Welcome to the era of personalised learning

Personalised Learning’ is manifesting and one of the most discussed educational initiatives today.

With the combination of pedagogic insight and technologies like AI (Artificial Intelligence) and ML (Machine Learning), learning has become personal, multi-faceted, contextual, and dynamic. Many schools are now adopting the personalised learning approach.  Even if the task is interesting enough or not, educators are trying to make sure everyone is at the “right level” of challenge in the learning process. This “right level” is the level unique to every student. Hence, this needs to be calibrated by the teachers. A teacher can only look at a certain number of parameters to calibrate this level. On the other hand, a Personalised Learning Environment (PLE) can use the best of technology to do this automatically.

Education is going digital.

Personalised learning, aided by computers can improve student engagement, increase attendance and better behaviour. This can also create a new paradigm with a solution to the constraints of the one-size-fits-all education system or one-to-many teaching.  

Now enough about the buzzwords! So, what is personalised learning? 

Blended learning and personalised learning are often used interchangeably in the field of online education. These terms are often confused for one another.

Personalised learning, by definition, is learning anywhere anytime. In personalised learning, we give emphasis on personalising the content to suit the need of a student.

Blended learning, on the other hand, is the implementation of technology to improve or aid both the teaching and learning process. Blended Learning glues the analogue world or conventional way of learning to the digital world.

While personalised learning does not necessarily require technology, one needs to focus on individual learner’s pace, need, and interests. Hence, learners’ profile and evidence of competency-based progression are the two key aspects to create tailored and more impactful learning content.

Factors in Personalised Learning

Let’s look at the factors in personalised learning that can drive an education transformation. Let’s also look at the reasons behind the school-wide personalised learning approach. Following are the main factors:

  • Customised learning pace as per each student’s needs
  • Tailored content, learning objectives, and tools to optimise student learning
  • Learning decisions centred on learner interests, i.e., individualised learning
  • Learning flexibility as in students get to choose what, how, when, and where they learn
  • Supported by technology and influenced by the individuals’ learning data
  • Helps students with a sense of control 
  • Fulfilling the learning objectives in small groups
  • Assisting  and reinforcing the learning by blended learning and online learning

So, how to make personalised learning more than just an amorphous terminology?

Being on the same page:

To make it more than a catchphrase, we need to share similar belief systems allowing for fluid and clear communication about personalised learning. We can develop a framework not just for classes but for the entire school/education ecosystem to help students succeed. This is because the future of PL may depend on how much-extended support teachers offer. 

Learning their full potential:

With a range of techniques and flexibility of being allowed to complete their assignments at their own pace, PL plays an active role in student’s learning. Many teachers and schools are already impressed with the outcomes.

“Personalized learning is tailoring learning for each student’s strengths, needs and interests — including enabling student’s voice and choice in what, how, when and where they learn — to provide flexibility and supports to ensure mastery of the highest standards possible”, says iNACOL, a nonprofit that supports personalized and competency-based education. 

Success measures:

Becoming more competitive takes an improved assessment system. This is powered by an individual’s learning data and accountability system that assures constructivism and resource-based learning. Several factors may be obstacles to success or track progress, such as socioeconomic backgrounds. Here is where a learning intervention can maximise non-cognitive skills and learner engagement.

Adoption rate:

R&D in personalised learning is a costly affair. Private schools and franchise centres can be avenues to implement personalised learning. However, there is a need to simplify the replicability to an extent that even public schools and government schools can afford it. We can look at CSR (Corporate Social Responsibility) based assistance and funding for better adoption.

Moreover, the personalised learning process doesn’t have to start in high school. It can start right from primary school. 

Personalised Learning Environment (PLE) in NumberNagar

With a learner-centred and student-first approach, we, at NumberNagar, look at the better ways for students’ overall development.

One of the main USPs of NumberNagar Learning Space is to provide personalized learning to the children. This learning is facilitated by an experienced facilitators. 

At the same time, R&D on Personalised Learning Environment (PLE) is in works for quite some time in our team. “Learning Paths” is in the centre of PLE at NumberNagar. PLE is driven by NumberNagar’s proprietary algorithms. It uses the power of Machine Learning to create personalised Learning Paths.

Learning paths are the multi-level path a student takes to reach a goal. Students can take a different learning path to reach the same goal.

  • A learning path can be created dynamically based on the various things – the topics a student/parent/teacher choose to deal with, the current level of the student, etc.
  • Each node in the learning paths is independent but it has attributes denoting what it is for and where it fits.
  • There may be an insertion of new nodes/milestones in the learning paths based on the assessment results at a previous node/milestone. This is transparent to the students. This is used to strengthen the knowledge of the student without showing a discouraging result.

Learning Paths are used to create a ‘Personalised Learning’ environment where:

  • Every student is treated differently
  • Weak students get extra attention and reinforcement
  • Strong students get extra content to keep them curious and engaged
  • Learning Paths are automatically decided based on Machine Learning
  • The facilitator/ administrator/ teacher can decide the path one has to take in advance too

Following diagram explains Learning Paths quite well:

Illustration by Nishant Krishna

A Personalised Learning Environment (PLE) can use the best of technology to personalise the learning by focusing on individual learner’s pace, need, and interests.

This article is co-authored by Parimita and Nishant.

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Pragyan Parimita Barik

Pragyan Parimita Barik

A news junkie, Parimita is an education technology writer and has been intricately associated with writing a range of topics including education, new edge learning, Artificial Intelligence, and disruptive technologies. She’s obsessed with how tech empowers people from all walks of life. She expresses herself best through photography.

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