Leveraging Neural Networks for Enhanced Online Education

In today’s fast-paced digital world, neural networks are revolutionizing every field they touch, and education is no exception. Imagine having a technologically advanced assistant that helps you navigate your academic journey with ease—that’s precisely what the future holds. One prime example is large language models (LLMs), which have carved out niches for themselves in education under the term LLM4EDU. These intelligent systems are reshaping the educational landscape in ways previously unimaginable.

Here’s a glimpse of how LLM4EDU is making waves in various educational activities:

1. Virtual Experiments: Conduct science experiments in a simulated environment, ensuring no logistical or safety concerns.
2. Exam Preparation: Receive tailored study plans and instant feedback to excel in your exams.
3. Communication & Translation: Break language barriers with real-time translations and smooth communication.
4. Educational Content Creation: Generate high-quality content that keeps students engaged.
5. Career Planning: Receive personalized career advice based on your strengths and interests.

But the potential of neural networks doesn’t end there. At ProgKids, an online programming school, we realized the need for a more nuanced approach to analyzing the quality of our online education. Traditional methods, such as comparing student answers to standard solutions and tracking metrics like completion rates and attendance statistics, have their merits. However, these methods fall short of providing a comprehensive picture of students’ progress, leaving teachers, parents, and course developers in the dark about students’ true learning experiences.

To fill this gap, we turned to automatic systems leveraging machine learning. We designed an advanced system that evaluates engagement levels during video conferencing, emotional states, and various other parameters through a combination of audio and video analysis modules. Despite the technical intricacies involved, building this system in today’s tech-savvy era is surprisingly straightforward.

Want to see for yourself? Let’s dive into a hands-on example where we’ll analyze a video.

First, we detect faces and gaze angles using the PyGaze library. This process is as simple as running a few lines of code:

# Import necessary packages
# Please make sure to install the package pygaze using pip if not already installed.
# !pip3 install pygaze

from pygaze import PyGaze, PyGazeRenderer  
import cv2  # Import OpenCV for image handling

# Initialize the PyGaze object
pg = PyGaze()

# Read the image from file
image = cv2.imread("test.jpg")  # Specify the correct path to the image file if necessary

# Check if the image has been loaded correctly
if image is None:
    print("Image not found. Please check the file path.")
else:
    # Use PyGaze to make predictions on the image
    predictions = pg.predict(image)

    # Print out the predictions
    print(predictions)

Next, we use DeepFace to recognize emotions, which provides a comprehensive image analysis unlike any other. Here’s how you can get started:

# Import necessary packages
# Please make sure to install the package deepFace using pip if not already installed.
# !pip3 install deepface

import cv2  # Import OpenCV for image handling
import matplotlib.pyplot as plt  # Import Matplotlib for displaying images
from deepface import DeepFace  # Import DeepFace for facial analysis

# Path to the image file
img_path = 'test.jpg'

# Read the image from the specified path
image = cv2.imread(img_path)

# Check if the image has been loaded correctly
if image is None:
    print("Image not found. Please check the file path.")
else:
    # Display the image using Matplotlib
    # OpenCV reads images in BGR format, while Matplotlib displays them in RGB format
    plt.imshow(image[:, :, ::-1])  # ::-1 reorders the channels from BGR to RGB
    plt.axis('off')  # Hide the axis
    plt.show()  # Display the image

    # Analyze the image using DeepFace
    # This function returns a dictionary with various attributes like age, gender, emotion, etc.
    analysis = DeepFace.analyze(img_path)

    # Print out the analysis results
    print("Analysis Results:", analysis)

Finally, we divide a video into frames and scrutinize each one individually. The imageio library comes in handy for this task:

# Import necessary packages
# Please make sure to install the package imageio with the pyav plugin using pip if not already installed.
# !pip3 install imageio[pyav]

import imageio.v3 as iio  # Import imageio for video reading
from deepface import DeepFace  # Import DeepFace for facial analysis
from pygaze import PyGaze, PyGazeRenderer  # Import PyGaze and PyGazeRenderer for gaze analysis

# Initialize the PyGaze object
pg = PyGaze()

# Path to the video file
video_file_path = "/path/to/your/video/file.mp4"  # Replace with the correct path to your video file

# Iterate over video frames using imageio
for i, frame in enumerate(iio.imiter(video_file_path, plugin="pyav")):
    try:
        # DeepFace analysis on the current frame
        deepface_analysis = DeepFace.analyze(frame)

        # PyGaze analysis on the current frame
        pygaze_analysis = pg.predict(frame)

        # Print or handle the analysis results
        print(f"Frame {i}:")
        print("DeepFace analysis:", deepface_analysis)  # Print DeepFace analysis results
        print("PyGaze analysis:", pygaze_analysis)  # Print PyGaze analysis results

    except Exception as e:
        # Handle exceptions (e.g., analysis errors on the current frame)
        print(f"Error analyzing frame {i}: {e}")

With these tools at our disposal, neural networks are poised to make education not just better but also safer and more attuned to the genuine needs and interests of students. Imagine personalized learning journeys, real-time emotional support, and a curriculum that evolves as you grow. The future of online education is here, and it’s more exciting than ever!