121,809 research outputs found
Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning
No full textDrachsler, H., Bogers, T., Vuorikari, R., Verbert, K., Duval, E., Manouselis, N., Beham, G., Lindstaedt, S., Stern, H., Friedrich, M., & Wolpers, M. (2010, 28 September). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. Presentation at the 1st Workshop Recommender Systems in Technology Enhanced Learning (RecSysTEL) in conjunction with 5th European Conference on Technology Enhanced Learning (EC-TEL 2010): Sustaining TEL: From Innovation to Learning and Practice, Barcelona, Spain.The presentation is based on the positioning paper of the dataTEL Theme Team of the STELLAR Network of Excellence (http://www.teleurope.eu/pg/groups/9405/datatel/) that addresses the lack of educational data sets in TEL and present ideas to overcome this situation. The accompanying paper: Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning, can be found at http://www.sciencedirect.com/science/journal/18770509 and a pre-print is available in our Dspace repository and at scribd.
The presentation starts with a description of the current situation where almost none educational data sets are publicly available. This is a strange situation as plenty of data is saved on a daily base in LMS like Moodle, Blackboard. In other domains like e-commerce it is a common practice to use publicly available data sets from different application environments (e.g. Yahoo, MovieLens) in order to evaluate algorithms and create new data products. These data sets are for instance used as benchmarks to develop new recommendation algorithms and compare them to other algorithms in certain settings.
Recommender systems are also increasingly applied in Technology Enhanced Learning field but it is still an application area that lacks such publicly available data sets. Although there is a lot of research conducted on recommender systems in TEL, they lack data sets that would allow the experimental evaluation of the performance of different recommendation algorithms using comparable, interoperable, and reusable data sets. This leads to awkward experimentation and testing such as using data sets from movies in order to evaluate educational recommendation algorithms.Stella
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
Collaborative and Semantic Information Retrieval for Technology-Enhanced Learning
No full textThe paper presents an ontological approach for enabling personalized searching framework facilitating the user access to desired contents. Through the ontologies the system will express key entities and relationships describing resources in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and concept recognition, for reasoning processes and for enabling user-friendly and intelligent content retrieva
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 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
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
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
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
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
A Multi-Language Comparison of Influences on Author Verification using Character N-Grams
No full textWe create a new multi-language corpus for author verification based on Wikipedia talkpages, and evaluate the influence that differences in topic and time have on character n-gram author profiles. Topic alignment between two texts is found to increase author verification precision, and an authors writing style is found to change over time, but not more significantly after 3 years than after 1 year.Information ArchitectureWISElectrical Engineering, Mathematics and Computer Scienc
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