The advent of Learning Analytics is certainly making waves in the education and training industry. Each individual’s cognitive ability is different. Some learn mathematics faster than history, some prefer learning through audio visual content rather than from books. The domain of Learning Analytics provides us the power to comprehend an individual’s cognitive skills and learning preferences. This gives educators a huge opportunity to deliver better training for each and every student.

Learning Analytics is a step in the right direction as technology becomes an integral part of the classroom. Conventionally, technology has been used to make the classroom experience engaging by trying new things like using rich multimedia lessons or even games in the classroom. The only downside was that it was difficult to really get answers to the most important questions :

Are these tools really helping to improve the student’s understanding of the topic?

Measuring performance helps in taking the right decisions in terms of interventions to improve the learning experience. But sometimes, just a test score is not enough. Consider an example where 15 out of 25 students in a classroom scored 7 out of 10 in a mathematics quiz. A logical thought would reveal that each of these students would have got different questions incorrect. However, it is not humanly possible for one teacher to address each student personally. To make matters worse, Asian countries have a much higher student to teacher ratio, which makes it additionally time intensive, if not impossible, to deliver personalised coaching to students.

In an attempt to demonstrate the benefits of using learning analytics in the classroom, we selected a scenario where students exhibited the kind of diversity that you would find in a large classroom. For just under 100 students, there were two teachers for the subject of mathematics. Students came to the classroom with scores of around 40% to 60% which indicated that they would require some assistance to grasp the topics in mathematics. The teachers were keen on two things :

  • Delivering lectures in the classroom which would be increasingly relevant and helpful for students
  • Helping each student develop a better understanding of the topic and score higher in the examinations

To achieve these objectives, a well crafted approach was put in place to make the teaching-learning process more effective. The steps taken have been described below :

PLANNING PHASE – Preparation of knowledge map and course content

Prior to the commencement of the session, discussions with the faculty helped in understanding the core curriculum contents and the common problems faced by students. Broadly speaking, the following steps were taken

  1. Create a map of knowledge for the subject
    • Topics in the curriculum were arranged in the logical order in which they need to be taken – Basics followed by advanced topics.
    • Prerequisite topics were identified and included as a preparatory course. These were sequenced at the beginning of the course
    • This map of knowledge was used as a guidance tool to personalise the learning experience for each student.
  2. Design courses with content and assessments for each topic
    • For each topic, micro-courses were prepared with a mix of learning resources and formative assessments.
    • Formative assessments were designed with hints and solutions for each question.
    • The real objective was to give the students access to the solution immediately since students learn best when they are actively seeking solution to a problem

IMPLEMENTATION PHASE – Providing insights to teachers and personalised learning to students

  1. Weekly assessment sessions – Topics taught in each week were assessed towards the end of the week. This helped to get faster and detailed feedback on students’ understanding of the topic.
  2. Weekly reports powered by Learning Analytics – Teachers were given weekly reports on student and classroom performance. These were used to identify the topics where the majority of mistakes were being made, so that they could be addressed in the upcoming session
  3. Personalised Learning for each student – Each student was provided with recommendations for learning based on their individual performance. This meant that even if two students scored a 7 on 10 in an assessment, the instructions they received would be different if they made mistakes on different questions.

As a result of this exercise, students were not only scoring better, they were actively involved in the learning. Student scores increased by an average of 30% as they progressed in each topic and teachers were empowered with specific insights to help them improve their lesson delivery.

Technology has traditionally been used to scale reach of education and training through standardised lessons.

The combination of Learning analytics and personalised learning presents an opportunity to deliver customised lessons at scale, making training increasingly meaningful and relevant for each individual learner


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