Effects of the ISIS Recommender System for navigation support in self-organised Learning Networks

Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Berlanga, A., Nadolski, R., Waterink, W., Boers, N., & Koper, R. (2008). Effects of the ISIS Recommender System for navigation support in self-organized Learning Networks. Presentation at the 4th conference Edumedia Conference 2008 Self-organized learning in the interactive Web – Changing learning culture? 1st Workshop on Technology Support for Self-Organized Learners (TSSOL08). June, 3, 2008, Salzburg, Austria.Presentation of the experimental effects of the ISIS recommender system for self-organized Learning Networks. In this study we applied a recommender system that was based on stereotype filtering algorithm and a domain ontology in an experimental Moodle environment. For the stereotype filtering part we took into account successful completed learning activities by other learners as implicit ratings. The ontology was mainly used to cover the ‘cold-start’ problem. Therefore, we took advantage of the explicit association of domain experts in order to recommend learning activities. The recommendation was based on quantitative information about successful completions of activities by learners with similar preferences.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Navigation Support for Learners in Informal Learning Networks

Learners increasingly use the Internet as source to find suitable information for their learning needs. This especially applies to informal learning that takes place during daily activities that are related to work and private life. Unfortunately, the Internet is overwhelming which makes it difficult to get an overview and to select the most suitable information. Navigation support may help to reduce time and costs involved selecting suitable information on the Internet. Promising technologies are recommender systems known from e-commerce systems like Amazon.com. They match customers with a similar taste of products and create a kind ‘neighborhood’ of likeminded customers. They look for related products purchased by the neighbors and recommend these to the current customer. In this thesis we explore the application of recommender systems to offer personalized navigation support to learners in informal Learning Networks. A model of a recommender system for informal Learning Networks is proposed that takes into account pedagogical characteristics and combines them with collaborative filtering algorithms. Which learning activities are most suitable depends on needs, preferences and goals of individual learners. Following this approach we have conducted two empirical studies. The results of these studies showed that the application of recommender systems for navigation support in informal Learning Networks is promising when supporting learners to select most suitable learning activities according to their individual needs, preferences and goals. Based on these results we introduce a technical prototype which allows us to offer navigation support to lifelong learners in informal Learning Networks

Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning

Drachsler, H., Hummel, H. G. K., & Koper, R. (2009). Identifying the Goal, User model and Conditions of Recommender Systems for Formal and Informal Learning. Journal of Digital Information, 10(2), 4-24.The following article addresses open questions of the discussions in the first SIRTEL workshop at the EC-TEL conference 2007. It argues why personal recommender systems have to be adjusted to the specific characteristics of learning to support lifelong learners. Personal recommender systems strongly depend on the context or domain they operate in, and it is often not possible to take one recommender system from one context and transfer it to another context or domain. The article describes a number of distinct differences for personalized recommendation to consumers in contrast to recommendations to learners. Similarities and differences are translated into specific demands for learning and specific requirements for personal recommendation systems. It further suggests an evaluation approach for recommender systems in technology-enhanced learning.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Ecology of social search for learning resources

Vuorikari, R., & Koper, R. (2009). Ecology of social search for learning resources. Campus-Wide Information Systems, 26(4), 272-286.Purpose: This paper deals with user-generated Interest indicators (ratings, bookmarks and tags). We answer two research questions: can search strategies based on Social Information Retrieval (SIR) make the discovery of learning resources more efficient for users, and can Community search help users discover a wider variety of cross-boundary resources. By cross-boundary we mean that the user and resource come from different countries and that the language of the resource is different from that of the user’s mother tongue. Design: We focus on a portal that access a federation of multilingual learning resource repositories. We collect users’ attentional metadata based on a server-side logging scheme and use this empirical data to answer two hypotheses. Findings: The search-play-annotation ratio is more efficient with Social Information Retrieval strategies, but Community browsing alone does not help users to discover more cross-boundary resources. Practical implications: By social tagging and bookmarking resources from a variety of repositories, users create underlying connections between resources that otherwise do not cross-reference, for example, via hyperlinks. This is important for bringing them under the umbrella of SIR methods. Future studies should include testing wider range of SIR methods to leverage these user-made connections between resources that originate from a number of countries and are in a variety of languages. Originality: The use of attentional metadata to model the ecology of social search adds value to the actors of learning object economy, e.g. educational institutions, digital libraries and their managers, content providers, policy makers, educators and learners

Development of a Reading Material Recommender System Based On Design Science Research Approach

Using design science research (DSR), we outline the construction and evaluation of a recommender system incorporated into an existing computer-supported collaborative learning environment. Drawing from Clark’s communication theory and a user-centered design methodology, the proposed design aims to prevent users from having to develop their own conversational overload coping strategies detrimental to learning within large discussions. Two experiments were carried out to investigate the merits of three collaborative filtering recommender systems. Findings from the first experiment show that the constrained Pearson Correlation Coefficient (PCC) similarity metric produced the most accurate recommendations. Consistently, users reported that constrained PCC based recommendations served best to their needs, which prompted users to read more posts. Results from the second experiment strikingly suggest that constrained PCC based recommendations simplified users’ navigation in large discussions by acting as implicit indicators of common ground, freeing users from having to develop their own coping strategies

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Exploring the impact of extroversion on the selection of learning materials

The Internet provides access to many learning materials that could complement class room teaching. An educational recommender system can aid learners to find learning materials most suitable to them. The best learning materials will depend on learner characteristics. This paper investigates the influence of learner personality. In particular, it describes a study in the language learning domain that explores the relation between learners’ extroversion and the extent to which learning materials are perceived to be enjoyable and to increase their confidence and skills. We found positive correlations between extroversion and these criteria for social and active learning materials

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