Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Adaptive Intelligent Tutoring System for learning Computer Theory

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned

Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis

In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments

Personalised Learning: Educational, Technological and Standardisation Perspective

The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective

Personalised Learning: Educational, Technological and Standardisation Perspective

The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective.The e-Learning paradigm shift capitalises on two main aspect: the elimination of the barriers of time and distance, and the personalisation of the learners’ experience. The current trend in education and training emphasises on identifying methods and tools for delivering just-in-time, on-demand knowledge experiences tailored individual learners, taking into consideration their differences in skills level, perspectives, culture and other educational contexts. This paper reviews the shift towards personalised learning, from an educational, technological and standardisation perspective

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Informing and Performing: A Study ComparingAdaptive Learning to Traditional Learning

Technology has transformed education, perhaps most evidently in course delivery options. However, compelling questions remain about how technology impacts learning. Adaptive learning tools are technology-based artifacts that interact with learners and vary presentation based upon that interaction. This study examines completion rates and exercise scores for students assigned adaptive learning exercises and compares them to completion rates and quiz scores for students assigned objective-type quizzes in a university digital literacy course. Current research explores the hypothesis that adapting instruction to an individual’s learning style results in better learning outcomes. Computer technology has long been seen as an answer to the scalability and cost of individualized instruction. Adaptive learning is touted as a potential game-changer in higher education, a panacea with which institutions may solve the riddle of the iron triangle: quality, cost, and access. Though the research is scant, this study and a few others like it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Clearly, more research like this study, some of it from the perspective of adaptive learning systems as informing systems, is needed before the far-reaching promise of advanced learning systems can be realized

Designing for change: mash-up personal learning environments

Institutions for formal education and most work places are equipped today with at least some kind of tools that bring together people and content artefacts in learning activities to support them in constructing and processing information and knowledge. For almost half a century, science and practice have been discussing models on how to bring personalisation through digital means to these environments. Learning environments and their construction as well as maintenance makes up the most crucial part of the learning process and the desired learning outcomes and theories should take this into account. Instruction itself as the predominant paradigm has to step down. The learning environment is an (if not 'the�) important outcome of a learning process, not just a stage to perform a 'learning play'. For these good reasons, we therefore consider instructional design theories to be flawed. In this article we first clarify key concepts and assumptions for personalised learning environments. Afterwards, we summarise our critique on the contemporary models for personalised adaptive learning. Subsequently, we propose our alternative, i.e. the concept of a mash-up personal learning environment that provides adaptation mechanisms for learning environment construction and maintenance. The web application mash-up solution allows learners to reuse existing (web-based) tools plus services. Our alternative, LISL is a design language model for creating, managing, maintaining, and learning about learning environment design; it is complemented by a proof of concept, the MUPPLE platform. We demonstrate this approach with a prototypical implementation and a – we think – comprehensible example. Finally, we round up the article with a discussion on possible extensions of this new model and open problems

Bored with point and click?

Computers have the potential to be exploited as one of the most exciting examples of instructional media. Yet designers often fail to realize this potential. This is, in part, due to the limitations of hardware and software and, in part, due to the lack of good theory developed through conclusive research. Good examples of computer-based learning may owe more to the imaginative flair of the courseware designer than they do to the application of explicit design guidelines and good learning theory. This paper will therefore consider a variety of issues that may be blocking theoretical development and draw conclusions for future courses of action. This starts with a statement of the problem, first by considering the macro and micro issues, and then by looking at a recent call for help in ComputerBased Learning Environment (CBLE) design. Next, the contribution of instructional design theories will be presented together with a way forward for investigating the issues. Finally the implications for future progress are presented