A user modelling component for adaptive learning

Education, and with it the opportunities for a better education, are changing and becoming increasingly accessible to the general public. Today's degree programmes offer a wide range of entry routes, enabling students from very diverse backgrounds to pursue their goals, thereby creating an increasingly diverse student body at universities. This inevitably leads to a greater need for individual support, which lecturers are unable to provide in large groups. For this reason, there is an increasing reliance on digital, computer-based support to make individualised learning support scalable. At the very least, since the last global pandemic, during which the entire education system had to switch to digital distance learning at short notice, education and the learning process have been taking place primarily in the digital environment. For the reasons just mentioned, digital adaptive learning environments are increasingly being used to promote efficient learning, better learning outcomes, and long-term, sustainable motivation. However, the ever-advancing pace of technological progress and the demands of digital literacy are also transforming digital learning and can thus drive breakthroughs in diversification and personalisation of learning.

To provide learners with the best possible support at all times, an increasing number of educational institutions and providers are turning to digital adaptive learning environments to offer far-reaching personalised automated support, thereby providing long-term support for individual learning processes. The foundation and core of such adaptive learning environments, alongside inference mechanisms (such as the recommender engine), is the so-called learner model, which is an explicit representation of individual learners' characteristics and interaction data, in the form of an internal machine representation encompassing many different aspects and enables various forms of personalisation, such as adaptive presentation of content, individualised content adjustments, and adaptive navigation, whilst taking into account the learner's current knowledge, skills, and preferences. Such targeted, individualised adjustments can have a lasting impact on learning progress. Consequently, this approach is increasingly being adopted in educational institutions. As a central component of the digital personalised learning system, the quality of learner models directly impacts the effectiveness of personalised learning.