Embracing machine learning in education

Source – standardmedia.co.ke

What is this so called machine learning that has everyone in the market adopting? Machine learning has varying definitions, depending on the authors of books and articles or one’s understanding. I like to define machine learning as a branch of artificial intelligence that provides systems with the ability to learn and act like humans, while also improving their learning over time through observations and real-world interactions without being explicitly programmed. In technical terms, it is simply a set of algorithms with the ability to learn, act and adapt autonomously without being explicitly programmed.

Machine learning can be applied in various areas in our day-to-day lives. With many industries adopting machine learning to improve their business models, the education industry has fallen behind. This is mainly attributed to by the current static structures that have been there over the generations, which make it notoriously slow to adopt change.
So, how can machine learning relate to and benefit the education sector? Traditionally, course materials and books are usually tailored for the average student and then produced in large numbers for all students to use. This approach is known as ‘one size fits all’ approach. From experience, however, many can testify that this methodology is not the best approach, as different students and teachers have quite distinct learning curves and teaching styles respectively.

Adaptive learning
This is where machine learning comes in and fabricates patterns from data and fashion educational insights. These insights are then in turn used to personalise each student’s learning path. This ensures learning takes place with regard to the learner’s pace to grasp concepts. When adaptive learning is introduced in a classroom, the tutor can assess the understanding of an individual or the class as a whole and adjust his/her pace and delivery with regard to the learner(s) progress. This information also enables tutors to identify learners who require guidance on topics they are uncomfortable with. When students join an institution of learning, they are expected to come out more knowledgeable and perform well. Thus institutions should continuously analyze and examine students’ performance with the aim of helping them to improve and providing them with a conducive learning environment.
Given the analysis on historical data of a student’s performance, the institution is able to monitor and predict future performance of a particular student. With this information, the institution can plan early and make arrangements on how students will be assisted to improve their grades, thus ensuring the institutions produce erudite students.

Detecting Plagiarism

The current assessment and grading systems hugely rely on human accuracy, which at times may be biased or not well informed. Most often the evaluation takes a prolonged period before the learners obtain their results. This can be automated by use of machine learning, thereby reducing the time taken for learners to obtain their results while at same time increasing accuracy. Machine learning also makes detecting plagiarism possible. While reducing these repetitive tasks from teachers, machine learning gives students more time to focus on their knowledge through extensive research on different subject matters. This also enables them to spend more time, with students tackling various concepts and engaging them in higher order thinking. Most students have no or little idea on what career path to follow after high school.

Given the students’ historical data on their high school performance and the courses selected, machine learning analyzes their rationale based on their choices and previous performances on the various subjects undertaken in high school. As a result, a course is recommended to the student based on his or her strengths, simplifying the career choice. Our education model today revolves around students ‘learning’ facts and how accurately those facts can be memorised, rather than focusing on whether those facts have been understood. With innovation and practical learning, our education model is constantly proving how static it is. While incorporating machine learning or technology as a whole might not be the silver bullet solution, it is indeed a step in the right direction.

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