Letter Blocks

Image source: www.pexels.com

The world of academics hasn’t seen many changes since its inception. It has traditionally relied on a human teacher who imparts knowledge to students. However, this methodology has its own set of challenges that needs to be addressed for the education system to grow and evolve. For instance, the current system considers the entire classroom as a unit. However, every student is unique and has different grasping and capabilities. If a student cannot understand the concepts taught by the teacher, they might get uninterested in the subject.

Similarly, teachers are also burdened with teaching a large number of students, with the student-teacher ratio being 17:1 in primary schools in Canada. Then, there are other duties that teachers need to carry out, such as scheduling exams, parents-teacher meets, extra-curricular activities, and so on, that can take a toll on their personal and professional lives.

Similarly, there are many non-teaching administrative activities that are a part of the functioning of educational institutions. These lead to limitations and challenges of their own. The implementation of machine learning for educational institutions is helping overcome these challenges at various places. Let’s have a look at how machine learning is helping educational institutions to get better results in their daily operations.

Personalizing Learning

As mentioned earlier, the educational system has a one-size-fits-all approach to teaching. However, in reality, each student has different learning capabilities. Some students may easily grasp a concept, while others may face difficulties in the same. Similarly, one teaching style can seem highly engaging to half the students, while the other half may find it boring and disengaging. Thus, not every student learns the same way and on the same level in a classroom. This, eventually, then results in certain students scoring low in the exams, while some even dropping out of the course. What educational institutions need is an adaptive teaching method that simplifies the teaching process based on the student’s capabilities.

The use of machine learning for educational institutions can help overcome all these challenges through personalized learning. Firstly, in the form of learning analytics, machine learning can provide detailed insights regarding student learning. Computer vision tools powered by machine learning technology can be used to monitor and analyze student behaviour in the classroom. The tools can be used to gain insights about how students consume the information provided by the teacher. It can help analyze areas of disinterest, based on which teachers can make adjustments to their teaching methods.

Similarly, machine learning, with data mining, can be used for predictive analysis, which can help reduce dropout rates. The tool can predict the future performance of the students based on historical data. If there are any worrisome signs, the teachers and parents can intervene to provide assistance to the student.

Carnegie Learning’s Mika software is an AI-based tutoring tool that provides personalized guidance to post-secondary students. It helps fill learning gaps usually seen in classrooms with a high volume of students. The app tracks the students’ unique learning processes and adapts lessons accordingly.

Similar to Mika, there’s Thinkster Math, a math tutoring program with a personalized teaching style. The app analyzes the answers provided by the student to determine where the student went wrong or missed an important step. The app also facilitates personalized learning through video assistance and providing immediate, personalized feedback.

Moreover, there are platforms such as NewsClassrooms and MyEdMatch that match teachers with students to provide personalized online learning experiences. These platforms help students find teachers that are best suited to their learning style.

Testive is an online tool that provides personalized SAT/ACT online tutoring. The software adapts according to the student’s strengths and weaknesses, ensuring that the student has a learning experience best suited to them.

Knewton provides personalized adaptive content by analyzing the real-time performance of the student and modifies teaching methods and curriculum based on it. The platform allows schools, publishers, and developers to provide personalized learning content to students.

Hamilton County leveraged IBM’s predictive analytics software SPSS to find hidden patterns in student performances, past and present, that helped identify at-risk students and increase graduation rates by ten percent.

Simplifying Grading Systems

Schoolchild solving elementary science test

Image source: www.pexels.com

Grading students’ answer sheets is a time-consuming process, especially if you need to evaluate a large number of students. It can also lead to errors due to prolonged sessions and inconsistencies and favouritism due to human bias. These challenges can be solved by implementing an automated, unbiased solution for assessing student responses and grading them.

