6 Ways Machine Learning Will Evolve Classrooms in 2021

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As a technologist, I spend a lot of time with my ear on the ground for the latest happenings around the EdTech Industry.

One of the trends I’ve seen in 2021, is the use of Machine Learning being implemented in EdTech tools and teaching practices.

This is an interesting development because, by definition, machine learning uses artificial intelligence to improve upon itself. When utilized by educators in the classroom, it opens up a number of opportunities for schools to optimize their curriculum and teaching strategy with detailed insights.

In this article, I’ve identified six interesting developments and challenges from my research that may help to guide educators through 2021.

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1. Predict when students will struggle with a type of concept

Are you about to start teaching a topic focused on critical thinking?
Will a particular student struggle based on previous critical thinking based topics? – These are the type of answers Machine Learning can provide to teachers.
Using historical assessment data, many EdTech companies are able to predict when a student may need more help.

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2. Which technique works best for a student

The term personalized learning has been around now for over 10 years but we are finally going to see this implemented this year.

To help us get there, Machine Learning is using various information points to identify a student’s learning style, as you can see in the diagram below.

A large task for educators will be to consider tagging individual learning modules. This will allow algorithms to better understand what modules worked better for which student using relationships between the tags.

It should be noted that GDPR’s maturity and accessibility have played a big part in allowing access to relevant student data and will continue to do so.

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3. Teachers building their own learning models

Machine Learning relies on its training data to learn how to navigate data.
To date, EdTech companies are training their own Machine Learning systems. This year we will start to see learning models provided by teachers.
Instead of using preset and student data, teachers will start providing their own data to Machine Learning. This will allow these tools to become effective teaching assistants in a sense.

[/kc_column_text][crum_single_image image_size=”full” align=”aligncenter” _id=”253930″ image_source=”media_library” image=”13579″ caption=”Teachable Machine is a great project that can be the start of how each of us will train our own Machines”][kc_column_text _id=”402340″]

4. Automated testing of curriculum

Curriculum designers are often looking to run test groups to improve their curriculum designs. With the aid of Machine Learning, data can be used to enhance areas such as curriculum flow. Does your lesson have the right amount of reinforcement? The system can even recommend quizzes where student engagement could increase.

A method commonly used by many EdTech organizations is the A/B test, which samples the curriculum with two groups and measures the effectiveness.

With Machine Learning and the added benefit of digital learning, we will also be able to measure student impact on assessment, engagement rates, the effectiveness of reinforcement techniques, and more.

In addition, the speed at which Machine Learning can analyze data is far greater than what we can achieve without its help. This will be a great step in the direction of truly personalized learning.

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“We help thousands of teachers at BSD Education with our ready-made digital curriculum and projects” – Nickey Khemchandani

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5. Body language detection and Audio analysis on video calls for behavioural health

Remote learning is now common practice. Teachers are facing the difficult task of identifying engagement or interest via the student body language on a video call.

It was not uncommon to hear the sound of disengagement or stress in student voices when teaching online. One of the areas Machine Learning was able to step in and help was to highlight “stress” indicators in students’ voices in a lesson.

Years ago, I read an amazing research paper by Ishan Behoora and Conrad Tucker from Carnegie Mellon University [https://bit.ly/3npEB1S] explaining how Machine Learning can classify the emotional state of designers in real-time. This got me keeping tabs on this space for how it can be utilized in Education.

As video calls become a norm in education, expect to see real-time detection of student engagement and attention tracking coming soon.

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The following is from a sample class we conducted with a technology partner. The voice of a pre-teen [below 13] was analyzed to identify stress levels. (Voices of pre-teens are easier to analyze since often their voice patterns sound similar and there are fewer similarities to an adult voice).

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6. Assisted grading of non-binary assessments

We have all seen multiple-choice questions being graded using machines. They work great and have been an incredible help for teachers.

With Machine Learning tools used in popular plugins such as Grammarly or the Hemingway Editor, it was only a matter of time before essay writing was also supported.

Research papers are already sharing promising developments and improvements in this space with the inclusion of Machine Learning.

[/kc_column_text][crum_single_image image_size=”full” align=”aligncenter” _id=”152890″ image_source=”media_library” image=”13587″ caption=”The Hemingway Editor is one of my favourite tools when creating a curriculum. http://www.hemingwayapp.com/”][kc_column_text _id=”417369″]

“Machine Learning tools need to be accessible by students as well. These tools can help students solve problems while they are encountering them. This is one of the goals technology aims to serve in Education.”
– Nickey Khemchandani

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At BSD Education, supporting educators/teachers is at the core of who and what we stand for.

With so much to look forward to as technology develops in education, it will be critical for the education community to support teachers throughout these transitions.

We are excited to hear how more schools incorporate machine learning in 2021.

about Nickey
Nickey is a Co-Founder and CTO of BSD Education, Refugeek, and the Founder of Collective Global. He is an expert in infusing technology and digital solutions into businesses.
He heads up innovation projects to evolve products and services using a combination of Design, Development, UX, and Marketing skills. He works on large scale digital transformation projects, digital marketing, and the effective use of social media to drive business success and harness the power data. Nickey is a Co-Founder of BSD Education, Refugeek, and Collective Global.

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