When talking about the advantages of Education technology (EdTech) over traditional methods, you’ll hear about personalisation. It’s presented as almost a core difference between the two.
But does it actually benefit students, and how does it work? As a software development company with extensive expertise in EdTech development, we will shine a light on this topic.
If you’re interested in learning more about our experience in the EdTech industry, you can read about it here: https://www.altamira.ai/edtech-development/
What is personalisation?
Personalisation refers to creating a tailored experience for each user. In EdTech, it’s about finding a unique approach for each student and meeting their education needs.
Instead of using a “one-size-fits-all” approach, it analyses the student’s behaviour and interests to develop a learning plan that best suits the student. For example, if a student struggles with a specific subject, the system will notice it and provide additional learning materials to reinforce their knowledge.
At the same time, students who excel in a specific subject will receive more challenging tasks to keep them interested in the topic.
A personalised approach to education relies on students’ data to tailor it to their unique needs. Most of this data comes from how the student interacts with the EdTech platform.
The role of AI in personalisation
Personalised experiences are very popular due to their engagement. However, since they are based on data analysis, they require specific technologies to be used properly.
It’s not hard to manually analyse the data from one student, but you’ll be quickly overwhelmed if you try to analyse data from every student. This is where artificial intelligence (AI) comes in.
AI’s core principle is making decisions based on the data presented. The most direct way to use AI is for data analysis. However, since AI data analysis is based on algorithms, which one will best suit your needs?
There are four options to choose from:
- A clustering algorithm is mostly used for unlabeled data analysis. It works by segregating all data into groups based on the similarities. Such algorithms are the main driver behind many recommendation engines in EdTech;
- The regression analysis method focuses on the relations between data and independent variables. Mainly, it’s used to predict trends and create forecasts;
- The association rules explain the relations between specific data units in a dataset. It’s the main driver in establishing how effective the collaboration between different students will be;
- Markov chains are the most straightforward data analysis method. They show all possible outcomes based on current actions, which is exceptionally useful for systems that want to predict how challenging the assignment will be from the student’s perspective.
Final words
To sum up, artificial intelligence is the main driver behind personalisation in EdTech. Its natural ability to manage and analyse large amounts of data fits perfectly with platforms with thousands of students. Handling so much data and providing tailored experiences for everyone makes it a lifesaver for EdTech, as it is responsible for its biggest selling point.
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