THE AUTHOR:
Education nowadays is turning into a focus and becoming increasingly more technology savvy to suit the developing needs of learners, educators, and other administrators. In these technologies, computer vision is a very prominent one that has revolutionised the field of application for both K-12 and higher learning intuitions.
What is it, in fact, that computer vision brings to teaching and learning? Why do people invest in it?
Computer vision is a rapidly developing field that has witnessed exceptionally high levels of growth in the last five years.
Computer vision use cases have spread across many fields. In this article, we’ll present the future of computer vision in education, its opportunities, challenges and considerations as a tool for institutions interested in integrating and implementing the technology into their settings.
Global Investment and Industry Growth
In recent years, Computer Vision development services have seen substantial adoption across industries, with the education sector showing a particularly strong interest.
Computer vision, a branch of artificial intelligence that enables machines to interpret and process visual information, has proven useful for automating administrative tasks, facilitating personalised learning experiences and improving classroom management.
According to market research, Global investment in computer vision across sectors has grown significantly, with educational applications playing a noteworthy role. According to IDC, spending on AI, including computer vision in education, is projected to grow at over 35% annually.
This rise is fueled by an increasing demand for technology-based solutions that can address specific needs within the sector, such as remote learning support, automated grading, and improved classroom engagement tracking.
Analysing the global computer vision market, we can estimate that the forecasted global growth rate of computer vision remains steady, with AI and machine learning technologies poised to create new opportunities in education.
The compound annual growth rate (CAGR) for the AI market, including computer vision, is expected to reach 37.3%, signalling substantial sector expansion.
Educational institutions and technology providers are investing in computer vision solutions in business. They do this to ensure the school keeps up with the new trends and standards expected of students, parents, and teachers.
Thus, as the volume of such investments increases, the possibilities for constructing education that is optimally progress-minded and efficiently delivered to learners also increase.
Drivers of Computer Vision in Education
Computer vision has found its way into the context of education to address several important issues such as students’ attendance, improvement of organisational activities, and other time-consuming tasks.
Let’s look at some specific factors driving its adoption.
Addressing Educational Needs and Challenges
1. Student Engagement Monitoring: The first major motivation for applying computer vision in education is to track students’ attentiveness during the learning process in real time.
Most conventional approaches to measuring engagement are invariably impressionistic in that they are made from an educationist’s point of view or the feedback given by learners.
On the other hand, computer vision development offers real-time insights into a learner’s emotional state, posture, and eye contact. This data could be helpful for teachers, for instance, allowing them to change classroom dynamics and/or speed.
2. Automating Grading and Assessment: Grading is an integral part of education; however, grading is still tedious. Computer vision can be used to evaluate exams, whereby questions with MCQ or short-answered questions that may be graded differently can be automatically graded.
This saves time for the teachers and adds fairness and impartiality when grading the students’ works.
3. Ensuring Classroom Safety: Due to the new focus on student safety, mainly because of evolving world issues, computer vision solutions are being applied to check classroom occupancy, social distancing, and any other emergent dangers in class. By furnishing real-time alerts, such systems can help administrators make schools more secure for students and workers.
4. Streamlining Attendance and Proctoring: Attendance and test proctoring are critical for face-to-face and online classes, although they create problems in implementation.
This means that computer vision can simplify these processes due to the use of facial recognition to check the student’s identity and the correctness of the attendance record.
Also, it helps support the administrative integrity of online classes, particularly during examinations, by observing students and reporting abnormal behaviour.
Benefits of Computer Vision for Educational Institutions
The advantages of computer vision are clear. Institutions that adopt this technology can expect improvements in areas such as:
• Efficiency: The effective use of technology helps decrease the load on teachers and administrative staff because many routine operations, like grading and attendance, can be performed automatically.
• Insights: Engagement and performance data that the computer vision will produce would enable educators to assess needs and problematic areas, and make decisions based on the findings.
• Savings: While there may be one-off costs of acquiring the required equipment and the right software, it will be evident that computer vision is an efficient solution for educational institutions since it will cut down manual work costs.
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How to Apply Computer Vision in Education
There are different types of computer vision applications where administrative roles are supported, and instructional support is involved.
