DEV Community: ProgKids

Elevating Online Education Quality with Artificial Intelligence at ProgKids

ProgKids — Tue, 20 Aug 2024 07:54:32 +0000

In recent years, online education has morphed from an experimental novelty to a mainstream educational tool, witnessing explosive growth. Since 2000, the online learning market has surged by an astonishing 900%. By 2024, it is expected to generate revenues of $185.20 billion, and by 2028, this number is projected to skyrocket to $257.70 billion, with an annual growth rate of 8.61%. More impressively, by the end of 2028, online learning platforms are expected to engage 1 billion users worldwide.

The transition to online education offers numerous advantages over traditional methods. Flexibility, convenience, self-paced learning, and accessibility are just a few examples. This mode of learning enhances control over the educational environment. However, like every coin has two sides, online education also faces challenges. Particularly daunting is the task of monitoring progress and quality, given the vast number of users and the physical separation between teachers and students.

Ensuring high-quality education remains a paramount priority, aligning with the United Nations Sustainable Development Goals. Therefore, educational organizations must maintain rigorous oversight of their educational processes.

Traditionally, quality assessment in online education has relied on direct comparisons of student responses with desired answers (tests, assignments) and evaluating educational metrics (return rates, Completion Rates, Success Rates, attendance statistics). Although effective, these methods fall short in systematically informing educators and course developers about student progress.

This gap has driven a surge in the adoption of automated feedback systems and quality analysis based on machine learning techniques. At ProgKids, we're at the forefront of this innovative wave.

The ProgKids Approach

At ProgKids, our programming school leverages an advanced system to analyze the engagement of both teachers and students, an essential indicator of educational quality. This system employs a series of Docker-based services structured as follows:

User API—developed using Flask.
Audio Engagement Analysis Module:
- File Processor—validates uploaded audio files and converts them into the required format using ffmpeg.
- Speech Recognition Module—utilizes a modified version of SOVA ASR.
- Emotion Analysis Module—uses the SpeechBrain model to detect emotions in audio.
- Transcript Analysis Module—analyzes the recognized text.
Video Engagement Analysis Module:
- Face Detection Module—identifies faces in video.
- Gaze Detection Module—determines the direction of the participant's gaze.

Our services adhere to a microservice architectural style, ensuring a service-oriented structure with loosely coupled, easily modifiable modules that interact via API.

Deliverables of the System

Upon completion of the analysis, our system provides:

Speech Analysis:
- Presence of pauses, their count and duration, list of filler words with start times, and lists of polite and impolite words.
Video Analysis:
- Absence of the participant's face and duration of absence.
- Instances of looking away and the duration of such distractions.

The Impact

Implemented at ProgKids since December 2022, this system has significantly boosted our student completion rates. By identifying decreased engagement, we can promptly inform educators, prompting them to enhance student involvement, or if necessary, replace the instructor.

Future Potential

Our work in this arena demonstrates significant potential for providing invaluable insights. The data collected underscores the effectiveness of our proposed quality assessment strategy. This approach not only aids in maintaining high educational standards but also helps predict student dropout rates, steering the future of online education toward greater success.

Elevating Online Education Quality with Artificial Intelligence at ProgKids

ProgKids — Tue, 20 Aug 2024 07:54:21 +0000

This gap has driven a surge in the adoption of automated feedback systems and quality analysis based on machine learning techniques. At ProgKids, we're at the forefront of this innovative wave.

The ProgKids Approach

User API—developed using Flask.
Audio Engagement Analysis Module:
- File Processor—validates uploaded audio files and converts them into the required format using ffmpeg.
- Speech Recognition Module—utilizes a modified version of SOVA ASR.
- Emotion Analysis Module—uses the SpeechBrain model to detect emotions in audio.
- Transcript Analysis Module—analyzes the recognized text.
Video Engagement Analysis Module:
- Face Detection Module—identifies faces in video.
- Gaze Detection Module—determines the direction of the participant's gaze.

Our services adhere to a microservice architectural style, ensuring a service-oriented structure with loosely coupled, easily modifiable modules that interact via API.

Deliverables of the System

Upon completion of the analysis, our system provides:

Speech Analysis:
- Presence of pauses, their count and duration, list of filler words with start times, and lists of polite and impolite words.
Video Analysis:
- Absence of the participant's face and duration of absence.
- Instances of looking away and the duration of such distractions.

The Impact

Future Potential

Iterative Software Development for ProgKids Video Conferencing

ProgKids — Mon, 19 Aug 2024 13:38:49 +0000

With the widespread adoption of online education among developers of remote educational platforms, there is a growing need to enhance existing software versions to improve the quality of remote lessons and better control learning progress and efficiency.

