Prof Dr Wolfram Hardt and Assistant Prof Dr Tudevdagva from Chemnitz University of Technology shares their expertise on how artificial intelligence (AI) makes learner-centred learning a success
Let us try to analyse the situation of the challenges faced by today’s higher education institutions. Professors design their course while they aim to increase the capacity of knowledge. Every course has one or more pre-conditions to enter and an expected amount of previous knowledge or experience. Nevertheless, in reality, we do not know how the knowledge of the learner matches the course pre-conditions. Maybe the learner knows much more than we are expecting or maybe too less to meet the expected pre-conditions. If the learner already has some prior knowledge of the offered course topic, then they will be bored during a lecture. This will reduce the study interest and motivation of the learner and, therefore, the actual learning process.
Or the opposite could be true: the previous knowledge of a learner is not sufficient and as such, they will understand less from a lesson. This will also reduce the motivation of any learner. Both are challenging points for learning environments in adapting to the needs of the learner. This is what we call learner-centred learning. A learner’s knowledge status and individual learning progress must be the optimisation normative for learning platforms, as well as teaching procedures.
Traditionally, basic solutions are already known. For example, some universities offer pre-courses for new students to prepare them for the academic start. Such courses are good for learners who do not match the pre-conditions. However, this solution is not applicable to all courses on offer and all individual learning statuses. Some universities offer online learning modules, but the learner has to find out independently that they need a supporting online module.
However, in any case, exams are offered for complete learner groups at the end of the teaching period. This process centred approach is easy to handle, but many resources in this vein are wasted. Students have to wait for the exam or they join exams without appropriate preparation, which of course, slows down the learning process.
To set up learner-centred learning, a learning platform as technology basis is needed. In addition, we collect the process data of each learning step individually. The learner’s activity, progress, exercise results, as well as learning time, are important indicators of an individual’s progress. All data has to be kept safe and anonymously to avoid any unwanted data usage, so we analyse the collected data by artificial intelligence (AI) methods. Deep learning-based strategies can be used to find and compare learning profile fingerprints. These fingerprints are used to establish motivation inputs for the learner and to adapt the learning modules as required. In this way, learner-centred learning programmes can be generated automatically. Self-assessment methods can be applied to this artificial intelligence (AI) based learning platform. Thus, the learner gets individual and detailed status information.
Once the learning progress matches the exam level, then the learner can join the exam. This decouples the learning processes, based on adaptive learning methods. This new approach to learner-centred learning offers high value for both learners and stakeholders, for example, at higher education institutions. In this respect, teaching quality can be raised, individual talents are supported and the learner’s diversity, due to their educational background and personal experience, is managed successfully.
Please note: this is a commercial profile
Prof Dr Wolfram Hardt
Assistant Prof Dr habil. Uranchimeg Tudevdagva
Chemnitz University of Technology
Tel: +49 371 531 25550