Neural Networks as a Catalyst for the Evolution of Online Learning

In today’s rapidly evolving digital landscape, neural networks are reshaping nearly every industry they touch—and education is no exception. Imagine having an intelligent, always-available assistant that guides you through your learning journey with ease. This vision is becoming reality thanks to large language models (LLMs), widely adopted in education under the term LLM4EDU. These advanced systems are transforming how we teach, learn, and understand student progress in ways that once seemed impossible.

How LLM4EDU Is Reshaping Learning

Neural network–driven tools are already making a significant impact across many educational activities:

Virtual Experiments: Students can perform science experiments in safe, simulation-based environments with no logistical constraints.

Exam Preparation: Personalized study plans and instant feedback make preparation more effective.

Communication & Translation: Real-time translation and language assistance eliminate communication barriers.

Educational Content Generation: High-quality, engaging learning materials can be created instantly.

Career Guidance: Students receive tailored advice based on their strengths, preferences, and learning patterns.

Yet these applications represent only a fraction of what neural networks can offer.

Why Traditional Evaluation Methods Are Not Enough

At ProgKids, an online programming school, we recognized the need for a deeper and more accurate way to assess the quality of online learning. Standard metrics—such as comparing student answers to sample solutions, tracking attendance, or measuring assignment completion rates—provide useful but limited insights. They fail to reveal what truly matters:
Is the student engaged? Are they confused or frustrated? Are they emotionally comfortable during the lesson?

Without this information, teachers, parents, and curriculum designers lack a complete understanding of the student’s real learning experience.

Our Solution: Automated Machine Learning–Based Quality Assessment

To close this gap, we developed a machine-learning system that evaluates student engagement, emotional state, and other behavioral signals during live video lessons. By combining audio and video analysis modules, the system delivers an enriched view of learner activity—far beyond what traditional metrics can capture.

Despite sounding technically complex, building such a system has become surprisingly accessible thanks to modern open-source libraries. Below is a practical example demonstrating how video analysis works in action.

1. Detecting Faces and Gaze Direction Using PyGaze

from pygaze import PyGaze, PyGazeRenderer
import cv2

pg = PyGaze()
image = cv2.imread("test.jpg")

if image is None:
    print("Image not found. Please check the file path.")
else:
    predictions = pg.predict(image)
    print(predictions)

2. Recognizing Emotions with DeepFace

import cv2
import matplotlib.pyplot as plt
from deepface import DeepFace

img_path = 'test.jpg'
image = cv2.imread(img_path)

if image is None:
    print("Image not found. Please check the file path.")
else:
    plt.imshow(image[:, :, ::-1])
    plt.axis('off')
    plt.show()

    analysis = DeepFace.analyze(img_path)
    print("Analysis Results:", analysis)

3. Analyzing Video Frame by Frame Using imageio

import imageio.v3 as iio
from deepface import DeepFace
from pygaze import PyGaze

pg = PyGaze()
video_file_path = "/path/to/your/video/file.mp4"

for i, frame in enumerate(iio.imiter(video_file_path, plugin="pyav")):
    try:
        deepface_analysis = DeepFace.analyze(frame)
        pygaze_analysis = pg.predict(frame)

        print(f"Frame {i}:")
        print("DeepFace analysis:", deepface_analysis)
        print("PyGaze analysis:", pygaze_analysis)

    except Exception as e:
        print(f"Error analyzing frame {i}: {e}")

The Future of Online Education

With these tools at hand, neural networks are poised to make online learning not only more effective but also more adaptive, safer, and more responsive to the real needs of students. Imagine personalized learning paths, real-time emotional support, and a curriculum that evolves alongside each learner’s progress.

The future of online education isn’t on the horizon—it’s already here, and it’s more inspiring than ever.