These needs prompted ProgKids LLC to refine the ProgKidsMeet video conferencing module and to scale the ProgKids children's educational platform to the Russian and Asia-Pacific markets.

As part of a grant, and according to specified technical parameters, the video conferencing module was improved. Additionally, a lesson analysis system based on machine learning methods was developed, which utilizes video and audio recognition technologies for analysis.

The audio recognition technology allows the system to detect the emotions of both the student and the teacher and to use the transcribed text for further analysis. This way, the system can provide feedback to the teacher on how the student perceived the lesson and identify areas needing improvement.

The video recognition technology is used to analyze recorded lessons to determine the student's participation activity and engagement during the lesson.

Thus, the relevance of this system lies in its ability to help teachers improve their teaching methods, enhance the quality of online education, and optimize the learning process.

The Agile software development methodology was chosen for this project, which involves using iterative and incremental approaches in product creation.

Within Agile development, the project is divided into smaller iterations, each including planning, development, testing, and product improvement. This approach enables the team to receive feedback faster and make adjustments, leading to higher quality and better customer satisfaction.

For managing the workflow, the Scrum methodology was chosen—one of the most popular Agile methodologies. In Scrum, the development team works in short periods, typically between 2 to 4 weeks, called sprints. During each sprint, the team develops new functionality and looks for ways to improve the product. At the end of each sprint, the team conducts a review and retrospective to assess the completed work and determine what can be improved in the next sprint.

The development team used the Scrum approach and worked in one-week sprints. During weekly work calls, the team planned tasks for the next sprint by defining development requirements and setting priorities. The team used a task list, called a backlog, which contained descriptions of all tasks necessary to achieve the project goals.

Throughout the sprint, the team developed new functionality defined in the weekly work calls and tested it to ensure it met the requirements. The team adhered to Scrum principles to ensure quality and timely completion of work, as well as continuous communication and interaction among team members.

At the end of each sprint, the team conducted a review to evaluate the work done, presenting the new functionality to all project participants, and a retrospective to identify what was done well and what could be improved. These events helped the team enhance product quality, increase work efficiency, and improve interaction among team members. This approach allowed the team to work more productively, accelerate the development process, achieve quick results, and ensure higher product quality and greater customer satisfaction. In the Scrum methodology, the task board is one of the key tools for project management. It is a physical or virtual board displaying all tasks needed to achieve project goals. The task board is used to visualize the status of tasks and control their execution.

In the project, the board was divided into four columns: "Submitted, Ready For Dev," "In Progress," "In Testing," and "Wait For Release." Each task was represented by a card indicating its title, description, and status. Cards were moved across columns according to their current state.

The development team used the task board to plan and manage their work. During weekly work calls, the team planned tasks for the next sprint and added them to the task board in the "Submitted, Ready For Dev" column. Throughout the sprint, the team moved task cards to the "In Progress," "In Testing," and "Wait For Release" columns, reflecting the current state of work. The task board made it easy for the team to track progress and manage priorities. Additionally, it fostered communication and collaboration within the team, as every team member could see which tasks were being worked on and what issues arose during the process.

YouTrack Platform

The development team uses the task board to plan and manage their work. During weekly work calls, the team plans tasks for the next sprint and adds them to the task board in the "Submitted, Ready For Dev" column. Throughout the sprint, the team moves task cards to the "In Progress," "In Testing," and "Wait For Release" columns, reflecting the current state of work. The task board allows the team to easily track progress and manage priorities. Additionally, it fosters communication and collaboration within the team, as every team member can see which tasks are being worked on and what issues arise during the process.

The YouTrack platform was chosen as the task board for several reasons:

High Functionality.
YouTrack offers a wide range of features for task and project management, including bug tracking, reporting, and analytics. This is useful for a team working under the Scrum methodology and using task management principles, such as the backlog, sprint task list, etc.
Ease of Use.
YouTrack has a simple and intuitive interface that can be easily customized to the team's needs. This helps accelerate the work process and reduce the likelihood of errors.
Integration.
YouTrack can be easily integrated with other tools used by the development team, such as version control systems (e.g., Git), continuous integration services (e.g., Jenkins), and other project management tools. This simplifies project management and ensures more effective interaction among team members.
Scalability.
YouTrack is suitable not only for small projects but also for large ones, as it supports multi-user access and can handle large volumes of data. This facilitates the management of large projects and ensures coordinated effort among participants.

