Project Work: Review-1 Report Date:18-10-2024
Work done details
Proposed Title: Student Success Platform
Abstract:
The Student Success Platform is designed to tackle the academic and career-related challenges that
engineering students frequently face. Our platform offers tools for comprehensive academic
progress tracking, consistent study habits, placement readiness, expert guidance, and access to
industry-relevant courses. By integrating these resources into a single platform, we aim to enhance
student success, equipping them with the skills necessary for product-based companies and other
high-level job opportunities. Our platform is a full-stack solution built with React, Node.js, and
MongoDB, enhanced by machine learning through TensorFlow.js for personalized
recommendations.
Problem Statement:
Engineering students often struggle with balancing academics, skill development, and career
planning, particularly in their early years. The lack of a structured support system to track progress,
maintain consistency, and prepare for placements makes it challenging to achieve career readiness. This
project addresses these issues by providing a tailored platform that supports students in setting and
achieving academic and career goals effectively.
Work done:
Feature Identification and Design:
During the initial weeks, we identified twelve key features essential for supporting students in
their academic journeys. These include Progress Tracking, Semester-Wise Planning, Placement
Analyser, Expert Guidance, and more. We then moved on to designing the user interface, ensuring it is
intuitive and caters to students' needs by allowing easy access to these tools.
Technologies Selection:
The project employs a full-stack technology suite:
Frontend: React for a responsive and interactive user interface.
Backend: Node.js and Express for managing server-side functionalities.
Database: MongoDB for flexible and scalable data management.
Machine Learning: TensorFlow.js to deliver personalized insights directly in the browser.
Data Collection:
We have gathered data from educational resources, academic forums, and student feedback to inform the
feature development. This data will guide the implementation of each feature, ensuring they are grounded
in the real challenges faced by students.
Algorithms Selection:
To offer personalized learning and career advice, we are exploring machine learning algorithms that can
provide tailored course recommendations and placement preparation tips. This will be integrated through
TensorFlow.js to support real-time data processing and personalized feedback.
Next Steps:
Continue implementing the designed features using the selected technologies.
Conduct user testing to refine the platform's interface and functionalities.
Integrate machine learning algorithms to enhance personalized guidance and recommendations.
Submitted by:
S.No Roll No Name of the Student Signature of the
student
1 219X1A2847 Chettay Aakash
2 219X1A2858 Naidupalli Shaik Ashraf Hussain
3 219X1A2845 B Aravind
4 229X5A2872 SMD Arif
Project Guide Name & Designation
Signature with date
Project Guide Name & Designation
Signature with date
