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Education - Why Machine Learning is Important in Education Sector - B-AIM PICK SELECTS

Why Machine Learning is Important in Education Sector

As the technological world keeps changing, the education sector keeps improving as well. Machine learning is among the advanced technology in the sector concerning education. Machine learning has uplifted the education industry to extreme heights. Every institute strives to bring out the generation ready to face the competitive world. So what exactly is machine learning?

Machine learning is a section of artificial intelligence that deals with computer development programmes. The system is meant to analyze data automatically, then come up with a concrete solution without necessarily involving much of human knowledge. The bottom line is that ML mainly focuses on computer programmes.

The entire process starts by analysis of data, for instance, instructions or experience. The importance of this stage is to enable better decisions in future. The bottom line is to enable computers to analyze data and take actions as required automatically without assistance from a human being. There are different methods involved in machine learning. Let us analyze them:

Methods in Machine Learning

Algorithms involved in machine learning are classified based on whether they are supervised or unsupervised. There is supervised, semi-supervised, and unsupervised Algorithms

  • Unsupervised Method

Unsupervised classification happens when the information used to train is not labelled; neither is it classified. The main focus on the unsupervised algorithm is to explore how a system can assume a function to define the unknown structure from unlabeled data.

In simple terms, you do not have to supervise the model when it comes to machine learning techniques. Instead, the model operates on its own to get the information

  • Supervised Method

In the supervised method, it is the opposite of unsupervised whereby machine learning is only applicable after learning has already taken place. i.e., new data is retrieved from prior learning, whereby the new data is used to foresee future events. The onset of a supervised algorithm starts off by analyzing already known data. A conditional function is later on produced to predict expected results. This implies that the system can only produce a new target for new data after adequate training. The system is adaptable to any change like identifying errors and rectifying to get the desired results.

  • Semi-Supervised Method

From the word semi, it only shows that the semi-supervised algorithm falls in between supervised and unsupervised algorithms. In this category, both known and unknown data is used during the training process. In reality, a large amount of unknown data is used alongside a small amount of known data. This kind of system is more accurate as compared to the other two.

The semi-supervised algorithm becomes effective in the event that the known data acquired requires highly skilled resources to implement it. On the other hand, unknown data does not require more resources for it to be effective.

  • Reinforcement Method

In this algorithm, the learning process produces actions and identifies errors as it networks with the adjacent environment.

The main characteristic of reinforcement algorithm is trial and error search. For this algorithm to operate effectively, it allows the software agents and machines to select perfect behaviour within a specified context automatically. For an agent to learn in a swift manner, an action should be put in place which is offering simple reward feedback. The feedback is known as reinforcement signal.

Machine learning is the best trend because it offers a chance to analyze a large amount of data. However, for it to be effective, more of the time and resources will be required to deliver the desired results at a faster rate. While it is the best practice so far, coordinating machine learning with cognitive technology alongside artificial intelligence can ease the entire process.

How is Machine Learning Applicable to The Education Industry?

Machine learning is a process that keeps advancing over time. Any organization dealing with education technology put more of its efforts in exploring the latest technologies relating to machine learning. Machine learning helps companies to be different from others. And since it is a trend that keeps advancing, the future can only be brighter because it is among the trends that are never going to fade away any time soon. The following are ways in which machine learning is improving the sector of education:

  • It Enhances Efficiency For Anyone to Achieve their Dreams

Any Educational system in operation has one main goal; to build a system that has a positive impact at large for the future generations. Above all, the system needs to be easy to operate and handles all the tasks just perfect. Machine learning has simply offloaded the teachers a lot of paperwork like maintain separate details relating to their students.

So, how has machine learning simplified the work of the teachers? The answer is simple,

  • Organizing Schedules for teachers

What machine learning has done is that it helps in organizing the content by arranging the schedule for the teachers on a daily basis. As a result, teachers can work smoothly with a well-arranged schedule, and the performance rate of the students improves.

  • Bridging the gap Between the Teacher and The Student

The best thing machine learning has helped the education industry is fulfilling the teacher-student gap. But then, how? Well, a student doesn’t have to keep chasing after the teacher for enquiries because the pre-planned system does such activities based on the pre-defined schedule between the two.


A web platform known as Netex has enabled machine learning whereby the teacher is able to schedule their activities for the day. They can customize their plan using content which has audio, discussion portals, videos, and even customized assignments.

In summary, machine learning has done the following:

  • It has innovated modified modes of learning

  • It has offloaded the teachers some work and duties

  • It has given both the students and teacher a new dimension to approach educational matters

It Embraces Projective Analysis of Results

Lack of oriental future goals often leads to reducing focus mainly on the students who think they are less fortunate on educational matters as compared to others. And in the olden days, tutors used to favour students and belittle others according to their performance scale.

Now, in machine learning, there are no favors since the prediction of data only happens by highlighting both past and present data to come up with the desired results.

The art of predicting results includes procedures from machine learning, data mining, and predictive modelling to weigh past and present experiences to determine future results.

So, how is this applicable to the education sector? Well, the tutors begin to put more weight on the underperforming students to boost their results. Through machine learning, the team players in the education sector are able to understand the areas which are weak and attend to them as required.

Ways in which projective analysis of results helps in Education

One of the key advantages of projective analysis of results to the education sector is that it allows the organizations to use both the existing and innovative knowledge to come up with ideas and solutions which will benefit the students at large.


A school, (Hamilton County) monitored how every student performed with IBM SPSS software for analyzing predictions. The reason why the opted for the route of data analysis was to improve each and every student at large. The data they collected helped them in many ways, including:

  • Improving the performance rate of each student

  • Creating a strategy to know each student well

  • Creating a system to retain the students

  • Improving the whole team involved

Introduction of Innovative Learning System

In the ancient days, teaching was just a repetitive routine which never had new ideas and advanced technology. However, with machine learning, everything has had a new experience and a new approach.

Through machine learning, means of teaching have improved steadily. For instance, lessons can be conveyed through digitalized means such as educational applications, video tutorials, Valuation queries, smart guides, and Flash disks. What is more attracting is that both teachers and students can access such resources at any given moment; be it during daytime or at night.

Another innovative machine learning technique is what is called virtual tutors. This is more interesting because a virtual teacher can teach different students via online platform despite their location. Isn’t this an idea worth applause? Well, it is. Imagine helping learn things and find solutions to some questions without necessarily being in class. It is with no doubt that machine learning has brought a lot of changes in the education sector.

Importance of Innovative Learning System

  • Introduction of virtual learning in the education sector

  • Keeping up and even outshining the competition

  • Boosting the rate of student’s performance


Machine learning is an innovative system that is never going to end any time soon in the sector of education. All the team players involved in the education industry should embrace such technology and even raise it to more heights.

It is high time the education industry do away with older ways of teaching and embrace new technology; the technology is none other than machine learning.

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