A risk scoring engine that analyzes grades, login activity, discussion participation, and submission patterns to flag at-risk students weeks before they fail, with specific intervention recommendations.
The institution's advising team was stuck in reactive mode. By the time a student appeared on the failing list, the damage was already done. Missed assignments had piled up, login activity had flatlined, and the student had mentally checked out. Advisors scrambled to reach out, but the conversation usually came too late. The students who needed help the most were the ones nobody saw coming.
The data to predict these outcomes existed in the LMS: grade books, submission timestamps, login logs, discussion board activity. But it sat untouched in isolated tables. No one had the time or tooling to monitor hundreds of students across dozens of courses in real time. Advisors relied on faculty to manually flag struggling students, which meant the alert depended entirely on whether an instructor noticed the pattern and remembered to send an email.
Retention numbers were declining, and leadership knew the problem wasn't a lack of caring; it was a lack of visibility. The team needed a system that could watch every signal the LMS was already collecting, calculate risk in real time, and tell advisors exactly who to call and why, before the student hit the point of no return.
Advisors only learned a student was struggling after they'd already failed, turning every intervention into damage control instead of prevention.
The LMS collected grades, logins, submissions, and discussion activity, but nobody had a way to monitor it across hundreds of students in real time.
Whether a student got flagged depended on individual instructors noticing a pattern and remembering to report it. An unreliable, manual process.
Even when advisors knew students were at risk, there was no ranking or scoring to help them decide who to contact first, so outreach was scattered and unfocused.
Worked with the advising team and faculty to identify the behavioral patterns that consistently preceded student failure: declining grade trends, missed submissions, days since last LMS login, absence from discussion boards, and late assignment patterns. These became the weighted factors in a composite risk scoring algorithm.
Connected to the LMS via OAuth 2.0 and REST APIs using PHP and cURL to pull grade books, submission metadata, login activity, and discussion participation data. A scheduled ingestion process normalizes and stores this data in SQLite, creating a unified student activity profile that updates automatically.
Built a multi-factor risk scoring algorithm that weighs each signal (current grade, submission rate, login recency, discussion engagement, and grade trajectory) against configurable thresholds. Each student receives a composite risk score that categorizes them as high, medium, or low risk, with the formula tuned against historical retention data for 94% accuracy.
Built a faculty-facing dashboard that shows at-risk students ranked by urgency, with detail views showing exactly which factors triggered the alert. Each flagged student comes with specific outreach recommendations. Instead of "this student is at risk," advisors see "this student hasn't logged in for 12 days, has missed 3 submissions, and their grade dropped 15 points this week."
If your advising team is buried in spreadsheets, waiting on faculty flags, or only discovering at-risk students after midterms, there's a better way. Let's talk about what a predictive early alert system could look like for your institution.
Start a ConversationComposite risk scores calculated from grades, missing submissions, login recency, discussion participation, and grade trends, all weighted and tuned against historical retention data.
Drill into any flagged student to see exactly which risk factors triggered the alert: login gaps, submission history, grade trajectory, and discussion activity, all in one view.
Each at-risk student comes with specific intervention suggestions: what to say and why, based on the exact signals driving the risk score.
Students are automatically sorted into high, medium, and low risk tiers, so advisors can focus on who to contact first.
Track how a student's risk score evolves over time. Spot declining trajectories weeks before failure and validate that interventions are actually moving the needle.
A clean, purpose-built interface that shows which students need attention, ranked by urgency. One screen instead of a dozen reports.
The main dashboard ranks students by composite risk score, color-coded by severity. Advisors see at a glance who needs attention, with risk indicators like last login, grade trend, and missing submissions visible without clicking into individual profiles.
A detailed view for each flagged student: individual risk factors, score history, submission timeline, login frequency, and outreach recommendations tied to the specific signals driving the score.
A week-by-week view showing how risk scores change over time. Advisors can see whether their outreach made a difference, spot students whose trajectories are worsening, and check whether early interventions actually worked.
The whole point of an early alert system is to make its own predictions obsolete. When an advisor reaches out and the student course-corrects, the system "got it wrong", and that's the best possible outcome. Accuracy matters for trust, but the real metric is how many flagged students ended up succeeding because someone intervened in time.
A grade is a lagging indicator. By the time a student's grade reflects failure, you're already too late. Login recency, submission patterns, and discussion participation turned out to be far more predictive than raw grade data. They captured disengagement while there was still time to act. The scoring engine's accuracy jumped significantly when behavioral signals were weighted alongside academic ones.
An early version of the dashboard flagged at-risk students but didn't explain the reasoning. Advisors found it unhelpful, they didn't know what to say when they called. Adding specific outreach recommendations tied to the triggering factors ("Student hasn't logged in for 12 days and missed 3 submissions") transformed the tool from a list into an action plan.
If your advising team is still finding out about struggling students after they've already failed, while the data that could have predicted it sits untouched in your LMS, I've already built the system that fixes this. Let's talk about what a predictive early alert engine could look like for your institution.
No pitch. No pressure. Just a conversation about what might work.