How i-Learn provides an innovative scientific basis
Did you know that the development and support of teachers within the i-Learn project is underpinned by state of the art scientific research on EdTech? Within the project, a passionate team of renowned international researchers is actively working on the scientific foundation of the choices, the direction and the goals of the i-Learn programme. With i-Learn, we are taking an international lead in the field of digital personalised education.
In this blog post, we want to take you into the world of our researchers and try to explain in human language what our team of researchers is actually doing. Don’t worry, we won’t make it too technical 😉
The development of i-Learn is based on the extensive needs assessment of schools. Our researchers organised a large-scale survey and set up focus groups with teachers and educational experts, in which they gauged the needs for digital personalised learning in Flemish education. The course and results of this can be read in previous blog posts (here and here).
On the other hand, the researchers have been building a strong scientific foundation in the field of digital personalised learning since the beginning of the project.
Intensive research into personalised learning
Digital personalised learning means ‘learning analytics’. In a previous blog post ‘The usefulness of learning analytics‘ we already demonstrated its value. The wide range of possibilities of learning analytics also inspires our researchers. With the input that can be generated about the learners via digital tools (their results, but also their pace, motivation, learning style, etc.), there is plenty of scope for personalisation – with respect for the learner’s privacy, of course. Within i-Learn, for instance, we investigate the possible mechanisms to create a personal recommendation system (cf. Netflix). Check out our blog post ‘How i-Learn can learn from Netflix‘.
It is also interesting for teachers to gain insight into the learning data of their students, in order to be able to optimally respond to their different needs. Researchers at i-Learn are looking into the effects of various forms of data dashboards for teachers and what effect these have on the role teachers take in the classroom.
Fast learning progress and greater motivation
With i-Learn, we are also delving into the technical side of learning analytics. In particular, we are investigating how different learning applications can cooperate and exchange data with each other. For example, learning progress in one learning application can be taken into account when using another tool, so that the learner no longer has to start as a beginner. The progress is taken across tools.
In addition, our researchers also pay attention to the mechanisms behind different rating systems (How motivated is a learner? How well does the learner score on the exercise? …). They then build on these mechanisms to optimise the algorithms. The research shows that students absorb the learning material faster as a result, and they also find it highly motivating.
Continuous evaluation
Finally, our research team works intensively on the evaluation of i-Learn. In the current phase, this mainly means evaluating the prototype of the portal and the current form of didactic support (coaching, i-Learn manual,…). The researchers contribute to the elaboration of the broad evaluation framework and support the set-up, the execution and the analysis of the different evaluation methods: focus group interviews, online surveys and the data coming in through the system. In this way, we raise the evaluation phase of the i-Learn project to the highest scientific level.
The combination of practice-oriented research and state-of-the-art science ensures that i-Learn has a solid basis in the educational field. The entire team will continue to work on this to further develop i-Learn as a top player in the international EdTech world.