Student Performance Analytics & Adaptive Assessment Tool
An AI-powered student performance analytics and adaptive assessment platform for GradeUp Academy. The system analyzes student responses to identify knowledge gaps, dynamically adjusts question difficulty, and provides personalized study recommendations. Teachers receive predictive alerts about students at risk of falling behind.
The Challenge
GradeUp Academy's teachers were struggling to personalize instruction for classrooms of 35-40 students with widely varying skill levels. Assessments were one-size-fits-all, making it impossible to challenge advanced students while supporting those who were struggling. There was no data-driven mechanism to identify at-risk students before they failed.
The academy also wanted to benchmark student performance against state board standards and track improvement over time at an individual and cohort level.
Our Solution
We built a Python/Django-based adaptive assessment engine using Item Response Theory (IRT) to dynamically adjust question difficulty based on each student's historical performance. A scikit-learn classification model predicts student risk scores weekly, alerting teachers to students needing intervention.
The Vue.js teacher dashboard provides cohort-level heat maps, topic mastery charts, and comparison against curriculum benchmarks. Students receive personalized practice question sets and video recommendations based on their identified weak areas. Celery handles batch analytics processing overnight to keep the dashboard fast during school hours.
System Preview
Explore the key screens and dashboards we designed and developed.
Weekly Activity
Monthly Growth
Recent Activity Log
System Configuration
Measurable Outcomes
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