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Stoitsis G.,University of Alcala | Manouselis N.,Agro Know Technologies | Sanchez-Alonso S.,University of Alcala
International Journal of Technology Enhanced Learning | Year: 2012

The availability of multilingual data sets can be pivotal for learning analytics' research that is trying to investigate issues related to the use of multiple languages. Nevertheless, existing data sets store limited information about the linguistic environment in which users interact with TEL applications. This paper tries to identify which data variables and properties make sense in a multilingual analysis context, by examining the case of data coming from a learning portal. More specifically, it analyses the log files of a web portal for organic and sustainable agriculture education, trying to identify whether its linguistic profile (i.e. language of interface, metadata records and learning resources) may affect the number of the users that is attracted and their search behaviour. The paper also includes some generic recommendations related to the information that data sets could store to facilitate multilingual learning analytics. Copyright © 2012 Inderscience Enterprises Ltd. Source


Manouselis N.,Agro Know Technologies | Karagiannidis C.,University of Thessaly | Sampson D.G.,University of Piraeus
Journal of E-Learning and Knowledge Society | Year: 2014

Evaluation of recommender systems has only lately started to become more systematic, since the emphasis has long been on the experimental evaluation of algorithmic performance. Recent studies have proposed adopting a layered evaluation approach, according to which recommender systems may be decomposed into several components, evaluating each of them separately. Nevertheless, there are still no evaluation studies of recommender systems that apply a layered evaluation framework to explore how all the different components or layers of such a system may be assessed. This paper introduces layered evaluation and examines how a previously proposed layered evaluation framework for adaptive systems can be applied in the case of recommender systems. It presents the possible adaptation of this layered framework that may fit the interaction components of recommender systems. Then, it focuses on a specific recommender system and carries out a retrospective analysis of its past evaluation results under the new prism that the layered evaluation approach brings. Our analysis indicates that implementing a layeredbased evaluation of recommender systems has the potential to facilitate a more detailed and informed evaluation of such systems, allowing researchers and developers to better understand how to improve them. Source


Verbert K.,Catholic University of Leuven | Manouselis N.,Agro Know Technologies | Manouselis N.,University of Alcala | Drachsler H.,Open University of the Netherlands | Duval E.,Catholic University of Leuven
Educational Technology and Society | Year: 2012

In various research areas, the availability of open datasets is considered as key for research and application purposes. These datasets are used as benchmarks to develop new algorithms and to compare them to other algorithms in given settings. Finding such available datasets for experimentation can be a challenging task in technology enhanced learning, as there are various sources of data that have not been identified and documented exhaustively. In this paper, we provide such an analysis of datasets that can be used for research on learning and knowledge analytics. First, we present a framework for the analysis of educational datasets. Then, we analyze existing datasets along the dimensions of this framework and outline future challenges for the collection and sharing of educational datasets. © International Forum of Educational Technology & Society (IFETS). Source


Verbert K.,Catholic University of Leuven | Manouselis N.,Agro Know Technologies | Manouselis N.,University of Alcala | Ochoa X.,ESPOL Polytechnic University | And 4 more authors.
IEEE Transactions on Learning Technologies | Year: 2012

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed. © 2011 IEEE. Source


Manouselis N.,Agro Know Technologies | Kyrgiazos G.,Computer Technology Institute and Press Diophantus | Stoitsis G.,Agro Know Technologies
Journal of E-Learning and Knowledge Society | Year: 2014

Results of previous studies have indicated that the performance of recommendation algorithms depends on the characteristics of the application context. The same algorithms have shown to be performing in totally different ways when a new or evolved data set is considered, thus leading to a need for continuous monitoring of how they operate in a realistic setting. In this paper we investigate such a real life implementation of a multicriteria recommender system and try to identify the needed adjustments that need to take place in order for it to better match the requirements of its operational environment. More specifically, we examine the case of a multi-attribute collaborative filtering algorithm that has been supporting the recommendation service within a Web portal for organic and sustainable education. Our study particularly explores the experimental performance of the already implemented algorithm, as well as an alternative one, using data from the intended application, a simulated expansion of it, and from similar portals. The results of this study indicate the importance of the frequent experimental investigation of a recommender system's various design options, and the need for the exploration of adaptive implementations in real life recommender systems. Source

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