Student dropout is often linked to broad factors like financial pressure, academic performance, or low engagement, but in reality, these are just the tip of the iceberg. Beneath them, there lies a spectrum of overlooked and surprising causes. In this blog, we explore three unexpected cases—and how data analytics can help address them.
Sometimes, the most telling signals are the ones we don’t think to question. When students drop out, the reasons aren’t always obvious—and trying to identify them can feel like navigating in the dark. Each institution faces its own mix of challenges, and assumptions often fall short. That’s where data analytics comes in: not to give generic answers, but rather to help your institution make sense of complex realities.
In this blog, we explore three real cases where the data told a story no one expected.
Psychological and social issues triggering student dropout
A study carried out by researchers at the University of Granada (Lorenzo et al., 2023) identified five main causes of student dropout, with psychological and social factors being the most recurrent. What makes this research especially relevant is the wide variety of reasons under the same umbrella—stress, anxiety, difficulty making friends, feeling disconnected from the university community, and even how students perceive themselves.
With this in mind, data analytics can offer institutions a clearer picture of students’ psychological and social challenges—making it possible to detect risks early and develop integration strategies that support a stronger sense of belonging within the university community.
When student dropout means pursuing the ‘university of life’
According to an article by the World Economic Forum (2022), 17% of 1,250 undergraduate students surveyed in the US—expected to graduate in 2023—said they planned to drop out of college after that year’s academic term. Of those students, 31% cited the desire to get a job as their main reason. The study suggests that the strong labor demand at the time was more appealing than finishing a university degree.
What can your university do when facing this kind of trend? With the help of data analytics, institutions might identify early signs of this shift and respond by aligning their curriculum with workforce or career training, strengthening partnerships with local companies, or highlighting the long-term value of completing a degree to access better-qualified jobs.
The same study revealed another surprising insight: 28% of students said they wanted to drop out to become entrepreneurs, inspired by famous cases like Mark Zuckerberg or Bill Gates. If you ask us, that’s pure gold for universities trying to reframe the conversation—emphasizing how higher education can enhance, rather than hinder, the entrepreneurial path.
Learning Management System (LMS) disengagement: the dropout risk warning you might be missing
A study conducted at a Finnish university (Vaarma & Li, 2024) analyzed the behavior of 8,813 students using demographic, academic, and LMS data to predict dropout risk. Surprisingly, one of the strongest indicators wasn’t GPA—it was how active students were on Moodle, the university’s learning platform. Those with fewer logins and minimal interaction were more likely to drop out, even if their grades looked fine on paper.
This highlights the power of predictive analytics: it can flag students at risk based on patterns of digital disengagement, offering institutions the chance to intervene long before academic performance begins to slip.
Data Analytics: that’s how you predict, prevent, and retain
Identifying why students drop out isn’t guesswork—it’s a process. And with the right tools, institutions can move from assumptions to informed action.
At Aquinas, we built Data Vision to make that process easier. It helps education leaders navigate the full data analytics journey—from organizing institutional data, to spotting patterns, and translating them into smart, timely decisions that support student success.
If you’re ready to take the next step, we’ve created a free guide that breaks down the three key stages of data analytics for student retention. It’s a practical starting point for turning insights into action.
🔗 [Download the guide here]
Student Dropout is not a mystery anymore—you just need the right tool to adapt better.
Lorenzo, O., Galdón, S. & Lendínez, A. (2023). Factors contributing to university dropout: a review. Frontiers, 8:1159864.
DOI: 10.3389/feduc.2023.1159864
Varma, M. & Li, H. (2024). Predicting student dropouts with machine learning: An empirical study in Finnish higher education. Technology in Society, 76, 1-10.
DOI: https://doi.org/10.1016/j.techsoc.2024.102474
World Economic Forum. (September 6, 2022). More students are dropping out of college in the US – here’s why. https://www.weforum.org/stories/2022/09/college-student-dropouts-2022/