Student_Concentration_Detection

A computer vision project for detecting student concentration levels using image classification techniques. The concentration levels can be one of the two: engaged and not engaged. Within each category, there are 3 sub-categories available.

Project Overview

This project aims to detect student concentration levels using computer vision techniques. The model classifies images to determine whether a student is focused or distracted.

Table of Contents

• Project Overview
• Data
• Model
• Code
• Applications
• Results
• Contact

Data

The dataset consists of images of students in various states of concentration. The images are labeled to indicate whether the student is focused or distracted. Link to dataset: https://www.kaggle.com/datasets/joyee19/studentengagement

Model

The model uses a convolutional neural network (CNN) architecture to classify the images. It is trained from scratch using the PyTorch Library.

Key Features:

• Data preprocessing and augmentation
• Model architecture: CNN with transfer learning
• Training and evaluation metrics
• Visualization of results

Results

The model achieves an accuracy of 91% on the test set and 95% on the training set. Here are some example predictions:

Applications

The Student Concentration Detection project has various potential applications, especially in educational and training environments. Here are some specific use cases:

• Real-Time Monitoring: Integrate with online learning platforms like Coursera, Udemy, or Khan Academy to monitor student engagement in real-time.
• Personalized Learning: Provide personalized feedback to students based on their engagement levels, suggesting breaks or additional resources if they appear distracted.
• Teacher Assistance: Assist teachers in traditional or hybrid classrooms by providing insights into which students are paying attention and which might need additional support.
• Interactive Lectures: Enable interactive responses or changes in teaching methods if a significant number of students are detected as distracted.
• Employee Training: Monitor employee concentration during training sessions, webinars, or virtual meetings to ensure effective learning.

Code

To run this project, you need Python and the necessary libraries installed. The code file is available here for view: https://colab.research.google.com/drive/1pC8k4ZlbnN1QKtHkRYxYWqMAWuw-s2sh?usp=sharing

Contact

• LinkedIn: https://www.linkedin.com/in/siddiquizainab/