Education · Student dropout prediction
Banao builds dropout-prediction models that surface at-risk students to advisors before the withdrawal form arrives. The model reads engagement patterns, assessment trajectories, and attendance signals already sitting in your LMS and student information system.
Advisors get a ranked shortlist each week — names, risk scores, and the signal that triggered the alert — not a report they filter manually. The output is a working list, not a dashboard left open on a screen.
A working early-warning system is more than a risk score. It needs advisor workflows, configurable thresholds, and institution-specific training — we own all three.
We pull LMS login patterns, submission timestamps, assessment scores, and attendance records — signals specific to your institution — rather than applying a generic research model that does not know your courses or calendar.
Each week, advisors receive a ranked list of students with a risk score and the two or three signals that drove it: missed a third submission, login frequency dropped 60%, assessment score fell below course median.
Dropout patterns differ between a first-year undergraduate cohort and a part-time postgraduate programme. Thresholds are set at programme level, not as a blanket institution-wide cut-off.
The pipeline connects to your existing student information system and learning management platform — Moodle, Canvas, Blackboard, or a proprietary build — without requiring a data warehouse rebuild.
Advisors log the outreach they made and the student's response. That outcome data feeds back into the model, so prediction improves over each term rather than staying fixed at deployment accuracy.
Each risk flag shows the contributing signals in plain language. Advisors can override, annotate, and dismiss — and every override is recorded so the model can learn from the advisor's judgement.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Studylab AI
Studylab AI needed at-risk detection that worked inside their adaptive practice product, not as a separate report. Banao built a dropout-signal layer that feeds the same LMS engagement data already powering their learning-path sequencing.
Banao runs a ~300-person engineering operation on the same AI systems we build for clients. InterviewGod reads candidate signals during hiring; Vikaas tracks engagement patterns across our own demand-generation pipeline. We build at-risk detection for the same reason we use it ourselves: waiting for the exit interview is too late.
A system that has to survive inside our own operation is already hardened. When we deploy dropout prediction at an institution, the approach is not experimental — it has already been tested against the kind of noisy, incomplete data that real organisations actually have.
We will tell you before you commission a build:
We audit your actual data before quoting a build. Dropout prediction fails when training data does not reflect real outcomes — we find that out in week one, not month six.
We pull a sample from your LMS and SIS, audit signal completeness and cohort size, and run a baseline feasibility test against historical withdrawal data. You receive an accuracy estimate, a data-readiness report, and a build recommendation — yours to keep. If you proceed, the Sprint fee is credited.
Train on your institution's full historical data, integrate with your LMS and SIS, build the advisor-facing alert workflow and override loop, and set programme-level thresholds with your student-support team.
Go live at the start of a term, monitor alert accuracy through the term, and retrain at the end of each term on the new cohort outcomes. The model improves with each academic cycle.
How many students do you need in a cohort to build a reliable model?
Roughly 200 or more students with historical dropout outcomes gives a usable training set. Below that, the model cannot generalise reliably and we typically recommend a rules-based threshold alert instead. The Discovery Sprint establishes this for your specific cohort before you commit to a build.
Which LMS and SIS platforms do you integrate with?
We have built integrations for Moodle, Canvas, Blackboard, and several proprietary builds. Where a standard API exists we use it; where it does not, we work with your IT team on a data extract. The Discovery Sprint maps your specific integration path.
How do you handle student data privacy and FERPA or GDPR obligations?
Student data stays within your institution's tenancy — we can deploy on your own infrastructure or a contracted cloud region. Student identifiers are decoupled from the training pipeline, and we document every data flow. We work to your existing data-protection agreements rather than asking you to sign ours.
Can advisors see why a student was flagged, or just a score?
Every alert includes the two or three signals that drove the flag in plain language — for example, 'missed two of the last three submission deadlines' and 'login frequency dropped 55% in the past three weeks'. Advisors can override, annotate, and dismiss, and those decisions feed back into the model.
What happens if the model flags a student incorrectly?
Advisors review every alert before acting — the model surfaces candidates, it does not trigger automatic interventions. Incorrect flags are dismissed with a reason code, which feeds back into retraining. We track false-positive rates by programme and report them at each term review.
In the first two weeks we audit your signal completeness and historical withdrawal data. If prediction is not the right answer for your cohort size or data quality, we will say so.
Book a 45-min scoping call