Here’s a closer look at some of the core applications:
1. Student Behavior and Engagement Analysis
Teachers may not always be able to tell when students are disengaged in a classroom setting. Computer vision in education enables teachers to make decisions in real time based on students’ facial expressions, looks, and even posture.
This is useful for teachers to pinpoint learners who need support, making the class more sensitive to individual needs.
2. Personalised Learning Paths
Educational software solutions can affect how students use educational applications and materials and engage their learning process.
For example, if a student struggles while reading a passage, the system might offer more elaborate definitions or additional readings on the same topic.
Such computer vision technologies assist students in achieving progress as required by different students with different learning capacities.
3. Remote Proctoring and Academic Integrity
As learning becomes more diverse, primarily through online sessions, the tools for remote proctoring remain credible. Hence, computer vision plays a key part in safeguarding the subject matter of online exams since it oversees student actions during the test.
Some of them include re-reading, looking away from the screen frequently, using unauthorised devices, and others, which makes it easier for the institution to set academic standards even with online classes.
4. Classroom Management and Security
Computer vision assists school administrators by observing premises usage, checking compliance with capacity limitations, and indicating potential risks. Emergency solutions are also examples of computer vision solutions because they are highly effective in such systems.
Using computer vision systems, the administration immediately gets an alert in case of a fire outbreak or an intruder and can act accordingly. This improves overall safety standards at educational institutions and hence comes up with better security standards for the students and faculty.
5. Automated Attendance Tracking
Computer vision offers a reliable solution to attendance tracking, ensuring that no student goes unnoticed.
By automatically recognising students as they enter the class, it eliminates the possibility of human error and guarantees accurate and up-to-date attendance records.
Challenges in Adopting Computer Vision in Education
While the benefits of computer vision in education are clear, implementing this technology comes with certain challenges.
Educational institutions considering its adoption need to be aware of these potential hurdles:
1. Data Privacy and Security Concerns
Perhaps the most critical problem of computer vision implementation in education is data privacy. Schools and universities must protect students’ information, including facial data and behavioural information.
Measures should be taken to meet GDPR requirements to safeguard students’ information and build some confidence among parents, educators, and students themselves.
2. Cost of Implementation and Maintenance
Applying computer vision technology can result in long-term cost savings, though it requires a significant initial investment. Items such as cameras and servers used in the system and staff training on how to operate it can quickly become expensive for small institutions.
Also, there are fixed costs for the daily upkeep that these systems may need to be relevant and secondary technical support.
3. Potential Bias in AI Models
AI models in computer vision can unintentionally reflect biases, impacting how student behaviour is interpreted. For instance, an AI system can fail to understand proverbs, festivals, beliefs, traditions, or even practices, causing biases.
Computer science professionals and teachers must make these systems as objective as possible and tweak the parameters involved to accommodate as broad a range of actions and attitudes as possible.
4. Technical and Operational Complexity
The use of computer vision systems entails technical knowledge that may not be available in all educational facilities.
Outsourcing partnerships with an experienced development team is beneficial when dealing with such issues; however, institutions need to be very selective when choosing a provider who has worked with CV applications before to avoid pacing with the problems.
Predictions for Computer Vision in Education
However, in the future, computer vision is expected to become even more critical to education.
We can expect several key developments in the coming years:
1. Integration with Augmented Reality (AR) and Virtual Reality (VR)
When both AR and VR gain public acceptance, their incorporation into computer vision would be beneficial in providing students with virtual experiences.
For example, during a biology lesson, a child could have an opportunity to interact with a model of the human body. While doing so, computer vision would monitor the student’s actions and provide extra help.
Combined, this invention could entirely change the way STEM is taught and prepared, so it becomes more simple to come up with solutions and make certain concepts more concrete and accessible to grasp.
2. Advancements in Adaptive Learning Platforms
Computer vision will likely become more involved in adaptive learning or delivering content based on students’ requirements.
In the future, based on the recorded facial expressions, gaze, and engagement levels, the subsequent adaptive learning platforms could become even more personalised while requiring limited intervention from a human source.
3. Broader Adoption of Remote Proctoring Solutions
As we have seen with student proctoring, institutions will extend the use of remote proctoring tools based on computer vision technology.
