Why is a data driven approach to education necessary?

Customised learning intuitively makes sense in how it can improve learning. To do this at scale requires innovation and technology.

5 mins read

The spotlight in the 21st century is on personalisation. Consumers are increasingly demanding personalisation of services, products, and rewards while companies are changing their business models to cater to target customers' personal preferences.

People studying in a university library People studying in a university library

Not surprisingly, the traditional approach of 'one-size-fits-all' is no longer relevant to students when it comes to education. According to Todd Rose, author and Professor at Harvard Graduate School of Education, generalised teaching approaches and curriculum fail to meet the academic needs of students or fuel their passion for learning.

Learning is optimised when teachers empower students to develop creative and analytical thinking and engage them fully in the learning process. This is possible when learning materials and teaching approaches are personalised to meet each learner’s specific needs. By taking a data-driven approach, educators can personalise learning experiences to derive optimum outcomes.

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Offering customised learning experiences

Personalised learning aims to tailor the learning material and approaches to each student's skills, interests, and strengths. This is typically based on a flexible learning plan that can include a blend of teacher-led instruction, experiential learning, group work, online learning, and self-paced learning.

A customised learning experience can be offered when teachers assess a student, individually give real-time feedback, and adapt learning content.

University students working in a group University students working in a group

For instance, UK based Teesside University partnered with a global software company to offer personalised learning experience through online platforms. While the platform automates communication between students and staff no matter where they are located, the real-time learning analytics enables instructors to create course content.

Big data analytics was also used by Arizona State University to create individual profiles for students and to personalise learning content. The tutors minimised generalised lecturing and focused on supporting students individually. As a result, college dropouts reduced by 50% while passing rates increased.

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What role does data play in personalised learning?

A data-driven approach facilitates personalised learning that prioritises the needs of each student. Data and learning analytics enable educators to design appropriate curriculum and learning materials that engage learners in the learning process.

AI tutoring systems are increasingly being used to engage students and offer personalised learning. With smart learning analytics, such AI-based data-driven learning platforms provide feedback to students on areas that they need to work on. Data-driven learning systems enable students to stay on track of completing their course and improving their learning. Smart data and learning analytics empower students to be prepared for the class while providing valuable insights to educators who can then deliver an impactful classroom experience resulting in deeper understanding and rich interaction.

Insights provided by data and learning analytics are designed to improve the students’ grasp of core concepts, enhance their performance while enriching their digital experience. The intelligent tutoring system offers educators feedback to enable them to understand what or where they should focus on each student.

Such data-driven tutoring systems based on learning analytics have far-reaching positive impacts on optimising the overall quality of academic outcomes as well as teaching.


Aradhana Iyer VohraAradhana Iyer Vohra
18th Dec 2020

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