Organic.Edunet Web Portal: User Satisfaction Analysis

The Organic.Edunet Web portal is a multilingual federation of educational repositories, developed in order to enhance the availability and retrieval of quality educational resources about Organic Agriculture (OA) and Agroecology (AE). This paper presents the results of an online survey that took place from March to August 2010 in order to evaluate the user satisfaction from the Organic.Edunet Web portal. A standard methodology was followed in this survey, which was based on the WebQual questionnaire with the required modifications. The collected results were analyzed and statistically processed in order to lead to the corresponding conclusions

Studying How E-Markets Evaluation Can Enhance Trust in Virtual Business Communities

One 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,

Evaluating a Personal Learning Environment for Digital Storytelling

The evaluation of flexible and personal learning environments is extremely challenging. It should not be limited to the assessment of products, but should address the quality of educative experience with close monitoring. The evaluation of a PLE using digital storytelling is even more complicated, due to the unpredictability of the usage scenarios. This paper presents an evaluation methodology for PLEs using digital storytelling, using a participatory design approach. The results from an open validation trial indicate that this methodology is able to incorporate all necessary factors and that the selected evaluation tools are appropriate for addressing the quality of educative experience

Online sharing educational content on biodiversity topics: a case study from organic agriculture and agroecology

E-Learning Technologies and Standards are emerging as the dominant way to make educational content widely available. Approaches to these technologies should be domain-independent and easily adaptable to different contexts. Organic.Edunet aims at making content on Organic Agriculture and Agroecology widely available through a single point of reference. To achieve this, the project has adopted and adapted Open Software solutions and has built upon them to offer the Organic.Edunet Web Federation Portal and the Repository Suite of Tools. This paper presents the tools that were developed in the frame of Organic.Edunet project, serving as a guide for all individuals that aim at establishing similar tools in a field such as the biodiversity

A Survey of Greek Agricultural E-Markets

The role that information technology plays in today’s business activities has led to an increase in firms using and/or deploying e-markets online. This development undoubtedly affects the agri-food sector, since a large number of agricultural firms are demonstrating or are expected to demonstrate e-commerce activities. This paper aims to provide an overview of the current status of agricultural e-markets in Greece, by presenting results from an analysis of 100 cases. Results indicate that Greek e-markets may still have a rather low degree of sophistication, but they demonstrate a strong B2B orientation, as well as an outreach for international customer bases.Internet, e-commerce, e-markets, agriculture, agri-food sector, survey, Consumer/Household Economics, Marketing,

Preface to Proceedings of the 1st Workshop on Recommender Systems in Technology Enhanced Learning (RecSysTEL 2010)

AbstractTechnology enhanced learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of both individuals and organisations. It is an application domain that generally addresses all types of technology research & development aiming to support teaching and learning activities. 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. There are plenty of resources available on the Web, both in terms of digital learning content and people resources (e.g. other learners, experts, tutors) that can be used to facilitate teaching and learning tasks. The challenge 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.The 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL) builds upon the tradition of a series of workshops on Social Information Retrieval for Technology Enhanced Learning (SIRTEL), Context-Aware Recommendation for Learning and Towards User Modelling and Adaptive Systems for All (TUMAS-A)a. RecSysTEL was organised jointly by the 4th ACM Conference on Recommender Systems (RecSys 2010) and the 5th European Conference on Technology Enhanced Learning (EC-TEL 2010), on 29–30 September 2010 in Barcelona, Spain. Its main goal was to bring together researchers and practitioners who are working on topics related to the design, development and testing of recommender systems in educational settings as well as present the current status of research in this area and create cross-disciplinary liaisons between the RecSys and ECTEL communities. Overall, its contributions outline the rich potential of TEL as an application area for recommender systems and identify the challenges of developing such systems in a TEL context

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

Panorama of Recommender Systems to Support Learning

This 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

Dataset-driven research for improving recommender systems for learning

Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.dataTEL, STELLAR, AlterEgo, VOA3