Evidence-Based Student Success in AgriSciences

This comprehensive study represents one of the most extensive student success analyses conducted in the Faculty of AgriSciences at Stellenbosch University. We tracked 153 students from the 2021 cohort over four years, revealing that 48.4% did not complete their degrees in the expected timeframe. Using the SUBSIFY framework, we measured 48 validated wellbeing factors at enrollment to understand what non-academic factors determine whether students successfully complete their degrees versus struggling or leaving.

Our rigorous methodology involved 3,696 statistical comparisons across 77 demographic scenarios with False Discovery Rate correction. From 358 initial significant results, only 75 survived stringent correction—representing a 2.0% success rate that ensures genuine relationships rather than chance findings. Four universal success factors emerged: physical health and wellness behaviors, emotional intelligence (particularly "knowing one's emotional needs"), religious and spiritual practices, and basic wellness behaviors including 150+ minutes weekly exercise and 6-8 hours quality sleep.

Different demographic groups showed distinct patterns requiring tailored interventions. Non-first generation students demonstrated our strongest pattern (30 significant factors, 23 surviving FDR correction), while students with Grade 12 averages of 70-80% showed sleep quality as their most powerful predictor. Most remarkably, our findings revealed surprising patterns including financial worry paradoxes and character virtue reversals that challenge conventional assumptions about student success.

The study establishes evidence-based intervention targets across three tiers: universal support (physical wellness, emotional intelligence, sleep hygiene education), targeted interventions (intensive wellness coaching for specific demographic groups), and early identification through SUBSIFY screening. This research provides AgriSciences with a scientific foundation to increase the current 51.6% four-year graduation rate to a target of 70% through comprehensive, evidence-based student support addressing the whole student beyond academic performance alone.

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 dramatic achievement gaps within the Faculty of AgriSciences that demand urgent institutional attention. Through comprehensive analysis of 153 students tracked from 2021 enrollment to graduation, we document substantial disparities in degree completion rates that mask behind the 51.6% overall DCM success rate.

Academic preparation shows the most staggering disparity: a 51.5 percentage point gap between students with Grade 12 averages of 80-90% (86.1% success) versus those below 70% (34.6% success). Racial achievement gaps reveal a 23.2 percentage point disparity between White students (58.2% success) and Black African students (35.0% success). Gender patterns show an 11.0 percentage point gap favoring female students (56.8%) over male students (45.8%). Socioeconomic patterns produce counterintuitive findings, with SES Level 1 students achieving the highest success rate at 74.1%, while financially stressed students (SES 2+) achieve only 43.4% success.

Critically, the presentation demonstrates how intersectional disadvantages compound barriers to success and why traditional academic remediation proves insufficient for agricultural education contexts. The analysis connects these achievement patterns to our wellbeing research findings, showing how 22 statistically validated factors provide evidence-based pathways for closing gaps through a three-tier intervention framework targeting universal wellbeing support, demographic-specific interventions, and intensive individual support for students facing multiple challenges. This approach addresses modifiable wellbeing factors - sleep hygiene, physical health, emotional intelligence - that can benefit students regardless of background while allowing targeted approaches based on group-specific patterns identified in AgriSciences education.

This video demonstrates the exact statistical procedures used throughout our AgriSciences study, using "Know Emotional Needs" as a concrete example to illustrate our complete analytical framework. Through detailed examination of how we conducted 3,696 individual t-tests across 77 demographic scenarios, viewers see precisely how advanced statistical techniques distinguish genuine predictive factors from statistical noise in wellbeing research.

The presentation walks through our rigorous 11-step methodology: from the original 1-5 Likert scale measurement of emotional self-awareness, through our three-pathway student framework (DCM vs UP&L, DCM vs UP, DCM vs L), to independent samples t-tests and critical False Discovery Rate correction using the Benjamini-Hochberg procedure. "Know Emotional Needs" emerged as a universal predictor, showing consistent medium to large effect sizes (Cohen's d = 0.50-1.08) across multiple demographic groups including non-first generation students, Grade 12 70-80% achievers, and SES Level 0 students.

Critically, the video demonstrates why rigorous multiple comparison correction is essential when conducting thousands of statistical tests—without FDR correction, we'd expect 185 false positives by chance alone from our 3,696 comparisons. Only factors surviving this conservative correction, like emotional intelligence in key subgroups, can be considered statistically reliable intervention targets. The presentation shows how successful AgriSciences students consistently averaged 4.076 on emotional awareness compared to 3.649 for others, representing practically meaningful differences that warrant targeted intervention.

This methodological transparency establishes why emotional intelligence workshops, self-awareness training, and emotional regulation programs represent evidence-based investment priorities for AgriSciences student success, backed by solid statistical rigor rather than intuition or wishful thinking.

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 AgriSciences 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 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 AgriSciences. The papers will be made available on this page once they become accessible.