What Are Predictive “Early Warning” Systems?

Over the last few years, many schools and universities have begun using predictive analytics systems which claim to identify struggling students before their teachers, tutors, or families might fully clock onto something being wrong. These tools can ingest large amounts of data – attendance records, assignment submissions, quiz scores, LMS logins, time spent on readings or videos, even patterns of forum participation – and look for combinations which historically correlate with poor outcomes such as failing a subject or dropping out. The output is usually a simple signal: a risk score, a flag on a dashboard, or a list of students sorted by “likelihood of needing intervention”.​

Some systems are embedded directly into learning platforms, providing colour‑coded indicators next to student names, while others operate at a higher level, feeding reports to pastoral care teams or data offices which then prompt teachers to check in. The marketing promise is straightforward: instead of reacting after a failure or crisis, schools can proactively reach out, adjust support, and connect students to resources as there is still time to change course. For overworked staff, or understaffed university and school structures., the idea of a digital assistant quietly monitoring patterns in the background could be tempting.​

Under the hood, most of these systems use relatively simple models: logistic regression, decision trees, or basic machine‑learning classifiers trained on several years of past cohort data. A smaller set are beginning to experiment with more complex AI methods, but the core logic remains the same: given a particular mix of signals, how similar is this student’s current trajectory to that of previous students who struggled? This is pattern‑matching, not fate. Some danger can arise here when risk scores are treated as destiny, or when nobody asks whether the original data reflects fair and inclusive practices in the first place.​

When They Can Help – and When They Quietly Can Hurt

Used with care, early warning systems can surface students who might otherwise fly under the radar. A quiet student who stops submitting low‑stakes quizzes, or a commuter who begins arriving late more often, may not immediately stand out in a classroom or crowded lecture hall, but their data trail can signal that something has shifted. When those signals trigger human conversations – a tutor or professor checking how a student is coping with family responsibilities, a counsellor reaching out about potential transport issues or financial stress – the technology can act as a pointer towards care and engagement, rather than signalling a verdict.​

There is also some evidence that structured, timely nudges (for example, personalised messages offering concrete next steps when risk rises) can support persistence in large, impersonal systems where students might otherwise feel invisible, particularly at a tertiary level. Importantly, the best‑designed interventions tend to focus on changing the conditions around the student (access to resources, clarity of expectations, flexibility of assessment and exam times) rather than simply urging them to “try harder” or ‘’get it together’’.​

However there are serious pitfalls and risks we need to take into account with the current way these systems can be structured. First, predictive models are only as fair as the data they learn from. If certain groups – for example, students from particular socioeconomic backgrounds, learners with disabilities, or students juggling work and study – have historically been under‑supported, the system may simply learn to flag them more often without solving underlying inequities. This can create self‑fulfilling patterns: a student labelled “high‑risk” might be steered away from challenging courses or treated with lower expectations. Second, the focus on what can be easily measured (logins, clicks, deadlines) risks neglecting forms of engagement which are not captured in online Learning Management Systems (LMS), such as offline study, caring responsibilities, or cultural and language barriers.​

For parents and students, one practical stance is to treat these systems as imperfect smoke alarms. A flag might signal that something is indeed smouldering – but the real work lies in the conversation that follows, not in the sign itself. For teachers, administration staff and tutors, it is worth asking some of the questions below whenever such tools are introduced:

• What exactly is being measured, and whose outcomes were used to train the system? When does the data being used to train the system data from and what was the scope?​
• Who sees the risk scores, and how are they allowed or not allowed to use them?
• What support options actually exist once a student is flagged – and do those options address structural barriers, not just individual effort?
• What follow up procedures are there in place to understand if any intervention given was timely and of use?

Here at Turtle & Elephant we are currently not using these systems, and have no plans to. If any of our students and their families come into contact with these kinds of systems through their formal education, we would try to help them think about what questions to ask to ensure the student’s best interest are kept central. 

As we are now, we see our role to support students on a daily basis in developing their own early‑warning signals: we try to notice when reading takes longer, when avoidance to tasks might start to be noticeable, and when it is time to perhaps increase collaboration with the student’s school and family. What would be key to these systems working well is how they are used, and that accountability is practiced so they are only used for the best interests of the student. 

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Note: the first draft of this article was done by AI Chatbot Claude with the support of Max Capacity. The text was then edited and adapted by Jaye Sergeant of Turtle & elephant, who is responsible for the published version.

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