Latest Articles from JUCS - Journal of Universal Computer Science Latest 24 Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Fri, 29 Mar 2024 12:54:49 +0200 Pensoft FeedCreator https://lib.jucs.org/i/logo.jpg Latest Articles from JUCS - Journal of Universal Computer Science https://lib.jucs.org/ Natural Language Enhancement for English Teaching Using Character-Level Recurrent Neural Network with Back Propagation Neural Network based Classification by Deep Learning Architectures https://lib.jucs.org/article/94162/ JUCS - Journal of Universal Computer Science 28(9): 984-1000

DOI: 10.3897/jucs.94162

Authors: Zhiling Yang

Abstract: Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students' English learning behaviour, replacing teachers in part to answer students' questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
English Teaching in Artificial Intelligence-based Higher Vocational Education Using Machine Learning Techniques for Students’ Feedback Analysis and Course Selection Recommendation https://lib.jucs.org/article/94160/ JUCS - Journal of Universal Computer Science 28(9): 898-915

DOI: 10.3897/jucs.94160

Authors: Xin Ma

Abstract: Higher vocational education is a self-contained method of higher education that is aligned with global productivity and economic development. Its goal is to develop talented workers who contribute significantly to the economy and industry. Teaching analysis, teaching strategy, teaching practice, and assessment are all part of the course design process in high vocational education. Teaching assessment is one of the most effective methods for improving the quality of course teaching among teaching processes. This research proposes novel techniques in English teaching based on artificial intelligence for course selection based on students' feedback. Here, the dataset has been collected based on the students’ feedback on courses for Higher Vocational Education in English teaching. This dataset has been processed to remove invalid data, missing values, and noise. The processed data features have been dimensionality reduction integrated with K-means neural network. And the extracted features have been classified with higher accuracy using recursive elimination-based convolutional neural network. Based on this feedback data classification, recommendation for courses in Higher Vocational Education in English teaching has been suggested. The experimental analysis shows various students' feedback dataset validation and training in terms of accuracy of 96%, precision of 92%, recall of 93%, RMSE of 68%, and computational time of 65%.

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Research Article Wed, 28 Sep 2022 10:00:00 +0300
A Probabilistic Multi-Objective Artificial Bee Colony Algorithm for Gene Selection https://lib.jucs.org/article/22605/ JUCS - Journal of Universal Computer Science 25(4): 418-443

DOI: 10.3217/jucs-025-04-0418

Authors: Zeynep Ozger, Bulent Bolat, Banu Diri

Abstract: Microarray technology is widely used to report gene expression data. The inclusion of many features and few samples is one of the characteristic features of this platform. In order to define significant genes for a particular disease, the problem of high-dimensionality microarray data should be overcome. The Artificial Bee Colony (ABC) Algorithm is a successful meta-heuristic algorithm that solves optimization problems effectively. In this paper, we propose a hybrid gene selection method for discriminatively selecting genes. We propose a new probabilistic binary Artificial Bee Colony Algorithm, namely PrBABC, that is hybridized with three different filter methods. The proposed method is applied to nine microarray datasets in order to detect distinctive genes for classifying cancer data. Results are compared with other wellknown meta-heuristic algorithms: Binary Differential Evolution Algorithm (BinDE), Binary Particle Swarm Optimization Algorithm (BinPSO), and Genetic Algorithm (GA), as well as with other methods in the literature. Experimental results show that the probabilistic self-adaptive learning strategy integrated into the employed-bee phase can boost classification accuracy with a minimal number of genes.

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Research Article Sun, 28 Apr 2019 00:00:00 +0300
Open Domain Targeted Sentiment Classification Using Semi-Supervised Dynamic Generation of Feature Attributes https://lib.jucs.org/article/23705/ JUCS - Journal of Universal Computer Science 24(11): 1582-1603

DOI: 10.3217/jucs-024-11-1582

Authors: Shadi Abudalfa, Moataz Ahmed

Abstract: Microblogging services have been significantly increased nowadays and enabled people to share conveniently their sentiments (opinions) with regard to matters of concerns. Such sentiments have shown an impact on many fields such as economics and politics. Different sentiment analysis approaches have been proposed in the literature to predict automatically sentiments shared in micro-blogs (e.g., tweets). A class of such approaches predicts opinion towards specific target (entity); this class is referred to as target-dependent sentiment classification. Another class, called open domain targeted sentiment classification, extracts targets from the micro-blog and predicts sentiment towards them. In this research work, we propose a new semi-supervised learning technique for developing open domain targeted sentiment classification by using fewer amounts of labelled data. To the best of our knowledge, our model represents the first semi-supervised technique that is proposed for open domain targeted sentiment classification. Additionally, we propose a new supervised learning model for improving accuracy of open domain targeted sentiment classification. Moreover, we show for the first time that SVM HMM is able to improve accuracy of open domain targeted sentiment classification. Experimental results show that our proposed technique outperforms other prominent techniques available in the literature.

