How does machine vision boost education of the future?

Introduction

Technology is entering the classroom and establishing itself as a common topic of study in computer science and engineering. Machine vision carries considerable weight in the field of education and is gradually becoming a crucial component of instructional technology around the globe. It is reported that over the next five years, there will be an explosive increase in the uptake of machine vision technology in education. On the basis of a deep-dive report of Holon IQ, it writes that by 2025, global investment in this specific application scenario is estimated to reach $6 billion. China will spearhead the growth, followed by the United States, which together will account for more than half of worldwide expenditure on the education sector driven by machine vision.

The influx of Edtech tools is changing classrooms in a variety of ways. By providing a tailored learning experience based on their strengths and shortcomings, computer vision in education can help maximize students' academic output. In comparison to traditional classroom instruction, it can also be beneficial to make the assessment process easier and less obstructive. The availability of inexpensive cameras and their widespread use in electronic devices like mobile phones and laptops, along with the technology's capacity to comprehend acquired digital images, allows educators to gauge students' interest.

Benefits of computer vision in education

Over the past decade, digital disruption has altered the dynamics of each industry to increase Internet usage. Systems for online learning are also growing with time. Tremendous improvements from smart classrooms to digital classrooms enhance the possibilities of computer vision in the future learning mode.

As we can see, the potential for scalable individualized learning has played an important role in the Edtech industry’s ascendance. The charm of the one-size-fits-all solution is fading away while education upholding the principle of inclusiveness and comprehensiveness is in blossom. Through monitoring a student’s behavior in an online classroom and developing more specialized filters on their knowledge background, strengths and limitations, machine vision in education can foster a sense of diversification. Teachers can examine their teaching strategies to better understand pupils based on the feedback from intelligent systems in order to boost the learning capabilities of all learners. Conventional classrooms integrated with smart systems facilitate supervision of students’ words and conducts by facial recognition supported by machine vision as well. In addition to sophisticated teaching methodology, machine vision strengthens interactions between students and teachers. Students are grouped based on their preferences and interests to boost the likelihood that they will actively engage in peer-to-peer dialogues.

Since the computer education is still in its infancy, the progress of instructional technology may lay a solid foundation for the implementation of a more advanced mode of education. The main objective is to create a sound atmosphere of cooperation and participation between teachers and learners. Industry sectors like hospitality, manufacturing and healthcare are adopting functional prototypes of virtual classrooms to instruct students, interns and staff members in certain disciplines for skill development.

According to the report published by HolonIQ in May 2019, “Generally, the applications of machine vision in education have been categorized into five areas that help map the underlying technology to specific use-cases, from the spectrum of Vision, Voice to Natural Language, Algorithms, Hardware.” The practical uses are face detection, attendance management, chat-box solution for Q&A, plagiarism detection, personalized learning pathways and machine-vision-based smart devices on campus.

Computer vision system serve myriad benefits to the education sector. Let’s have a look at its use cases:

-Engagement detection and enhancement

Greatly affected by the global pandemic, strict measures of quarantine and social distancing is taking effect. All kinds of instructional organizations, schools of all grades, educational institutes, tutoring centers, are faced with being shut down and resorting to the online mode of learning. Online learners attend various types of subjects like what they have to do at school, including reading, online exams, online meetings and so on. Engagement level varies among students, so some of them may show frustration, boredom, amusement, or confusion. Thereby, it is essential for online educators to perform effective interventions to pupils through pedagogical support. The energy of educators is limited though, educational technology driven by machine vision is make this a breeze. Distance learning platforms could collect real-time data on students’ behavior via computer vision, for instance, eyeball movement, body language (slouching or sitting up), and facial expression (yawning, frowning and squinting eyes). Teachers can accordingly adjust their methodology hinged on the data set collected, like recommending reference readings comparable to students’ capabilities or establishing subgroups of learners with the same interest.

-Automated proctoring

With the advancement of machine-vision-based instructional technology, more and more educational organizations see the necessity to carry out automated proctoring. In today’s teaching and learning environment, the examination is a crucial indicator to test students’ short-term or long-term academic performance and also serves as a crucial barometer of the effectiveness of instruction. However, proctoring is not a piece of cake, which is repeated tasks that put a lot of pressure on the markers and consume their time and energy. The machine-vision-based proctoring system makes use of character recognition, image processing to mark and correct candidates’ handwritten responses to easy questions, including multiple-choice questions, judgment questions and calculation questions, count the scores of candidates' test paper, and count and rank the scores of all candidates.

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