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Student Enrollment and Performance Analysis. This project is aim to analyse student performance and enrolment. We have collected raw data from students based on their previous grades.

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Learning Analytics Based Student Enrollment and Performance Analysis(SEPA)

Project Overview

Introduction

This project is aim to analyse student performance and enrolment. We have collected raw data from students based on their previous grades. The project has two parts, one is system will classify Grades into two catageories Good or Bad using Sciket learn with the help of Machine learning techniques, and in second part we visualize the pre-processed data using D3.js.

Features

Analysis of Student Data

  • Subject wise performance
  • Sub-continent wise enrolment analysis
  • Department wise analysis
  • Analysis of source of Study
  • Prediction of Good or Bad Grades ( In Mainly Machine Learning part)

Project Architecture

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Libraries/Algorithms

Algorithm for Analysis

  • Logistic Regression
  • KNeighbours Classifier
  • Random Forest Classifier
  • SVC(Support Vector Classifier)

Libraries and Tools for Analysis

  • Sciket learn using Python
  • Libraries: Pandas, numpy

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Tools for Visualization

  • D3.js
  • Node.js

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Screenshots of the Visualization

Simple Bar Charts

  • Department wise Enrollment Analysis github-small

  • Country wise Enrollment analysis github-small

  • Sub-Continent wise Enrollment Analysis github-small

  • Learning Resource github-small

Interactive Bar Charts

  • Department wise Student Enrollment Analysis for year 2019 github-small

  • German Student Analysis github-small

  • Internationl Students Analysis github-small

  • Grade Average for each Department github-small

Installation

Installation for Machine learning

Install python with jupyter Notebook and as well as the libraries Pandas and Numpy

If you are Mac or Linux user, you can use the commands, otherwise follow the links for each installations

Installing Jupyter using Pip

Open command Terminal and write down these commands

  • python -m pip install --upgrade pip
  • python -m pip install jupyter

Here are the links

After Installing it, Open Terminal and type "Jupyter Notebook " if its works Congratulations you have installed sucessfully

How to Run

  • Clone our project folder , specially the "LA_project_grades_analysis.ipynb" file and "final_data.tsv"

  • Open Terminal and write Jupyter Notebook, and then open the "LA_project_grades_analysis.ipynb"

  • Set your path for Dateset with name "final_data.tsv"

  • Run it line by line

Installation for Visualization

First make sure you have node installed , if you do not have installed then go to official Node web page and download the latest version.

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write these command to check Node and NPM is properly installed

  • For Node.js "node --version "
  • For NPM "npm --version "

After Installing Node, download the D3.Js librray , if you want to start with a new project

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How to Run

Clone our project and open the one of the visualization folder

To Run it locally, open the terminal in folder where the visualization code reside, here in the case we have in "Visualization using simple bar charts "

  • write the follwoing commands

    • npm install -g http-server
    • http-server
  • Server address

Same as for Interactive bar chart folder

Demo

Group Members

  • Abdul-Rahman, khan
  • A.B.M. Rocknuzzaman
  • Piush, Aggarwal
  • Amin, Shahin
  • Tianyu, Zhu
  • Muhammad, Zeeshan
  • Ghulam Dawood Nasimi

Evaluation

Special Thanks to SOCO TEAM

We are very thankful to Prof. Dr. Mohamed Amine Chatti and Dr. Arham Muslim

  • For providing us such a platform to learn the theoritical concept as well as the practical skills
  • To improve communication, presentation ,theoritical and practical skills
  • And having the oppurtunity to Brainstorm ideas during workshops and as well as have the chance to work with globally minded students
  • This project was the part of Learning Analytics course in UDE

Version

1.0

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Student Enrollment and Performance Analysis. This project is aim to analyse student performance and enrolment. We have collected raw data from students based on their previous grades.

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