Dimensions of personalisation in technology-enhanced learning: a framework and implications for design

Personalisation of learning is a recurring trend in our society, referred to in government speeches, popular media, conference and research papers and technological innovations. This latter aspect—of using personalisation in technology-enhanced learning (TEL)—has promised much but has not always lived up to the claims made. Personalisation is often perceived to be a positive phenomenon, but it is often difficult to know how to implement it effectively within educational technology. In order to address this problem, we propose a framework for the analysis and creation of personalised TEL. This article outlines and explains this framework with examples from a series of case studies. The framework serves as a valuable resource in order to change or consolidate existing practice and suggests design guidelines for effective implementations of future personalised TEL

ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

International audienceIn this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides in- depth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests

Emotional Regulation in Synchronous Online Collaborative Learning: A Facial Expression Recognition Study

Emotional regulation in learning has been recognised as a critical factor for collaborative learning success. However, the “unobservable” processes of emotion and motivation at the core of learning regulation have challenged the methodological progress to examine and support learners’ regulation. Artificial intelligence (AI) and learning analytics have recently brought novel opportunities for investigating the learning processes. This multidisciplinary study proposes a novel fine-grained approach to provide empirical evidence on the application of these advanced technologies in assessing emotional regulation in synchronous computer-support collaborative learning (CSCL). The study involved eighteen university students (N=18) working collaboratively in groups of three. The process mining analysis was adopted to explore the patterns of emotional regulation in synchronous CSCL, while AI facial expression recognition was used for examining learners’ associated emotions and emotional synchrony in regulatory activities. Our findings establish a foundation for further design of human-centred AI-enhanced support for collaborative learning regulation

AI-enabled adaptive learning systems: A systematic mapping of the literature

Mobile internet, cloud computing, big data technologies, and significant breakthroughs in Artificial Intelligence (AI) have all transformed education. In recent years, there has been an emergence of more advanced AI-enabled learning systems, which are gaining traction due to their ability to deliver learning content and adapt to the individual needs of students. Yet, even though these contemporary learning systems are useful educational platforms that meet students’ needs, there is still a low number of implemented systems designed to address the concerns and problems faced by many students. Based on this perspective, a systematic mapping of the literature on AI-enabled adaptive learning systems was performed in this work. A total of 147 studies published between 2014 and 2020 were analysed. The major findings and contributions of this paper include the identification of the types of AI-enabled learning interventions used, a visualisation of the co-occurrences of authors associated with major research themes in AI-enabled learning systems and a review of common analytical methods and related techniques utilised in such learning systems. This mapping can serve as a guide for future studies on how to better design AI-enabled learning systems to solve specific learning problems and improve users’ learning experiences.publishedVersio