164 research outputs found
Studying How E-Markets Evaluation Can Enhance Trust in Virtual Business Communities
No full textOne of the major drawbacks of conducting business online is the raised level of risk associated with business transactions. Potential business partners usually have limited information about each others reliability or product / service quality before an online transaction. In this paper, we focus on the problem of selecting a trustful electronic market (e-market), in order to perform business transactions with it. In particular, we examine how the decision of selecting an appropriate e-market can be facilitated by an e-market recommendation algorithm. For this purpose, a metadata model for collecting and storing e-market evaluations from the members of a virtual business community in a reusable and interoperable manner is introduced. Then, an e-market recommendation algorithm that can synthesize existing e-market evaluations stored using the metadata model, is designed. Finally, a scenario of how the presented e-market recommendation algorithm can support a virtual agribusiness community of the organic agriculture sector is discussed.E-market, metadata, recommender system, virtual community, Institutional and Behavioral Economics, Marketing,
Recommender systems for learning
No full textRecommender systems are extremely popular as a research and application area, with various interesting application domains such as e-commerce, entertainment, and others. Nevertheless, it was only around early 2000 when the first notable applications appeared in the domain of education, since relevant work was generally considered to be connected to the area of adaptive educational systems.
Today, research around recommender systems in an educational context has significantly increased. Responding to this growing interest, this book expands the relevant chapter on Recommender Systems in Technology Enhanced Learning (by Manouselis, Drachsler, Vuorikari, Hummel and Koper) that was published in the Springer Recommender Systems Handbook (2011) and provides an extensive and in depth analysis of the recommender systems currently found in relevant literature. This book aims to briefly introduce recommender systems and to discuss a wide and representative sample of issues that people working on recommender systems for learning should be expecting to face. It serves as an overview of work in this domain and therefore especially addresses people that start studying or researching relevant topics and want to position their work in the overall landscape.sponsorship: The work presented in this book has been carried out with European Commission funding support. More specifically, the work of Nikos Manouselis has been supported by the EU project VOA3R - 250525 of the CIP
PSP Programme (http://voa3r.eu). The work of Hendrik Drachsler was funded by the NeLLL funding body in the context of the AlterEgo project.
Katrien Verbert is a Postdoctoral Fellow of the Research Foundation - Flanders (FWO).
Part of this work was also supported by the SIG dataTEL of the European Association of Technology Enhanced Learning and the former dataTEL Theme Team of the STELLAR Network of Excellence (grant agreement no. 231913).status: Publishe
Recommender systems for technology enhanced learning: research trends and applications
No full text© Springer Science+Business Media New York 2014. As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years. Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices. Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.status: Publishe
A trust-based social recommender for teachers
No full textFazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2012). A trust-based social recommender for teachers. In N. Manouselis, H. Drachsler, K. Verbert, & O. C. Santos (Eds.), 2nd Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL 2012) in conjunction with the 7th European Conference on Technology Enhanced Learning (EC-TEL 2012) (pp. 49-60). September, 18-19, 2012, Saarbrücken, Germany.Online communities and networked learning provide teachers with social learning opportunities to interact and collaborate with others in order to develop their personal and professional skills. In this paper, Learning Networks are presented as an open infrastructure to provide teachers with such learning opportunities. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable resources for them. In this paper, recommender systems are introduced as a potential solution to address this issue. Unfortunately, most of the educational recommender systems cannot make accurate recommendations due to the sparsity of the educational datasets. To overcome this problem, we propose a research approach that describes how one may take advantage of the social data which are obtained from monitoring the activities of teachers while they are using our social recommender.NELLL, Open Discovery Space (ODS
What If Annotations Were Reusable: A Preliminary Discussion
No full textManouselis, N., & Vuorikari, R. (2009). What If Annotations Were Reusable: A Preliminary Discussion. In M. Spaniol, Q. Li, R. Klamma & R. W. H. Lau (Eds.), Proceedings of the 8th International Conference Advances in Web Based Learning - ICWL 2009 (pp. 255-264). August, 19-21, 2009, Aachen, Germany: Lecture Notes in Computer Science 5686; Berlin, Heidelberg: Springer-Verlag.This paper discusses the rationale for the representation of user feedback in a structured and reusable format so that it can be reused by different recommender systems. We emphasize how information about the context can be included in such a representation. This work-in-progress takes place in the context of two large European initiatives that set up collections of digital educational resources in distributed repositories to serve the needs of different