Thus, the choice of YouTrack was based on its wide range of functionalities, ease of use, integration capabilities with other tools, and scalability, which facilitated project management within the Scrum methodology and increased the team's productivity.

Innovating Virtual Classrooms: PROGKIDS LLC Improves Remote Learning with Custom Video Conferencing

ProgKids — Mon, 19 Aug 2024 13:16:43 +0000

The video conferencing landscape is rapidly evolving, characterized by constant functional upgrades, the integration of cutting-edge technologies, and a relentless pursuit of higher efficiency and quality. Amidst this dynamic environment, price wars and enhancements in service quality also play crucial roles.

The challenges faced by online educational platforms in organizing and managing remote lessons served as the catalyst for PROGKIDS LLC to embark on an ambitious project. By leveraging grant funding, the team re-engineered their existing ProgKidsMeet video conferencing module and aimed to scale the ProgKids educational platform across the Russian and Asia-Pacific markets.

Project Aspirations: Crafting a Next-Gen Virtual Classroom

The centerpiece of this initiative was the creation of a bespoke video conferencing tool tailored for the ProgKids educational platform. This tool needed to provide robust and comprehensive functionalities, including interactive capabilities that foster an engaging learning environment.

Must-Have Features

PROGKIDS LLC meticulously identified a suite of technical features and specifications for its revamped solution, which included:

Seamless Authorization: Integrating with the already established system for smooth user authentication.
Universal Accessibility: Enabling conference participation from any device.
User Roles and Permissions: Defining specific roles and access rights within the system.
High-Quality Sound & Image: Ensuring superior audio and visual communication during conferences.
Multi-Camera Display: Showing feeds from multiple participant cameras.
Flexible Device Settings: Allowing users to adjust microphone, camera, and audio settings.
Screen Sharing: Facilitating easy demonstration of screen content.
Interactive Chat: Providing real-time messaging for all participants.
On-Screen Drawing: Allowing participants to draw on shared screens.
Automatic Recording: Capturing and storing both video and audio of conferences for future use.
Machine Learning Integration: Utilizing AI to analyze recorded sessions.
Robust Admin Panel: Providing comprehensive administrative controls.
System Compatibility: Ensuring seamless interaction with the existing main system.

Quantitative Benchmarks

To measure the effectiveness of the newly developed features, the team set specific quantitative goals:

User Capacity: The system should support up to 50 simultaneous connections, facilitating up to 25 concurrent lessons.
High-Definition Streaming: Ensuring HD quality (720p) for users with internet speeds above 10 Mbps, with automatic quality adjustments for lower speeds.
Smooth Frame Rates: Maintaining 25 frames per second for high-speed connections with adaptive adjustments for lower bandwidth.
Lesson Delivery: Targeting 2,000 to 2,500 lessons per month via the new system upon full implementation.
Minimized Cancellations: Reducing lesson cancellations due to technical issues to just 5-10 per month.
Comprehensive Recording: Automatically recording all lessons, matching the count of delivered sessions to identify between 2,000 and 2,500 recordings per month.

Technical Requirements

The hardware requirements to support the revamped video conferencing suite were also defined:

Server Configuration: A minimum of 8 dedicated CPU cores, 16 GB RAM, 100 GB SSD, and running on Ubuntu 20.04.
Cloud Storage: Starting with a minimum of 1 TB for video recordings.

Transforming Education: The Road Ahead

Integrating cutting-edge technologies like machine learning and automated feedback into online education platforms addresses existing challenges while maximizing the advantages of remote learning. PROGKIDS LLC's initiative to develop a specialized video conferencing module exemplifies a forward-thinking approach to enhancing online education quality and accessibility.

As virtual learning continues to evolve, the emphasis on creating robust, scalable, and efficient systems will be crucial. This project not only propels the ProgKids platform to the vanguard of educational innovation but also sets a new standard for the future of digital education. By pushing the boundaries of what online learning can achieve, PROGKIDS LLC is paving the way for remote education to potentially surpass traditional methods in effectiveness and engagement.

This initiative firmly places ProgKids at the forefront of educational technology, heralding a new era of interactive and efficient virtual learning.

Leveraging Neural Networks for Enhanced Online Education

ProgKids — Mon, 19 Aug 2024 12:43:09 +0000

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:

Virtual Experiments: Conduct science experiments in a simulated environment, ensuring no logistical or safety concerns.
Exam Preparation: Receive tailored study plans and instant feedback to excel in your exams.
Communication & Translation: Break language barriers with real-time translations and smooth communication.
Educational Content Creation: Generate high-quality content that keeps students engaged.
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!