Case · Education
EdTech — early dropout signal + pass-rate scenarios
// PERSONA
Yoo, Data Lead — certification EdTech
// INDUSTRY
Education / EdTech
// DATASET
case_09 · learner_activity.csv · 240K learners × 18 months
PROBLEM
The Problem
240K learners' study logs, assessments, and environment data piled up, but the team could not answer "which learner is at high dropout risk in the next 4 weeks?" or "what study pattern would get them to pass?" — no model gave a justified, learner-specific answer.
COMING SOON
APPROACH
XimTier Approach
Study activity, assessments, and environment are regressed to detect early dropout signals, with SHAP contributions for each at-risk learner. Reverse What-If solves the study-hours / problem-count / review-cycle mix needed to hit a target pass rate, and produces a rationale that is explainable to learners and parents.
COMING SOON
OUTCOMES
Outcomes
OUT / 01
Dropout detection 4 weeks early (84% accuracy)
OUT / 02
Pre-simulated pass-rate scenarios
OUT / 03
Learner / parent-facing explainable rationale
OUT / 04
Tutor capacity +40% (learners per tutor)
⚠ Example data — fine-tuned on customer data at deployment.