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Early Intervention for Improving Student Retention and Progression

We developed an AI-powered early intervention system designed to detect academic performance issues and enable targeted support. By shifting to a student-centric feedback model, this system leverages Natural Language Processing (NLP), Machine Learning (ML), and diagnostic analytics to provide timely and personalized insights into each student’s academic journey.

Problem Overview

Student retention and progression are critical challenges for educational institutions.
A shift from teacher-centric to student-centric learning can address this to some extent.
To achieve optimal results and foster a culture of "Teaching Excellence," the implementation of a robust student-centric feedback mechanism is vital.

Proposed Solution:

This AI-powered system that gathers, analyses, and utilizes student feedback to enhance learning, improve teaching practices, and ultimately increase student retention and progression
NLP is being used to capture sentimental data and Machine Learning (ML) algorithms is employed to analyse survey data and provide insightful suggesting.

How It Works

Multi-Level Performance Tracking: Weekly and term-based performance is recorded using Class-Based (CBR), Unit-Based (UBR), and School-Based (SBR) metrics.
Student Feedback Surveys: Conducted at the start, mid, and end of the term, capturing input on unit delivery, assessments, and learning challenges.
AI Analytics Framework : Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics
Targeted Interventions: Based on diagnostic results, the system triggers tailored support workshops and alerts academic staff to take proactive measures.

Technologies Used

Natural Language Processing (NLP) to analyze sentiment in student feedback.
Machine Learning (ML) algorithms to analyze performance trends and predict dropouts.
Time Series Analysis to track progression across key academic checkpoints.

Impact

Increased retention through timely and focused student support.
Improved academic progression by addressing issues before they escalate.
Enhanced teaching practices using continuous feedback and analytics.
Fostered a culture of Teaching Excellence and data-informed decision-making in education.

Conclusion

This solution empowers institutions with a robust student-centric performance monitoring framework that enables early intervention and maximizes student success. Educators gain visibility into student challenges, while learners receive the support they need—when they need it most.

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