Assessfy Industry Projects Lab Advanced 6 milestones 100 marks

Student Dropout Prediction and Early Intervention Dashboard for Indian EdTech Platforms

Industry: EdTech Industry: EdTech Function: Data Analytics Type: Industry-vertical applied project Team: up to 4 Assessment: 6 milestones (100 marks)

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

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About this project

Objective: To develop a predictive dashboard that identifies at-risk students and recommends targeted interventions to reduce dropout rates on Indian EdTech platforms.

Context: Student dropout is a persistent challenge for Indian EdTech companies, impacting learning outcomes and platform revenues. Causes include low engagement, digital divide, language barriers, and lack of timely support, especially in Tier 2/3 cities and rural areas.

Approach: The team will collect and analyze historical engagement data from an Indian EdTech provider (or public datasets like NPTEL/SWAYAM), apply predictive analytics to flag at-risk students, and design a dashboard for educators to monitor risk and suggest evidence-based interventions.

Deliverables: The project includes a data pipeline, machine learning model for dropout risk, an interactive dashboard (Power BI/Tableau), and a report analyzing top risk factors, model performance, and recommended interventions tailored to Indian learners.

Business Impact: The solution enables EdTech managers to proactively address student dropouts, optimize intervention resources, improve retention, and inform future product enhancements—directly supporting business sustainability and learner success in the Indian context.

Milestones

1. Problem Definition & Business Case

10 marks 18d

Define the specific dropout problem for an Indian EdTech context, identify stakeholders, and outline the business case. Reviewed via a project proposal and stakeholder validation.

2. Domain Research & Data Gathering

12 marks 22d

Conduct secondary research on dropout causes in Indian EdTech, select or simulate datasets, and gather user requirements. Reviewed with a literature review and data readiness report.

3. Solution Design / Methodology

15 marks 18d

Design the predictive analytics workflow, feature selection, model selection, and dashboard wireframes. Reviewed through a methodology presentation and expert feedback.

4. Build / Analysis & Implementation

30 marks 35d

Develop data pipeline, train/test predictive models, and build the intervention dashboard. Reviewed via code submission, dashboard demo, and interim results.

5. Validation & Results

23 marks 27d

Validate model accuracy, conduct stakeholder/user testing of the dashboard, analyze results, and refine recommendations. Reviewed with a validation report and user feedback.

6. Final Report & Presentation

10 marks 15d

Compile the final report, business impact analysis, and present findings to academic and industry panel. Reviewed through final submission and Q&A.

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Skills you'll learn

EdTechData AnalyticsUnderstanding of Indian EdTech business models and student engagement patternsData cleaning and feature engineering with education datasetsPredictive analytics and machine learning (classification models)Dashboard design and visualization (Power BI/Tableau)Stakeholder communication and requirements analysisData interpretation and actionable insights generationCollaborative project management

Tools used

Python (pandasscikit-learnmatplotlib)SQL (MySQL/PostgreSQL)Microsoft Power BI or TableauExcel for preliminary analysisNPTEL/SWAYAM datasets or simulated EdTech dataCRISP-DM analytics framework

Prerequisites

Basic statistics and probabilityIntroductory Python programmingFundamentals of SQL/data queryingIntroduction to business analytics or management principles

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