Tech 101 & System Design Assignment

This assignment proposes a personalized learning recommendation system to solve the problem of generic course suggestions on an e-learning platform, which currently lead to low engagement, poor completion rates, and reduced satisfaction for learners, instructors, and admins.

Key Points:

Problem:
Learners don’t get relevant courses, instructors’ content doesn’t reach the right audience, and admins lack visibility and control over recommendation effectiveness.

Solution:
Build an AI-driven personalized recommendation system tailored to learner skills, interests, career goals, and behavior, while giving instructors insights and admins control.

User Needs Covered:

Learners: relevant recommendations, progress tracking, reminders, AI learning companion

Instructors: course tagging, learner analytics, recommendation performance insights

Admins: algorithm control, bias detection, reporting, system monitoring

KPIs & Targets:

+25% learner engagement

+30% course completion

+20% satisfaction

≥99.9% uptime, <300ms response time

Fair and diverse course exposure

System Design Decision:
A “Fast Lane + Deep Lane” architecture

Fast Lane: real-time, lightweight recommendations (<100ms)

Deep Lane: batch ML processing for deeper personalization (every 6–24 hrs)
This balances speed, scalability, and personalization.

Why This Works:
Fast responses improve UX and engagement, while deeper models improve accuracy over time without hurting performance.

Future Scope:
AI learning assistant, predictive dropout prevention, social learning, cross-platform integration, advanced analytics, and immersive learning (VR/AR).

Outcome:
The system is designed to meet business goals of engagement, completion, and satisfaction through scalable, explainable, and fair personalization.

Working on the Tech 101 and System Design assignments was a valuable learning experience. It challenged me to think critically, apply technical concepts practically, and improve my problem-solving skills. While some aspects were complex and required careful analysis, completing the assignments helped me gain a deeper understanding of system architecture, design principles, and real-world application scenarios. Overall, it was both engaging and enriching.