1,525 research outputs found
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
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
Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities
Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed
Combining Social- and Information-based Approaches for Personalised Recommendation on Sequencing Learning Activities
Hummel, H. G. K., Van den Berg, E. J., Berlanga, A. J., Drachsler, H., Janssen, J., Nadolski, R.J., & Koper, E.J.R. (2007). Combining social- and information-based approaches for personalised recommendation on sequencing learning activities. International Journal of Learning Technology, 3(2), 152-168.Lifelong learners who assign learning activities (from multiple sources) to attain certain learning goals throughout their lives need to know which learning activities are (most) suitable and in which sequence these should be performed. Learners need support in this way finding process (selection and sequencing), and we argue this could be provided by using personalised recommender systems. To enable personalisation, collaborative filtering could use information about learners and learning activities, since their alignment contributes to learning efficiency. A model for way finding has been developed that presents personalised recommendations in relation to information about learning goals, learning activities and learners. A personalised recommender system has been developed accordingly, and recommends learners on the best next learning activities. Both model and system combine social-based (i.e., completion data from other learners) and information-based (i.e., metadata from learner profiles and learning activities) approaches to recommend the best next learning activity to be completed.This work has been sponsored by the EU project TENCompetenc
Defining adaptive learning design templates for combining design and runtime adaptation in aLFanet
Adaptive features are expected to improve the effectiveness of the
learning process in online learning. Nevertheless, most current adaptive sys-tems
do not deal with combining design and runtime adaptations. To take ad-vantage
of this combination a new adaptive iLMS based on standards, called
aLFanet, is being developed. The system includes: (i) an authoring tool to de-velop
courses IMS-LD compliant, (ii) an adaptive engine based on a multi-agent
architecture which is intended to cope with several adaptive tasks for
various types of users (learners, authors and tutors), (iii) a set of advanced
pedagogical scenarios that combine design and runtime adaptations to make the
authoring of these type of adaptive courses feasible. In this paper we focus on
the types of adaptations and the process we have defined to facilitate the con-struction
of the adaptive scenarios.EC 5th Framework IST-2001-3328
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