Implementing machine learning for educational institutions offers the perfect solution for student grading system woes faced by teachers. With machine learning tools, teachers can scan the students’ answer sheets, and the ML-based tool can intelligently and automatically analyze the answers. It can then provide the grades based on the responses provided, without any error or bias, in a short period of time.

Gradescope, a machine learning tool developed by John Hopkins University, automates the process of assessment, making it time-efficient and error-free. It uses machine learning techniques to scan and analyze student responses and then grade them. It also provides detailed per-question and per-rubric statistics that can help teachers understand their students’ performance.

Copyscape and Turnitin are online tools that help identify plagiarized content and ensure that the content submitted by students is original. Teachers can easily run a plagiarism check on the software before beginning the answer evaluation process. If any plagiarism is detected, they can reject the submission outright, helping save time and effort. While Copyscape is used more at the enterprise and business levels, Turnitin is the preferred choice when it comes to academics.

Managing Schedules

Note Paper, Post It, Push Pins, Labels, Paper, Office

Image source: www.pixabay.com

Teachers spend a substantial amount of time in non-teaching activities such as lecture content creation, administrative tasks, classroom management, and scheduling. Machine learning tools can help educators become more efficient by taking over these tasks. The algorithms can analyze the daily schedule of the teacher and automatically plan appointments for other tasks.

Similarly, machine learning can help students automatically book a teacher’s appointment, in case they have doubts regarding the subject or other issues they want to talk about. The software can show the students the teachers’ availability, based on which they can request an appointment within the system. Once the teacher accepts the request, the time slot gets booked for the particular student. Thus, students and teachers can save a huge amount of time managing their daily academic schedule with the help of machine learning tools.

Conducting Exams

Machine learning tools can be used for conducting exams online and in the classroom. Computer vision tools with machine learning capabilities can be used as invigilators to ensure that the exams are conducted fairly. The tools can be used to monitor the students’ movement, body language, and other activities to identify instances of cheating during a classroom test. The same tools can be used to monitor online examinations with the help of the users’ webcam and microphone.

Machine learning tools can also be used for automating the exam paper creation process. Machine learning solutions can analyze historical data to generate new question papers autonomously. Once a question bank is uploaded, the tool can create multiple sets of question papers. This further helps reduce instances of exam fraud as students will have a different set of questions to answer.

AutoProctor is an online tool that monitors online tests. The tool allows the test administrator to set a timer for the test to ensure that every student has the same test duration. The tool also leverages the user’s webcam and microphone to detect the face and the environment audio to ensure that the user doesn’t cheat during the test. If the tool isn’t able to detect the user’s face at any instance, it captures the same and reports for violations. Similarly, it listens for other people’s voices, and if it detects any noise, it reports the same as a violation. Additionally, the tool detects if the user has switched any tabs or windows on the computer and reports them for violation. Test administrators can then take further actions based on the reported violations.

Quillionz is an online AI-powered tool that can be leveraged to create questions papers. Users need to submit the content for which they need a question paper to be generated. The software then analyzes the data to generate the question set. You can create multiple-choice, recall, and short descriptive questions with Quillionz.

Thus, the process of conducting exams is simplified significantly with the help of machine learning tools.

Teaching Online

Top View Photo of Girl Watching Through Imac

Image source: www.pexels.com

With education going digital, learning is no longer confined to the walls of schools and colleges. Students now have the option to learn anything they want, anytime they want with online learning courses. This has given rise to ‘smart tutors’ where a tutor is available to teach on-demand. Video tutorials, educational apps, assessment quizzes, and massive online open courses (MOOC) have provided an amazing platform for educators to connect with students across the world. Machine learning solutions are improving online education in the following ways:

  • Adjusting the course delivery to match the learner’s capabilities
  • Removing administrative work through automation
  • Personalizing content according to the preferences of the student
  • Communicating and interacting with students intelligently
  • Allowing students to learn without a teacher

Cognii has developed an AI-based Virtual Learning Assistant that engages with students in a chatbot style conversation. It tutors students by providing personalized hints and tips that can help students to grasp concepts easily. The tool also provides in-depth analytics based on the students’ conceptual understanding, which can help teachers instruct and teach students better.

