Computer Vision In The EdTech Industry
EdTech, which is short for educational technology, has been around in the industry spotlight since the 1960s. It is a specialized branch of computer science that focuses on the use of technology in education. The significance of this industry can be seen in the fact that it is currently worth $8 billion. Experts predict that the EdTech sector could be worth $150 billion by 2026, thanks to various computer vision applications. The significance of computer vision in this industry is growing as the scope of what it can do expands.
During the onset of the coronavirus pandemic, we witnessed the EdTech industry going through rapid change, where traditional teaching methods were being replaced by Virtual Reality (VR) and Augmented Reality (AR) systems. These new technologies provide a more interactive and engaging learning experience for students. Computer vision (CV)—the ability to see through images in the form of numerical data—is the next major step possible because of recent AI advancements.
The computer vision system allows teachers to monitor each student’s progress without having to physically observe them or even see the faces of every particular student. It can also help students in receiving personalized feedback for their learning process. In this article, we discuss the significance of computer vision in the EdTech industry and some use cases of it in the education and teaching process that help create a smarter learning experience for students.
How Does Computer Vision Technology Work In The Education Field?
Computer vision technology almost imitates the working nature of the human brain. The computer vision algorithms we use today were written on the basis of pattern recognition. Computers are provided with a massive amount of visual data such as images, labelled objects, and so on, by using which data computers find patterns. Let’s find out more about how computer vision works:
- Computer vision largely depends upon pattern recognition techniques. It can even self-train and understand visual data.
- The wide availability of data makes the computer visual process more accurate and faster.
- In earlier computer vision applications, machine learning (ML) algorithms were used but now deep learning algorithms have become the best solution.
- Machine learning techniques use a large amount of data and active monitoring of human action to deliver possible accurate results.
- Deep learning algorithms largely rely on neural networks and use examples for solving complex problems.
- In the past, computer vision technology used the Viola Jones face detection algorithm. Now, it uses the newest trending algorithm called convolutional neural networks (CNN).
Challenges Of Computer Vision For EdTech
In recent years, the computer vision industry has been growing exponentially. With the emergence of technologies such as convolutional neural networks and deep learning along with AI/ML and Big Data analytics, computer vision has also become a powerful tool for many enterprises. However, there are still some challenges with computer vision that we will need to overcome to be more efficient in the future. Here are the four most common challenges businesses face while implementing computer vision technology in their business operations.
1. Insufficient Hardware
Implementation of computer vision technology requires both software and hardware for getting better results. Hardware like high-resolution cameras, sensors, and bots are costly. On the other hand, if any of the hardware is not installed properly, it will lead to poor outcomes. In addition to this, some computer vision systems require IoT-enabled sensors. The cost of the hardware requirements and of having reliable and stable cloud computing technology for the CV system make it a bit expensive.
2. Poor Data Quality
For having a successful computer vision system, you will need high-quality, labeled, and annotated datasets. Industries, like the healthcare industry, that use CV technology require high-quality data annotation, or else there can be significant damage. Poor data quality is one reason for the massive tool failure when it came to identifying COVID-19. At the same time, collecting relevant and sufficient data could be more challenging for businesses in modern days. This may lead to a situation where businesses can’t provide enough training data to the CV system.
3. Weak Or Poor Model Development
Having weak or poor planning for creating an ML model will play a crucial role in upsetting an ambitious CV system. Often executives start planning the ML model without having desired targets, which makes it much harder for the data science team to achieve their goals. Also, it may slow businesses from meeting their objectives and cost more by forcing the use of an unrealistic amount of computing power, having a poor performance, etc. To avoid such a situation, businesses should focus on creating a strong project plan with respect to the project scope and gather all relevant data.
4. Time Shortage
Often project managers start predicting the time required for project deployment based on the time that was required for developing a model. However, they fail to consider the time required during the planning phase of the model project. This leads to time delays in the deployment. In addition to the planning, extra time might be taken for setting up the system, configuring it, hardware calibration, data collecting, data cleansing, data labeling, and training and testing the model. These factors play a key role in project delays.
Uses Of Computer Vision In The Education Sector
The presence of computer vision in the EdTech industry connects educators with software developers, designers, and developers who can create or adapt the software to suit their needs by providing an interactive learning environment with features such as simulations and games. In addition to these features, EdTech aims to provide students with individualized instruction plans according to their skill level, to help them reach higher levels by working on the areas they need improvement in. The need to find a balance between Artificial Intelligence and human intelligence has led to some inevitable consequences in the EdTech industry.
Computer vision technology provides a way to get information about one’s surroundings without having to manually search for it by typing keywords into a search bar or look at computer screens all day. This will help people learn about their field of study more easily, without needing to use textbooks and lectures. AI-based CV technology is the hottest trend in the market. This technology keeps evolving and delivers more fascinating things to the educational sector. With the help of this AI-based vision technology, the modern EdTech industry manages to deliver personalized learning experiences to its students. Here is a list of a few applications that use computer vision technology:
- AI-based security for schools
- Real-time indoor mapping
- Intelligent parking management
- Intrusion detection
- AI-based vandalism prevention
- Detecting suspicious objects
- Analyzing facial emotion
- Automated attendance monitoring
- Crowd analysis
The applications listed above are the latest trend in the EdTech industry. Almost all the leading EdTech companies use this kind of tech to gain students’ attention. With the aid of educational software developers, EdTech companies create software and tools that can cope with different pedagogical methods to make things easier for students and educators.
Computer Vision: The Next Frontier In Education
Computer vision is the process of enabling a machine to understand what it is looking at. Convolutional neural networks and algorithms are being used to make real-time decisions, identify objects, and track them in 3D space. The EdTech industry is poised to grow exponentially as companies develop learning materials based on this technology. It is one of the most exciting, and challenging, fields to work in today. To create a successful computer vision system, a deep understanding of algorithms and programming is required.
The demand for computer vision technology is rising, along with the demand for skilled graduates trained in the field. The number of students enrolled in computer science programs has not kept pace with the growing need for talent, and a new generation of students are entering college with little or no experience with robotics and programming.
To keep up with the growing demand for trained professionals, new educational offerings need to be developed that target students who want to pursue a career in computer vision. The number of high-quality tools for training computer vision algorithms has grown significantly in recent years, with platforms such as TensorFlow making machine learning accessible to a wider audience. But there are still major barriers when it comes to educating students about this technology that need to be addressed urgently so we can prepare students for future jobs and tomorrow’s opportunities.
Conclusion
In recent years, the use of computer vision in both commercial and research settings has exploded. As the demand for computer-vision technologies increases, it’s important to be aware of its limitations as well as the contributions that have been made to alleviate these challenges.
