Ontology-Based Open-Corpus Personalization for E-Learning

Conventional closed-corpus adaptive information systems control limited sets of documents in predefined domains and cannot provide access to the external content. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of the World Wide Web and are expected to operate on the open-corpus content. In order to provide personalized access to open-corpus documents, an adaptive system should be able to maintain modeling of new documents in terms of domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on the World Wide Web is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of content collections. A central domain ontology is used to maintain overlay modeling of students’ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach successfully supports open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content

Learning Kit 7 : Learning Models

Disponible en français dans EDUQ.info sous le titre "Trousse no 7 : Modèles d’apprentissage"

The Discourse Beneath: Emotional Epistemology in Legal Deliberation and Negotiation

All lawyers negotiate, and all negotiators deliberate. This article addresses the pervasive but unrefined use of emotional insight by deliberating and negotiating lawyers, and suggests that legal education could improve lawyering by adopting a fuller model of legal thinking that takes account of this epistemological emotionality. In forming the beliefs that underlie choices made during deliberation and negotiation, people rely on insights informed by past and present emotional experience. Such epistemological emotionality fuels a pre-linguistic, quasi-inductive reasoning process that enables us to draw on stored information about emotional phenomena to hypothesize about motives, behavior, and potential consequences. As deliberation moves from the individual to the collective endeavor, negotiators draw on epistemological emotionality in an iterated process of evaluating and adjusting for the impacts of each round of exchange on each participant, to maintain an effective negotiating environment. Epistemological emotionality thus fuels the processes of deliberation and negotiation that permeate legal practice, but lawyers are discouraged from refining (or even acknowledging) their use of emotional insight by a professional culture that disdains it. Disdain is inculcated by a tradition of legal education steeped in the Platonic dichotomy between cognition and emotion, in which cognition is the virtuous champion of objective truth and emotion is an amoral, primitive wilderness to be tamed by reason. However, lawyers are called upon to untangle the vexing, layered, and often emotionally-charged situations that others have finally relinquished to the care of professionals, and neglect (or abuse) of epistemological emotionality can compromise their performance. Growing recognition in other academic and professional disciplines attests to the importance of emotionally-informed knowing in reasoned human discourse - but nowhere is reasoned human discourse more important than in the deliberative enterprise of law, which requires its agents to reflect carefully about the meaning of social interactions and institutions (and accords great significance to their ultimate decisions). The piece argues that a more purposeful synthesis of emotional and analytic data in deliberation and negotiation will improve the potency with which lawyers, judges, and lawmakers approach the problems they are called upon to solve

Adaptive intelligent personalised learning (AIPL) environment

As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal.In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis

Multisensory learning in adaptive interactive systems

The main purpose of my work is to investigate multisensory perceptual learning and sensory integration in the design and development of adaptive user interfaces for educational purposes. To this aim, starting from renewed understanding from neuroscience and cognitive science on multisensory perceptual learning and sensory integration, I developed a theoretical computational model for designing multimodal learning technologies that take into account these results. Main theoretical foundations of my research are multisensory perceptual learning theories and the research on sensory processing and integration, embodied cognition theories, computational models of non-verbal and emotion communication in full-body movement, and human-computer interaction models. Finally, a computational model was applied in two case studies, based on two EU ICT-H2020 Projects, "weDRAW" and "TELMI", on which I worked during the PhD

Supporting Negotiations: Methods, Techniques, and Practice

The family of decision analysis techniques can be applied effectively to support practical negotiators in international settings. These techniques are most appropriate in support of the prenegotiation phase, when parties are diagnosing the situation, assessing their own plans and strategies, and evaluating likely reactions and outcomes. The paper identifies how these approaches have and can be used to assist negotiation practitioners, offer a rationale for the application of decision analytic approaches in terms of the particular analytical requirements of the prenegotiation period, suggests how these process-oriented tools can be integrated with substantive tools, and discusses ways in which these tools can be presented and delivered to practitioners in a practical and confidence-building manner