making with statistical confidence. The presentation demonstrates how realistic goal-setting consistently predicts Arts student success across diverse demographic groups, with particular relevance for managing expectations about degree completion timelines and career prospects in humanities fields.

To ensure transparency and scientific rigor in our student success research, we have developed a comprehensive statistical framework that examines wellbeing factors across diverse demographic groups and student pathways. The analysis you see reported for this faculty employs sophisticated multiple comparison corrections, effect size calculations, and variance testing to distinguish genuine predictive factors from statistical noise. This methodology video provides detailed insight into the four-step analytical process we use, explaining techniques such as Levene's test for variance assumptions, False Discovery Rate correction using the Benjamini-Hochberg procedure, and Cohen's d effect size interpretation. Understanding these statistical foundations is crucial for interpreting our findings correctly - particularly why we report both uncorrected and FDR-corrected results, and how our three-pathway student model (degree completers, unsuccessful persisters, and leavers) provides more nuanced insights than traditional binary success/failure approaches. The rigorous application of these methods across thousands of statistical tests ensures that the intervention targets we identify represent reliable, evidence-based opportunities for supporting student success rather than chance findings.

Note: This video was originally created for our EMS faculty analysis, but all methodological questions and implementation guidance apply equally to Arts students. Please disregard any specific EMS references, as the underlying statistical approaches, intervention principles, and implementation strategies are identical across both faculties.

This video addresses the most frequently asked questions about our comprehensive wellbeing analysis of student success patterns. The presentation covers critical methodological questions including why we used False Discovery Rate correction to ensure reliable results, how our 4,224 statistical comparisons were generated across demographic scenarios, and what effect sizes mean for practical intervention planning. It also addresses implementation questions such as which interventions to prioritize first, cost-effective funding strategies, and how findings might transfer to other faculties. Most importantly, the video provides an honest assessment of study limitations, particularly the distinction between correlation and causation, and outlines the intervention research needed to prove that improving wellbeing factors actually causes better student outcomes. Whether you're interested in the statistical rigor behind our findings or practical guidance for implementing evidence-based student support programs, this Q&A provides essential context for understanding how these research results can inform institutional decision-making.

We are currently developing academic paper(s) that will unpack this research, its results, and implications in detail. These publications will provide comprehensive scholarly analysis of the wellbeing factors that predict student success in the Faculty of Arts & Social Sciences. The papers will be made available on this page once they become accessible.