One of the fastest-growing transformative technologies, Computer Vision has far-reaching applications in all sectors, including education.
What is Computer Vision?
Computer Vision is understood as the ability of a machine to assess and analyze images and derive information from them to further make decisions.
It is a subfield of artificial intelligence that makes sense of any visual input - like videos and images. It then decides what their properties look like (much like human vision) and learn how to use that information to understand any environment better.
Over the years, applications of AI, and its subfield computer vision, have seen a steep rise.
According to Bernard Marr, best-selling author, technology advisor and contributor for Forbes, computer vision is expected to reach USD 48.6 billion by 2022.
Several companies like Tooliqa, Cipia, Orbital Insight and Standard Cognition make use of the several applications of Computer Vision to deliver their services in design, automotive, geospatial analytics and retail industries respectively.
Tooliqa is using Computer Vision and AI to create ultramodern tools that aid in disruptive innovation.
Why would we need to apply computer vision in education?
There is no denying that there is a growing dependence on technology for all our needs today- the pandemic has proved that to us.
Post-pandemic education has turned virtual in the form of online classes, distance learning courses and other hybrid formats.
We need to keep up with changing times, particularly in the field of education, where there is innovation and discovery each day. Given the advancements in artificial intelligence and its subfields, it would be a massive mistake to ignore the incredible potential these technological fields offer.
Another reason for educational institutions and independent educators to consider incorporating computer vision is to make teaching more student oriented. Using the crucial data provided by computer vision systems, educators can design each class to be more interactive, improving students’ moods, attendance and overall grades.
What are the applications of computer vision in education?
Possibly, one of the biggest reasons why Computer Vision is quickly growing popular in this sector is because of how there is no need for continuous intervention by human beings.
Using computer vision can help educational institutions tackle any persistent management difficulties and prevent any errors that can creep in while manually collecting data.
Here are some of the applications of computer vision in education:
- Face detection for attendance tracking
Gone are the days when manually noting down the names of the students present in class was the norm. Biometric systems seem to be time-consuming for fast-moving technology too, therefore a more optimized method of tracking attendance is to incorporate computer vision.
In October 2017, several Chinese news outlets reported that a professor at the Communication University in China adopted this method. Students would have their photographs taken by his tablet computer. It would then match the images with the college’s student database for easy attendance tracking.
This system was based on Baidu’s open AI platform.
Using computer vision through facial recognition systems reduces the teacher's workload to collect attendance and manually input that information into the college’s attendance tracker later. It also greatly reduces the time it would take to get to the end of the list, especially if there are larger class sizes.
- Better conduction of online exams
Many schools and colleges are yet to completely phase-out of the online mode of instruction. They are either opting for a hybrid model or offering students the option to attend offline or online. Due to the large class size, many colleges are leaning towards online examinations to rescue the risk of virus transmission.
Incorporating computer vision in this respect allows them to conduct these online exams by better-proctoring methods.
An examiner doesn’t have to be seated behind a camera at all times to monitor students taking the exams.
It is an unobtrusive method that allows students to write their exams uninterruptedly without the worry of being constantly monitored.
It is also cost-effective as all one needs is a computer system, a working microphone, and a working video camera.
This can also help reduce instances of fraud during exams by checking for suspicious movements or facial expressions. The recorded footage can serve as proof in instances where students have been caught cheating.
- Measuring engagement levels and other behaviors by checking expressions
This has added importance especially considering how the pandemic pushed most urban and semi-urban schools and colleges to shift to an online mode of learning. Distance education then became the norm.
Before the pandemic, educators could rely on their visual ability to identify if students were paying attention or were as motivated in a class by noticing if they were looking distracted or confused or were slouching or yawning.
Once the shift to online classes occurred, tracking individual attention or motivation levels through each student’s expression and posture was simply not possible.
With an intervention by computer vision mechanisms, instructors and educators can collect real-time data on individual behavior. They can then use this data to refine their teaching methods to suit their students’ needs and provide personalized feedback.
This can boost students’ confidence levels, positively impact their grades and improve student-instructor relationships as well.
This was found to have promising results in a study conducted by M. Ali Akber Dewan, Mahbub Murshed and Fuhua Lin in 2019. They found that computer vision was a non-intrusive and inexpensive method to collect useful data about students’ engagement levels.
- Campus security and compliance with rules and regulations
Through face recognition systems, colleges can also monitor if their students are complying with the social distancing measures. If connected to security systems, they can trigger automatic sound alerts if crowds gather on the campus premises.
Especially because schools and colleges tend to be very crowded, it is important to check for unmasked people.
This can be done easily using computer vision.
For better protection of property, colleges can also prevent acts of vandalism using computer vision-based people detection surveillance systems. They can detect suspicious behavior and immediately alert security guards or on-site personnel. Facial recognition systems can also track repeat offenders.
All that colleges need for this are inexpensive surveillance cameras that can be used for crowd monitoring.
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