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

Supporting the tutor in the design and support of adaptive e-learning

The further development and deployment of e-learning faces a number of threats. First, in order to meet the increasing demands of learners, staff have to develop and plan a wide and complex variety of learning activities that, in line with contemporary pedagogical models, adapt to the learners’ individual needs. Second, the deployment of e-learning, and therewith the freedom to design the appropriate kind of activities is bound by strict economical conditions, i.e. the amount of time available to staff to support the learning process. In this thesis two models have been developed and implemented that each address a different need. The first model covers the need to support the design task of staff, the second one the need to support the staff in supervising and giving guidance to students' learning activities. More specifically, the first model alleviates the design task by offering a set of connected design and runtime tools that facilitate adaptive e-learning. The second model alleviates the support task by invoking the knowledge and skills of fellow-students. Both models have been validated in near-real-world task settings

An evaluation of scaffolding for virtual interactive tutorials

Scaffolding refers to a temporary support framework used during construction. Applied to teaching and learning it describes measures to support a learner to become confident and self-reliant in a subject. In a Web environment scaffolding features need to replace the instructor. We discuss our approach to Web-based scaffolding based on the cognitive apprenticeship and activity theories. We suggest a set of four scaffold types that have made our scaffolding-supported virtual interactive tutorial successful. We present a novel evaluation approach for virtual tutorials that is embedded into an iterative, evolutionary instructional design

Design of a Learner-Directed E-Learning Model

How can one create online educational material that support and motivate students in guiding their own learning and make meaningful instructional decisions? One of the main focuses on designing e-learning is about creating an environment where learners can actively assume control and take responsibility for their own learning with little or no guidance from the tutors. This research aims to discover a new way to design learning that would cater to individual choices and preferences. The idea goes beyond learner-centred design; it is about learner control and direction. As an option, learners should be able to choose to be in the driver’s seat, to direct their own learning journey. As a starting point, this research explores the use of two educational theories - Experiential Learning Theory (ELT) and Self-Regulated Learning (SRL) theory as the underpinning instructional design for a Learner-Directed Model to support students’ online learning in both domain knowledge and meta knowledge in the subject of computer programming. One unit material from an online Introduction to Java Programming course has been redesigned based on the proposed Learner-Directed Model for the experimental design study. The study involved a total of 35 participants divided randomly into one Experimental Group and one Control Group. They were assigned to either a Learner-Directed Model (Experimental Group) or a linear model (Control Group). Pre/post tests, survey, follow-up interview as well as log file analysis were instruments used for assessing students’ domain knowledge, meta knowledge and their attitudes for their overall learning experience. Learning experience is further broken down into perceived ease of use and user satisfaction; system usability; learner experience; and perceived controllability. The results of the study have revealed that there is statistically significant difference between the survey results for the Experimental Group and the Control Group. The Experimental Group reported a higher level of overall learning experience and better attitudes in general. However, there was no statistically significant difference existing between the two groups on the domain and meta level knowledge improvement. Based on these results, I have proposed further research directions and put forward a number of recommendations and suggestions on learner-directed e-learning design

Personalised trails and learner profiling in an e-learning environment

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

A note on organizational learning and knowledge sharing in the context of communities of practice

Please, cite this publication as: Antonova, A. & Gourova, E. (2006). A note on organizational learning and knowledge sharing in the context of communities of practice. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. September 12th, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.orgThe knowledge management (KM) literature emphasizes the impact of human factors for successful implementation of KM within the organization. Isolated initiatives for promoting learning organization and team collaboration, without taking consideration of the knowledge sharing limitations and constraints can defeat further development of KM culture. As an effective instrument for knowledge sharing, communities of practice (CoP) are appearing to overcome these constraints and to foster human collaboration.This work has been sponsored by the EU project TENCompetenc