Learning styles, personalisation and adaptable e-learning

Common Learning Management Systems (for example Moodle [1] and Blackboard [2]) are limited in the amount of personalisation that they can offer the learner. They are used widely and do offer a number of tools for instructors to enable them to create and manage courses, however, they do not allow for the learner to have a unique personalised learning experience. The e-Learning platform iLearn offers personalisation for the learner in a number of ways and one way is to offer the specific learning material to the learner based on the learner's learning style. Learning styles and how we learn is a vast research area. Brusilovsky and Millan [3] state that learning styles are typically defined as the way people prefer to learn. Examples of commonly used learning styles are Kolb Learning Styles Theory [4], Felder and Silverman Index of Learning Styles [5], VARK [6] and Honey and Mumford Index of Learning Styles [7] and many research projects (SMILE [8], INSPIRE [9], iWeaver [10] amonst others) attempt to incorporate these learning styles into adaptive e-Learning systems. This paper describes how learning styles are currently being used within the area of adaptive e-Learning. The paper then gives an overview of the iLearn project and also how iLearn is using the VARK learning style to enhance the platform's personalisation and adaptability for the learner. This research also describes the system's design and how the learning style is incorporated into the system design and semantic framework within the learner's profile.

Evaluation of a suitable learning style for iLearn: a personalised e-learning platform

Brusilovsky and Millan (2007) state that learning styles are typically defined as the way people prefer to learn. Learning styles and how we learn is a vast research area and many research projects (SMILE, INSPIRE, iWeaver amongst others) attempt to incorporate these learning styles into e-Learning systems. This paper describes commonly used learning styles and how they are currently being used within the area of adaptive e-Learning. This work also builds upon current research and evaluates learning styles using criteria proposed by Sampson and Karagiannidis (2004) in order to select a suitable learning methodology for the iLearn e-Learning platform. The Sampson and Karagiannidis (2004) criteria is adapted for the purpose of the research and describes the measurability, time effectiveness and descriptiveness and prescriptiveness of the specific learning style. A suitable learning style for the iLearn e-Learning platform is then proposed within the paper and finally the research briefly introduces how the chosen learning style will be used for the proposed e-Learning platform.

Personalised e-learning, semantic web and learning ontologies

Kurzel(2004) points out that researchers in e-learning and educational technologists, in a quest to provide improved Learning Environments (LE) for students are focusing on personalising the experience through a Learning Management System (LMS) that attempts to tailor the LE to the individual (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez,2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman; 2003). According to Kurzel (2004) this tailoring can have an impact on content and how it’s accessed; the media forms used; method of instruction employed and the learning styles supported. This project is aiming to move personalisation forward to the next generation, by tackling the issue of Personalised e-Learning platforms as pre-requisites for building and generating individualised learning solutions. The proposed development is to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system’s processing engine. This paper will discuss some of our early work and ideas.

Semantic web and e-learning

With emergence of "Semantic Web" there has been much discussion about the impact of technologies such as XML and RDF on the way we use the Web for developing e-learning applications and perhaps more importantly on how we can personalise these applications. Personalisation of e-learning is viewed by many authors (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez, 2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman, 2003) as the key challenge for the learning technologists. According to Kurzel (2004) the tailoring of e-learning applications can have an impact on content and how it's accesses; the media forms used; method of instruction employed and the learning styles supported. This paper will report on a research project currently underway at the eCentre in University of Greenwich which is exploring different approaches and methodologies to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system's processing engine.

Reflective practice: the enduring influence of Kolb’s Experiential Learning Theory

Since 1984 David Kolb’s Experiential Learning Theory (ELT) has been a leading influence in the development of learner-centred pedagogy in management and business. It forms the basis of Kolb’s own Learning Styles’ Inventory and those of other authors including Honey and Mumford (2000). It also provides powerful underpinning for the emphasis, nay insistence, on reflection as a way of learning and the use of reflective practice in the preparation of students for business and management and other professions. In this paper, we confirm that Kolb’s ELT is still the most commonly cited source used in relation to reflective practice. Kolb himself continues to propound its relevance to teaching and learning in general. However, we also review some of the criticisms that ELT has attracted over the years and advance new criticisms that challenge its relevance to higher education and its validity as a model for formal, intentional learning.

Can the use of supplementary video and audio materials support student understanding of 3D animation? Some results, analysis and conclusions

A project within a computing department at the University of Greenwich, has been carried out to identify whether podcasting can be used to help understanding and learning of a subject (3D Animation). We know that the benefits of podcasting in education (HE) can be justified, [1]; [2]; [3]; [4]; [5]; [6] and that some success has been proven, but this paper aims to report the results of a term-long project that provided podcast materials for students to help support their learning using Xserve and Podcast Producer technology. Findings in a previous study [6] identified podcasting as a way to diversify learning and provde a more personalised learning experience for students, as well as being able to provide access to a greater mix of learning styles [7]. Finally this paper aims to present the method of capture and distribution, the methodologies of the study, analysis of results, and conclusions that relate to podcasting and enhanced supported learning.