Spotting the Student About to Fall Behind, Weeks Before Anyone Else Does
By the time a struggling student shows up on an instructor’s radar — often through a failed midterm or a string of missed classes — meaningful intervention has already gotten harder. The early warning signs, declining assignment scores, dropping engagement, missed logins, are usually visible in the data weeks earlier.
Why this keeps costing you
Those early signals exist across multiple disconnected systems — the LMS, the gradebook, attendance records — and nobody is assembling them into a single, actionable view until a crisis is already underway. Advisors end up reacting to failure instead of preventing it.
How we build it
Grade, engagement, and participation data from the LMS and gradebook feed into a single tracking layer continuously rather than on a term end batch cycle, and a scoring model trained on historical patterns of decline generates a weekly risk score per student rather than waiting for a single bad grade to trigger a flag. Instead of surfacing a raw score with no context, the system matches the specific pattern behind the score — missed logins versus declining scores versus dropping participation — against intervention types that have historically worked for that pattern, and presents the recommendation directly on the advisor's dashboard alongside the trend that triggered it.
What this looks like once it is running
- 1Continuous tracking of grades, engagement, and participation signals
- 2Early alert scoring that flags risk weeks before a formal failure occurs
- 3Recommended intervention paths, not just a raw risk number to interpret
- 4Advisor dashboards built for action, not just static reporting
- 5Trend tracking across a full term or an entire program
Interventions land while they can still genuinely change the outcome, advisors focus their limited time on the students who need it most, and programs see measurably better retention and completion rates.
For more details, click the relevant case study link below.
View Levo AI case studyStudent Learning Analytics (AI Assessment Platform)
Student Learning Analytics is a named feature inside Zaltech's education platform line: comprehensive tracking of student progress, skill development, and learning patterns with AI powered insights and recommendations, built to sit alongside the AI Assessment Platform's grading and proctoring modules so risk scoring draws on the same performance data already being generated.
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Want this one built for your business?
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