Agro Know Technologies

Athens, Greece

Agro Know Technologies

Athens, Greece
SEARCH FILTERS
Time filter
Source Type

Stoitsis G.,University of Alcalá | Manouselis N.,Agro Know Technologies | Sanchez-Alonso S.,University of Alcalá
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.


Palavitsinis N.,University of Alcalá | Manouselis N.,Agro Know Technologies | Sanchez-Alonso S.,University of Alcalá
Electronic Library | Year: 2014

Purpose - This paper aims to address the issue of poor quality of metadata records describing educational content in Learning Object Repositories (LORs). Through this, it aims to improve the discoverability of learning objects in such LORs through a structured process that supports metadata creation. Design/methodology/approach - This paper presents a proposed metadata quality assessment certification process for LORs. The process was designed as a generic approach that may be customized to fit various application domains. Findings - Initial results from the application of the process in the context of a specific LOR report an improvement of the quality of about 11,000 metadata records. More specifically, metadata completeness for all metadata elements used in the repository under examination was significantly improved from 30 percent to 85 percent. Research limitations/implications - The main limitation of the findings is that they come from the application of the proposed process on a relatively small repository, which does not allow safe generalizations without further experimental study in bigger ones where resources and requirements scale up. Practical implications - This paper addresses implications for the development of a repository in the educational domain, identifying issues related to the metadata application profile, the support to domain experts and the mechanisms that may be put in place to support metadata creation. Originality/value - The value and also the originality of the approach presented lies within the fact the proposed approach quantifies issues related to metadata creation and management by studying actions and perceptions of stakeholders who are involved in the repository lifecycle. Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.


Verbert K.,Catholic University of Leuven | Manouselis N.,Agro Know Technologies | Manouselis N.,University of Alcalá | 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).


Verbert K.,Catholic University of Leuven | Manouselis N.,Agro Know Technologies | Manouselis N.,University of Alcalá | 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.


Tzikopoulos A.,Ellinogermaniki Agogi | Manouselis N.,Agro Know Technologies | Kastrantas K.,Agro Know Technologies | Costopoulou C.,Agricultural University of Athens
Program | Year: 2012

Purpose: Away from central public authorities, regional (also called rural) enterprises do not have direct, physical access to all the services that governmental or public agencies offer. Very often, these services are essential for enterprises, mostly small and medium-sized enterprises (SMEs), in such areas, in order to perform their business operations. This paper aims to present an example of how such types of information management and use took place in the case of familiarizing rural SMEs with the use of e-government. Design/methodology/approach: This paper is a case study of how a practical application is designed and developed for the blended training of rural SMEs. First of all, an identification of the main information resources that will be stored, annotated, shared and accessed through the system took place. Then, an outline of the general architecture and user roles involved was developed. System analysis and specification using Unified Modeling Language (UML) then took place. This was accompanied by design and specification of the database, based on appropriate metadata schemas for describing the information resources. The whole process was completed by the design and prototype development of the interface, which was put into public operation and testing with a sample set of real users. Findings: Although there are several information management systems focusing on the education and training of rural stakeholders, their learning resources are not directly relevant to SMEs' needs. The main finding of this paper is that it shows a complete case study of designing, developing and evaluating an information technology application for rural businesses. Originality/value: The value of the approach presented here is the combination of training resources in an information system with a blended training approach, so that it better matches the learning needs of SMEs. Through this web-based environment, rural SMEs are able to find information on the e-government services offered in their region, as well as gaining access to e-learning content on how they can use such services. © Emerald Group Publishing Limited.


Karampiperis P.,Greek National Center For Scientific Research | Manouselis N.,Agro Know Technologies | Konstantopoulos S.,Greek National Center For Scientific Research
Procedia Computer Science | Year: 2012

As the trend to open up data and provide them freely on the Internet intensifies, the opportunities to create added value by combining and cross-indexing heterogeneous data at a large scale increase. To seize these opportunities we need infrastructure that is not only efficient, real-time responsive and scalable but is also flexible and robust enough to welcome data in any schema and form and to transparently relegate and translate queries from a unifying end-point to the multitude of data services that make up the open data cloud. Transparent relegation and translation relies on detailed and accurate data summaries and other data source annotations, and with increased data volumes and heterogeneity managing these annotations, it becomes by itself a challenging data problem. In this position paper we discuss (a) how a scalable and robust semantic storage can be developed, using indexing algorithms that can take advantage of resource naming conventions and other natural groupings of URIs to compress data source annotations about extremely large datasets; and (b) how query decomposition, source selection, and distributed querying methods can be designed, that take advantage of such algorithms to implement a scalable and robust infrastructure for data service federation. © 2012 Published by Elsevier Ltd.


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.


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.


Manouselis N.,Agro know Technologies | Verbert K.,TU Eindhoven
Procedia Computer Science | Year: 2013

Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users - such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset - And indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. © 2013 The Authors. Published by Elsevier B.V.


Manouselis N.,Agro Know Technologies | Kvrgiazos G.,Computer Technology Institute and Press Diophantus | Stoitsis G.,Agro Know Technologies
CEUR Workshop Proceedings | Year: 2012

Results of previous studies have indicated that the same recommendation algorithms perform in totally different ways when a different dataset is considered, thus leading to the need for continuous monitoring of how algorithms perform in a realistic and evolving setting. In this paper we investigate such a real life implementation of a multi-criteria recommender system and try to identify 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 sen-ice within a Web portal for organic and sustainable education.

Loading Agro Know Technologies collaborators
Loading Agro Know Technologies collaborators