Did you know that the i-Learn project also includes scientific research? On this page, you can find an overview of the research projects that are related to the i-Learn project or that are processing data from the i-Learn portal.
What is digital personalised learning?
Recently, digital personalised learning has received more and more attention. The use of digital personalisation tools can offer many advantages for students and teachers (e.g. individual feedback, attractive narrative, dashboards, …). A great variety of tools has already been developed for this purpose. As a result, there are many descriptions of what exactly digital personalised learning is. Within i-Learn we try to form an all-embracing definition based on various research literature (more than 7000 articles). In addition, the variety of tools is also mapped, as well as the impact.
Teachers’ perceptions of digital personalised learning in Flanders
Teachers are not heard enough. It is a justified criticism that is often voiced when education is being renewed. Many teachers already use digital teaching aids in the classroom, but within i-Learn we go one step further and aim to make digital personalised learning possible for all pupils. What are teachers’ views on this? How do they currently differentiate in the classroom? What are the pitfalls? Within i-Learn we try to map this out, because only by taking into account the voices from the educational field can i-Learn succeed.
This research was approved by the ethics committee of KU Leuven, file number G-2019 10 1798.
Evaluation of adaptive teaching tools
Although many different digital tools exist to support learning, little is known about their impact on learning. Adaptive tools assess the learner’s ability during learning and are therefore able to provide exercises tailored to each learner. Within i-Learn, we conduct research into the extent to which students achieve better learning results after training with an adaptive tool. We also gauge how much students enjoy learning with adaptive tools. Based on these results, we will investigate how the use of adaptive tools can lead to maximum learning opportunities.
Learning to model, as it happens
Often, an online learning environment offers the possibility of keeping a record of each learner’s moment-by-moment information. This information is often diverse: from choices within the learning environment, to the speed of certain actions, to the correctness of given answers. Within i-Learn we investigate how this information can be combined to reliably estimate the level and progress of each learner. Because a reliable assessment of each learner makes effective personalised learning possible.
Adaptivity in online learning environments: theory and practice
An adaptive learning environment adapts its content, sequence, speed, etc. to suit the learner. The goal is to stimulate learning by offering the right content, for the right learner, at the right moment. The adaptivity research within i-Learn focuses on the development of new adaptivity algorithms. On the other hand, it looks for solutions to implement adaptivity in practice, for example in an existing online learning environment. For that too, customisation is necessary.
Modelling metacognition to stimulate personalised learning
Metacognition is the ability of a person to think about how he or she thinks. It is an umbrella term for mental processes that also affect how a person learns. More precisely, metacognition helps guide someone through a learning task. For example, metacognition is known to have a positive impact on school results. However, it is not easy to measure or estimate someone’s metacognition. Within i-Learn, we wonder if we can determine the metacognition of students between 8 and 18 years old based on online learning behaviour. The aim is to recommend learning activities (to teachers and students) that stimulate metacognition during learning and to do so in a personalised way.
Researcher: Sohum Bhatt
This research was approved by the ethics committee of KU Leuven, file number G-2020-2248-R2(MIN).
Learning analytics for primary and secondary schools
Learning analytics, the use of event, log, and clickstream data to analyse learning and learning environments, is a growing field in education and society. It offers the possibility to give teachers and instructors more information, give students better feedback, and personalise learning. Until now, however, most applications of learning analytics have focused on higher education. Within i-Learn we are shifting this focus and investigating whether and how learning analytics can be used specifically in primary and secondary education.
Researcher: Sohum Bhatt
Digital personalized learning to teach programming
The digital revolution is having a major impact on education. However, it is not natural to always be up to speed with new subject areas, let alone designing lessons to teach new competencies to students. One such topic that has been gaining popularity recently is computational thinking, more specifically programming. Since it is not easy for teachers to find suitable tools that also take into account large differences in students’ prior knowledge or interests, we set up research for this within i-Learn. Based on design-based research (close cooperation with teachers and students) we will develop an adaptive learning track with exercises to teach students in the first grade of secondary education how to program. In addition, we will also look at how teachers use these adaptive tools and what their perceptions of them are. In this way, the researchers try to give shape to principles that can help with the implementation of digitally personalized tools.