user communities, and to collect user feedback such as ratings, bookmarks and tags related to the resources. The overall aim is to facilitate the exchange and reuse of their data sets in order to support recommendation of appropriate resources to the end users.European Commission (project No ECP-2006-EDU-410012 Organic.Edunet) and with a stipend from HS 100-vuotissäätiö
Introduction slides for RecSysTEL workshop
No full textDrachsler, H., Manouselis, N., Santos, O., & Verbert, K. (2012, 19 September). Introduction slides for RecSysTEL workshop. Presentation at the 2nd Workshop on Recommender Systems for Technolgy Enhancend Learning (RecSysTEL 2012) at EC-TEL 2012, Saarbrücken, Germany.Introduction slides for RecSysTEL workshop at ECTEL12 conference, Saarbruecken, Germany.AlterEgo, dataTEL, Open Discovery Spac
Towards a Social Trust-aware Recommender for Teachers
No full textFazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2014). Towards a Social Trust-aware Recommender for Teachers. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 177-194): Springer New York.Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this . The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trust-based recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the re-quirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical ev-idence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ul-timately be deployed in the ODS platform.NELLL, EU 7th framework Open Discovery Spac
Comparing different metadata application profiles for agricultural learning repositories
No full textAgricultural learning repositories can provide new opportunities for sharing, accessing, using and reusing learning resources online. Metadata plays a crucial role in such systems: apart from simply indexing resources, metadata makes it easier to discover a learning resource in a repository, as well as to decide about ways to use it for teaching or learning purposes. In the context of agricultural education and training, a variety of appropriate metadata standards may be selected, adapted and implemented for a learning repository. In this paper we introduce the concept of metadata for agricultural learning resources, and compare two particular cases: one application profile based on the Dublin Core Metadata Element Set (DCMES) and the other based on the IEEE Learning Object Metadata (LOM). The paper attempts to identify similarities and differences between the two case studies and to outline issues that have to be resolved in order to harmonize such efforts
Panorama of Recommender Systems to Support Learning
No full textThis chapter presents an analysis of recommender systems in TechnologyEnhanced
Learning along their 15 years existence (2000-2014). All recommender
systems considered for the review aim to support educational stakeholders by personalising the learning process. In this meta-review 82 recommender systems from
35 different countries have been investigated and categorised according to a given
classification framework. The reviewed systems have been classified into 7 clusters
according to their characteristics and analysed for their contribution to the evolution
of the RecSysTEL research field. Current challenges have been identified to lead the work of the forthcoming years.Hendrik Drachsler has been partly supported by the FP7 EU Project LACE (619424).
Katrien Verbert is a post-doctoral fellow of the Research Foundation Flanders
(FWO). Olga C. Santos would like to acknowledge that her contributions to this
work have been carried out within the project Multimodal approaches for Affective
Modelling in Inclusive Personalized Educational scenarios in intelligent Contexts
(MAMIPEC -TIN2011-29221-C03-01). Nikos Manouselis has been partially supported
with funding CIP-PSP Open Discovery Space (297229
How Recommender Systems in Technology-Enhanced Learning depend on Context
No full textDrachsler, H., & Manouselis, N. (2009). How Recommender Systems in Technology-Enhanced Learning depend on Context. Presentation given at the 1st workshop on Context-aware Recommender Systems for Learning at the Alpine Rendez-Vous 2009. November, 30 - December, 3, 2009, Garmisch-Patenkirchen, Germany.Technology-Enhanced Learning (TEL) can roughly be differentiated into formal and non-formal learning settings. Both settings offer a rather different context that has to be taken into account by recommender systems in order to offer most suitable information to individual learners.
Formal learning, being usually organized according to some curriculum, traditionally occurs in teacher-directed environments with person-to-person interactions. Non-formal learning is described as a learning phase of lifelong learners who are not participating in any formal learning context. They are acting more self-directed and they are responsible for their own learning pace and path. In addition, the learning content for their learning nowadays come from many different Web 2.0 sources like blogs, social bookmarking tools, or sildeshare. The learning process is also not designed by an institution or responsible teachers like in formal learning, but it depends to a large extent on individual preferences learners have or choices that learners take.
Depending on the learning settings, the aims of TEL systems, their environmental conditions, and the tasks that they support also change. Thus, considering the way TEL context variables vary according to the adopted setting, the information needs of the targeted users change. This can greatly affect the design of recommender systems for the different learning settings.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
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