Machine Learning in EdTech: Conquering New Frontiers

Given the growing popularity of the applications of Machine Learning, a subfield of AI, it is no surprise that it is creating an indelible mark in the EdTech sector as well.

We are a generation that still continues to follow traditional methods of teaching and learning to a large extent. But there is a lot we can learn and implement from the rapid transformations occurring in the field of data science.

Understanding the benefits of next-gen technology, such as Artificial Intelligence (AI) and Machine Learning is, thus, crucial so we can implement them to their fullest potential in EdTech.

But first, what is AI?

Artificial Intelligence (or AI) is a subfield of data science that explains the ability of a computer system(s) or other machine systems to do tasks that are typically done by humans. In simple terms,

AI is the intelligence that machines possess to execute complex tasks.

AI uses complex algorithms to perform activities as human beings would, thus mimicking human cognitive actions like complex problem solving, decision making, logical thinking to derive more accurate outputs.

This is based on the assumption that human intelligence can be so accurately described and defined to the point where machines can replicate similar processes.  

What is Machine Learning?

Machine learning is a field of Artificial Intelligence that enables a system to improve outcomes based on larger amounts of data and experience. Programmers feed into the computer a set of algorithms that become historical data input for the system.

Based on these algorithms, the computer system then predicts output values.

Over time, due to constant learning, the machine learns from the initial sample data input and draws some patterns from it. This allows the machine to recognize similar patterns in newer data and make predictions about the data’s outcomes without any need for explicit programming.

Using machine learning, machines constantly learn to refine their processes of arriving at the output conclusion.

What are some examples of the applications of Machine Learning?

We can apply Machine Learning across several fields and sectors of the economy, including medical diagnosis, linguistics, marketing, banking, telecommunications and user behavior analytics, to name a few.

In comparison, the applications of machine learning in education are fairly recent and still improving.

There are a lot of opportunities for implementation in this regard.

Applications of Machine Learning in EdTech

There are several possibilities (and already existing realities) regarding optimization of in-classroom experience using Machine Learning. Some of them are:

• Creating a student-specific approach to teaching

Machine learning algorithms do the heavy lifting for us. Using these algorithms, teachers can identify what areas of study are most interesting to each student and guide them accordingly.

First, they feed the machine with initial data input of various students’ learning styles along with their interests and goals. Then, the algorithms can help teachers identify the best methods they can adopt to approach each concept that each student likes. This allows them to tailor each learning experience to individual students.

Teachers can redefine what classroom teaching must look like and involve 3D models into their teaching if need be. Tooliqa, a deep tech startup that specializes in services using deep learning techniques and 3D innovation, has simplified the process of creating 3D art. Even those who are just interested in the concepts can use it, thanks to its user-friendly interface.

Thus, educators can bring their classes to life.

This improves teacher efficiency and positively impacts student-teacher relationships.

• Plotting a student’s growth trajectory

Using the existing data pool of each student’s details - their initial test scores and scores in each test - machine learning systems can predict each student’s performance in academics.

A case study presented by S. B. Kotsiantis in 2011 highlighted the use of ML systems in predicting student academic outcomes. Students’ initial data stored from their admissions and registration information, demographic and academic data, e-learning logs, written assignments, and so on were used as the input data for machine learning algorithms.

Using this information, their future performance could be predicted.

• Helping teachers to identify at-risk students

Similarly, based on acquiring initial data about each student’s performance in each test and assignment, their general levels of interest and other factors, teachers could also identify students that are at a higher risk of dropping out.

To prevent this, the teachers can adequately design helpful strategies and interventions for each student as per their needs.

An example of a software idea to detect this is IBM’s Smarter Classrooms Project. The tech giant aims to create a cloud-based learning system that not just identifies students who are struggling with their academics but also provides insight into the methods that can be adopted to help them. It seeks to automate the process of acquiring information in classrooms.

• Providing personalized feedback using EdTech

Based on individual performance, teachers can deliver personalized feedback to the students through machine learning algorithms. Learning analytics track each student’s performance to build statistical models of their knowledge and provide computerized feedback on each student’s academic progress.

• Optimizing grading systems

Teachers can make use of machine learning algorithms to grade students’ assignments.

Currently, there are a few examples of software designed to assess students’ assessments almost automatically.

For example, Turnitin’s powerful Revision Assistant can help students improve their academic essays and automatically scores them as well.

• Matching students to the right higher institutions

Several platforms make use of machine learning algorithms to help students pick the right course and college that fits their interests best. Not only this, but these platforms also provide assistance in building and refining your resume and providing all necessary information at hand.

An example of this kind of innovation is the online platform SchooLinks by Katie Fang.

• Organizing teaching materials

One can also apply Machine Learning in content analytics to organize classroom and teaching content. It also helps teachers organize curriculum in a better manner.

This is to ensure ease of access to content in one area, further optimizing the education experience to make it more student friendly.

• Introducing AI-powered teaching assistants

To save time during non-class activities, machine learning can help educators introduce AI and ML-powered virtual teaching assistants to help students.

One can also think of various machine learning-powered websites and applications called ‘smart tutors’ today. These are in the form of flashcards, video tutorials, and other educational applications. Because of technology, they are accessible to the students and educators 24/7, making learning fun and informative.

With the increasing penetration of mobile phones, laptops and other such devices, along with the accessibility of internet and data services, Machine Learning is set to revolutionize the field of education.

It is only a matter of time before the world embraces it as the standard method for acquiring and completing one’s education.

Read also: Incorporating Deep Learning In Elementary Education (tooli.qa)

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