These systems are predicted to develop to a level where they will not only be able to detect signs of cheating behaviour but also differentiate between normal cheating behaviour and other symptoms of a student who might require extra help in their academics.
What to Consider When Implementing
Choosing the right development partner is crucial for institutions looking to adopt computer vision. A reputable partner can guide schools through the complexities of implementation, ensuring the system aligns with their specific goals and resources.
Here are some steps and tips for successful adoption:
1. Define Clear Objectives
Institutions should have a clear understanding of what they hope to achieve with computer vision, whether it’s improved engagement tracking, automated grading, or enhanced security.
2. Assess Technology and Infrastructure Needs
Consider the hardware and software requirements, such as high-quality cameras and reliable servers, to ensure smooth operation.
Computer vision systems can be resource-intensive, and institutions should ensure their infrastructure can handle the additional load.
3. Focus on Data Privacy and Compliance
The security of students’ personal information is one of the most important objectives. Institutions should engage their technology vendors to ensure the system meets various privacy laws such as GDPR, FERPA, or any other.
This may include aspects such as the use of a code, access to the data, and how long the data will be kept secure to ensure that clients know the steps to secure it.
4. Prioritise Staff Training and Support
Computer vision technology can be complex to operate and maintain. Several recommendations include getting full-school payouts for the technology to be used to give teachers and other staff members a thorough briefing on the usage of the technology to gain an understanding of how to get maximum data insights, and possible basic hitches in the system.
A good and reliable technical support plan is also necessary when solving problems that may occur.
5. Engage with Stakeholders for Feedback
Therefore, corresponding support and feedback from the stakeholders are crucial for the successful implementation of computer vision. Schools have provided a way of effectively solving people’s complaints and have included students, parents, and teachers.
Schools should ensure that it takes time to ask them how the system works. Incorporating this feedback into ongoing adjustments will help maximise the computer vision benefits.
6. Plan for Scalability and Future Upgrades
Upgrades and expansions of computer vision technology suggest that educational institutions should expect the same.
Promoting a system that could be flexible with growth in student enrollment or in feature development will be helpful in perpetuity.
Exploring Real-Life Computer Vision Use Cases in Computools Projects
Our software development company has led successful projects across various industries, showcasing expertise in computer vision to deliver innovative solutions that drive real-world impact.
One notable example is MeterSnap, a project designed for precise utility meter readings. This solution automates data extraction and analysis through advanced computer vision algorithms, reducing errors and optimising operational efficiency — an ideal computer vision business solution for utilities and smart city applications.
In the Alpha-Pig project, Computools applied computer vision use cases to enhance productivity in the agriculture sector. By using object recognition and analysis tools, the solution empowers farmers to monitor livestock health and environmental conditions accurately, resulting in better management and yield improvement.
Computools’ Drone Analytics among other case studies, is a prime example of how computer vision can transform industries. Here, the technology is applied for high-precision aerial data analysis, benefiting businesses that rely on remote site monitoring and inspections, such as construction and infrastructure.
Our clients reviews exemplify our software development expertise, demonstrating the value of CV in diverse sectors.
The approach to each project reflects Computools’ capacity to create customised solutions across industries — including Edtech, where our Edtech software development services and education software development solutions leverage computer vision to enhance educational experiences, from student engagement to data-driven insights.
Embracing Computer Vision for a New Age of Education
Computer vision has a number of positive implications for educators, students, and school or institution managers. It may enable operational improvements and more learner-sensitive designs through process operations, managing engagement, and making learning safer.
That is why it is critical to approach the use of Computer Vision development services for business purposes as an adoption process, which indeed demands strategising, careful planning, spare attention to privacy issues, and an unending process of improvement.
For institutions implementing computer vision, this is a future where learning is enhanced equally from an efficiency perspective and from the perspectives of flexibility and fun. Therefore, schools and universities can take all these and make this technology valuable for the present and future generations of learners.
As educational institutions continue to explore computer vision, one final question remains: Will this technology be added to the classical model of education, thus changing the face of classroom learning for a couple of generations of students and teachers to come?
From the current rate of adoption and implementation, computer vision positively models the integration of AI in education systems.
Find out how computer vision can transform your education business! Email us at info@computools.com for a free consultation.
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