Similarly, a computer science professor at the University of California, San Diego, Paval Pevzner and his colleagues have developed an online intelligent tutoring system (ITS). The ITS is an advanced online undergraduate course that provides personalized learning experiences to the students enrolled in the course. The course carries out a continuous evaluation of the students through ‘just in time’ exercises and quizzes.

Duolingo is an online learning tool that helps individuals learn new languages without an actual teacher. The platform has users complete exercises, ranging in complexity, on a daily basis. Based on the answers(data) provided by the users, the platform uses machine learning technology to understand the user’s learning and remembering capabilities.

Assisting in Tutoring

We have already seen how machine learning solutions help in online tutoring. Similarly, machine learning tools can be used to assist teachers in physical classrooms. Machine learning-powered teachers in the form of robots can be utilized to assist teachers in managing and teaching their students. These robots can prove to be highly beneficial, especially for preschool teachers. Managing and teaching toddlers is a tough job, and ML robots can help make the teaching process fun and easy.

High-Angle Photo of Robot

Image source: www.pexels.com

Singapore had deployed two humanoid robots, Pepper and Nao, to assist kindergarten teachers. Another robot, called Kibo, is programmed to read instructions on a wooden block and follow the instructions, such as backward, forward, and shake, printed on them. The robots help provide a playful and engaging environment to the kids and reduce the burden of the teachers. The Singaporean government is highly optimistic about the use of robots in its education system.

Developing Engaging Content

Quiz, Test, Answer, Exam, Questionnaire, Education

Image source: www.pixabay.com

The use of machine learning for educational institutions can help create engaging learning content for students, called ‘smart content.’ Machine learning solutions can be used independently or integrated with other technologies such as AR and VR to analyze and determine which learning module is most preferred by students. Educators can then create content that generates maximum engagement and interaction from the students.

Cram101 uses artificial intelligence technology to turn textbooks into short, smart study guides. The tool reads and analyzes the textbook content and then breaks it down into summaries, practice tests, and flashcards. This not only helps students save precious time but also makes learning engaging and fun.

Quizlet is another similar tool that uses machine learning to understand how students can learn more effectively. The platform allows users to create fun, engaging quizzes, and flashcards for other users. Quizlet’s Learning Assistant Platform analyzes data to prioritize terms that users are most likely to forget. The algorithm analyzes the answers submitted and the time taken for answering, among other factors, to come up with term prioritization. The algorithm is highly accurate at predicting user recall and eventually helps users to learn new concepts efficiently.

Netex Learning is another platform that allows educators to create compelling, engaging, and interactive learning modules. Content creators can leverage audio, video, graphics, simulations, and gamification modules to create engaging content for students.

Simplifying Back-office Activities

There are a number of back-office activities that are involved in the day-to-day functioning of an educational institution. For example, managing accounts, bus schedules, and other administrative tasks. Machine learning helps simplify these activities, reducing the time and resources needed.

School transportation

Yellow School Bus Beside Gray Concrete Building

Image source: www.pexels.com

EDULOG is helping educational institutions with school bus routing and planning. Schools can track their entire transportation services effectively with EDULOG. EDULOG is helping schools to find the most effective routes for students. Similarly, it helps ensure the safety of students by marking hazardous zones to help route around busy streets and other hazardous areas. Additionally, schools can set field trips easily with EDULOG.

The Bartlesville Public Schools implemented EDULOG’s solution for its transportation services. The schools were able to eliminate around 120 stops, improving efficiency. 

Student diet

DietMaster Pro enables students to log their food intake. The software can also be integrated with fitness tracking devices to get a holistic view. The software then compiles the data and generates reports that provide detailed information about nutritional intake. Schools that offer lunch services can then change their meals based on the reports. The software can also be customized to hide student names to protect student privacy.

