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The education sector has long treated every student the same. However, every student is unique and has different learning capabilities. The use of computer vision in education can help to maximize students’ academic output by providing a customized learning experience based on their individual strengths and weaknesses.
Benefits of computer vision in education
The main advantage of computer vision in education is the ease and non-obstructiveness of the assessment process compared to traditional classroom education. Teachers can observe whether a pupil is motivated or disinterested in the class without interrupting their activities. Affective computing techniques, availability of low-cost cameras, and their widespread use in electronic devices like cell phones, computers, and tablets, allow educators to measure learners’ engagement levels using computer vision. Monitoring a student’s behavior continually is not possible. Computer vision eliminates the need for a teacher to continually monitor every student’s behavior, which proves to be highly beneficial. With computer vision technology, every student’s engagement in the classroom can be monitored easily. There is hope that computer vision will enhance the ability to detect, measure, and respond to individual students’ levels of engagement in the near future.
Uses of computer vision in education
Here’s how the use of computer vision will help transform education:
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Improve digital teaching methods
The world is coming closer due to the internet. People can easily search for any topic they might be interested in and find relevant information at their fingertips within seconds. Expanding on this, users can opt for any course or skill-oriented program from a variety of courses available online. People are not restricted when it comes to learning about something they are interested in. Capitalizing on the demand for online learning, many massively open online course (MOOC) platforms are on the rise. Many reputed institutes now offer online certification courses in addition to standard college courses. These online training courses provide audio and video lectures to the students, along with digital materials such as e-books for learning. These courses almost provide a near real-world classroom experience for the students. Students can learn from these courses as per their schedule and do not have a fear of missing out on important lectures. These courses are usually opted by students who can’t commit full-time due to full-time employment or enrolment in a different course. Online learning provides an excellent opportunity for such students who want to learn an additional skillset.
The only major drawback of these courses is that there is no direct real-time interaction and feedback between the teacher and student. Low student engagement continues to be a significant concern faced by educators in online learning contexts. Research shows that students opting for digital courses suffer from low levels of engagement and ultimately give up the course without completion. The completion rates for online courses or MOOCs are as low as 7%. This is a huge concern for educators as well as for the students. Self-reporting of doubts and unclear study material is hardly exercised by students taking these courses. It is a tedious process and doesn’t produce the desired results.
The use of computer vision can help overcome this problem. With computer vision, educators can analyze user behavior, eye movement and posture to assess engagement levels. This helps educators study their students’ behavior as to which sections were engaging and which made the student uninterested. Thus, they can provide engaging courses, which require consistent interaction to garner attention from the user. As new technologies enable us to record and collect data on everything from posture, eye movement, and even facial expression, self-reporting is no longer the only or even the best way to measure engagement.
Improve traditional teaching methods
As with online courses, computer vision can be helpful to track student engagement in traditional classrooms. Analyzing students’ postures and behaviors and eye-tracking can help monitor their interest level and attentiveness. By studying this data, teachers can analyze the behavior of every individual student and measure the spikes and slides in their interest level at every point in time. Educators can modify their teaching methods to garner attention from maximum students and maximize their interest. This helps develop an open interaction between the teachers and students. Teachers can gauge and understand student reaction to their teaching methods and ask students to provide genuine feedback which can be compared with the data collected with computer vision. This data can then be utilized to improve the teaching methods and provide customized courses and materials for students according to their understanding capabilities.
Computer vision is also helpful in improving cooperation between students. Teachers can place students into groups where they are most comfortable interacting and sharing their ideas and opinions. Introverted students can benefit significantly as they may be uncomfortable among large crowds and around loud, extroverted people. Arranging students according to their personalities helps in the growth of student as they are at their comfort level. Students can share their ideas and suggestions and discuss their doubts, if they have any, freely with other students who are a close match to their own personality and can easily interact with each other. Computer vision analysis help study behavior and interaction during diverse group tasks, how students teach others, and how comfortable they are with fellow students. This optimizes peer-to-peer interaction between students when teachers group students according to their comfort levels. Computer vision can offer valuable insights into students’ posture, their facial orientation, or gesticulation during the team activities, by recording all students’ interactions.
Concerns regarding the use of computer vision in education
The use of computer vision in education has been controversial and debatable. The ethics of placing students under continuous surveillance has long been debated upon. There is also the argument that the technology itself may not yet be ready to be implemented widely. The facial recognition tools used for computer vision are currently quite susceptible to errors and report higher error rates for both women and people of color. Concerning online courses, there is the privacy issue of user data being used without their consent. The amount of data and duration for which the website can monitor the user’s movements is highly debatable. The facial recognition data being used for other illegal purposes, and the possibility of it being sold or hacked by a third party is also a major concern. Stringent security measures need to be adopted by institutions implementing computer vision technology to protect user data from being misused. Not every institution can implement such strong security measures as it requires capital investment.
The use of computer vision in education is not without its limitations. However, these issues can be resolved with further improvements in the technology. Companies that deal in edtech can leverage the benefits of computer vision to create an engaging and intuitive atmosphere at educational institutes. The use of computer vision technology can mutually benefit the parties involved by making learning more satisfying, productive, and, most importantly, fun.
