A new era towards more engaging and human-like computer-based learning by combining personalisation and artificial intelligence techniques
Maria K. Virvou · 2018
Engagement of learners in computer-based tutoring constitutes an important feature that is sought by learning applications to maximise the educational effectiveness. Moreover, the recognition of human emotions in interactive computer-based learning applications has also been considered important although it had been overlooked for many years in the past. In view of these, dynamic personalisation and a variety of artificial intelligence techniques offer new perspectives, insights and realistic results in rendering computer-based learning more human-like and engaging than it has ever been before. This talk will present and discuss research challenges and effective approaches towards a new era of personalisation and human-like behaviour of computer-based learning software. I will review the research advancements on this topic that have been achieved in the software engineering lab of our department. In particular, the talk will focus on the automatic analysis of computer observations in computer-based learning that are collected, taking input from at least three modalities of interaction, namely the keyboard, camera and microphone in conjunction with contextual information and are then used to draw inferences about the users' cognitive status, reasoning, social classmate behaviour, preferences and emotions. In return, the tutoring content and the user interface is automatically adapted accordingly to address the individual user's needs by making appropriate recommendations and presenting adaptive guidance. Contextual information differs depending on the kind of computer- based learning applications. As such, a diversity of paradigms of fully developed and evaluated computer-based learning systems, in our lab, will be presented and discussed to exemplify the above research in the context of virtual reality educational games, social network learning, mobile learning and multi-modal stand-alone learning, based on the combination of cognitive theories about human reasoning and emotions, machine learning algorithms, decision making theories and fuzzy logic. The talk will conclude by highlighting open research areas for further research.