Helping Students in Career Advancement

Machine learning solutions can help students prepare for their academic future by simplifying the various processes involved. It can help in tasks such as resume building to ensure that the student’s resumes can be easily found by recruiters using ATS. Similarly, it can help in calculating finances for a specific course. Furthermore, machine learning tools can even help students to choose the best schools or colleges by analyzing the information they provide. Thus, it can be concluded that machine learning tools can help students build a strong foundation for future academic and professional life.

Photo of Man and Woman Walking in Campus

Image source: www.pexels.com

CollegeAI is one such tool that is assisting students in the college selection process. Students are required to answer a few questions in a fun quiz format regarding their preferences. The questions are fairly simple to answer, such as ‘areas of academic interest, school size, and location.’ Based on the answers provided, the software provides a list of recommendations matching the criteria. Students can then compare the various options presented and make better-informed decisions.

Similarly, SchooLinks is a platform that provides college and career readiness programs. The tool assists students in course planning and selection process by analyzing the data collected from the answers provided by the students.

The Future of Machine Learning for Educational Institutions

Two Books Beside Two Chalks

Image source: www.pexels.com

The education sector has heavily relied on human-to-human interaction. However, the influx of technologies like machine learning is transforming the way it functions. The penetration of technology is only expected to get deeper in the future. Prominent enterprises such as Microsoft, Google, and IBM are investing heavily in machine learning for educational institutions and are expected to bring a revolution in the education industry.

The technology is likely to be used as an on-demand service by most educational institutes initially and then, depending on the scale and budget, become an integral part of educational institutions.

However, it won’t be machine learning alone that will drive technological change in the education sector. Artificial intelligence, computer vision, big data analytics, and blockchain technology are likely to join machine learning in transforming the education sector.

Challenges with Implementing Machine Learning for Educational Institutions

The use of machine learning for educational institutions is not without its fair share of concerns. There are legitimate issues about the adoption and advancement of machine learning and associated technologies such as artificial intelligence in the education sector.

For instance, the increasing use of machine learning for educational purposes has raised issues regarding job losses. And the concerns are highly valid. According to a McKinsey Global Institute study, by 2030, around 400 to 800 million people are expected to lose their jobs, across various sectors, to automation. Thus, considering machine learning possibilities in the education sector, many jobs are expected to be lost to it and other associated technologies.

Secondly, there are issues regarding the data collected by machine learning systems and how they can be exploited to compromise students’ and teachers’ privacy or be used for unlawful activities. 

Hacker, Hacking, Cyber Security, Hack, Cyber Space

Image source: www.pixabay.com

Then, there are the costs associated with implementing machine learning solutions. Not every institution can afford to implement machine learning tools and also incur the maintenance costs on a regular basis.

These challenges can easily be mitigated by the careful and thoughtful implementation of machine learning solutions at the institutional level. To solve the issue of job losses, machine learning should be implemented as an assistive technology to human educators. It shouldn’t be approached as a complete replacement for human educators. Instead, machine learning should be complementary to human teachers and used to enhance teaching and learning methods. And if jobs do get displaced due to the use of machine learning for educational institutions, such individuals should be provided with alternative job opportunities.

The data privacy and security issue can be resolved by employing strong data security measures such as data encryption, password protection, and data access restriction, among others. Adopting a data-driven cybersecurity approach will help educational institutions thwart many of the cyber attacks.

The costs associated with machine learning can be negotiated by deploying solutions on a Software as a Service (SaaS) model and leveraging them as and when needed.

It is evident that the adoption of machine learning for educational institutions is rapidly increasing and the technology is here to stay. The traditional education sector is likely to evolve into the EdTech domain, which is being facilitated by the current COVID pandemic. Thus, now is the right time to invest in machine learning technology and other Edtech tools to improve teaching and learning practices and ensure the continued growth of the education sector.