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Research Article Wed, 28 Nov 2018 00:00:00 +0200
Astmapp: A Platform for Asthma Self-Management https://lib.jucs.org/article/23695/ JUCS - Journal of Universal Computer Science 24(11): 1496-1514

DOI: 10.3217/jucs-024-11-1496

Authors: Harry Luna-Aveiga, José Medina-Moreira, Oscar Apolinario-Arzube, Mario Paredes-Valverde, Katty Lagos-Ortiz, Rafael Valencia-García

Abstract: Asthma is a chronic lung disease of the airways that makes breathing difficult. Worldwide, asthma is a leading disease among children and adolescents and a leading cause of hospitalizations among adolescents. Asthma self-management is a systematic procedure that allows educating, training, and informing patients to control their disease and avoid it when it is possible and reduce it when it is necessary. Nowadays, there is a need for technological tools for supporting different tasks within the process of asthma self-management, such as education, control, and monitoring, that help patients and their families improve their quality of life and reduce the direct and indirect costs. This work proposes Astmapp, a platform that relies on semantic and mobile technologies and recommender systems to increase the patients' knowledge about asthma regarding topics such as triggers, symptoms, activity restrictions, medications, among others, and to promote the asthma control by means of the monitoring of symptoms and parameters such as physical activity, heart rate, blood pressure, temperature, among others. Likewise, Astmapp recommends educational resources based on the preferences of patients and generates medical recommendations based on the symptoms and health status of the patient aiming to prevent asthma and reduce its exacerbation. Astmapp was evaluated in terms of its ability to recommend asthma educational resources relevant for the patients as well as to provide health recommendations. The evaluation results suggest that Astmapp has the potential to effectively support the asthma self-management process.

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Research Article Wed, 28 Nov 2018 00:00:00 +0200
Building an Educational Platform Using NLP: A Case Study in Teaching Finance https://lib.jucs.org/article/23607/ JUCS - Journal of Universal Computer Science 24(10): 1403-1423

DOI: 10.3217/jucs-024-10-1403

Authors: Soto Montalvo, Jesus Palomo, Carmen Orden

Abstract: Information overload is one of the main challenges in the current educational context, where the Internet has become a major source of information. According to the European Space for Higher Education, students must now be more autonomous and creative, with lecturers being required to provide guidance and supervision. Guiding students to search and read news related to subjects that are being studied in class has proven to be an effective technique in improving motivation, because students appreciate the relevance of the topics being studied in real world examples. However, one of the main drawbacks of this teaching practice is the amount of time that lecturers and students need for searching relevant and useful information on different subjects. The objective of our research is to demonstrate the usefulness of a complementary teaching tool in the traditional educational classroom. It is a new educational platform that combines Artificial Intelligence techniques with the expertise provided by lecturers. It automatically compiles information from different sources and presents only relevant breaking news classified into different subjects and topics. It has been tested on a Finance course, where being continually informed about the latest economic and financial news is an important part of the teaching process, specially for certain key financial concepts. The utility of the platform has been studied by conducting student surveys. The results confirm that using the platform had a positive impact on improving students' motivation and boost the learning processes. This research provides evidence about effectiveness of the new educational complement to traditional teaching methods in classrooms. Also, it demonstrates the improvement on the knowledge transfer within an environment of information overload.

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Research Article Sun, 28 Oct 2018 00:00:00 +0300
Unsupervised Feature Selection for Microarray Gene Expression Data Based on Discriminative Structure Learning https://lib.jucs.org/article/23295/ JUCS - Journal of Universal Computer Science 24(6): 725-741

DOI: 10.3217/jucs-024-06-0725

Authors: Xiucai Ye, Tetsuya Sakurai

Abstract: The analysis of microarray gene expression data to obtain useful information is a challenging problem in bioinformatics. Feature selection is an efficient computational technique in processing the analysis of high-dimensional microarray data. Due to the lack of label information in practice, unsupervised feature selection is considered to be more practically important and correspondingly more difficult. In this paper, we propose a novel unsupervised feature selection method, which utilizes local regression and discriminant analysis for structure learning on microarray gene expression data. By imposing row sparsity on the weight matrix through l2,1-norm regularization, the proposed method optimizes for selecting the discriminative genes which are more informative and better capture the interesting natural classes of samples. We develop an effective algorithm to solve the l2,1-norm-based optimization problem in our method and present the convergence analysis. Finally, we evaluate the proposed method on real microarray gene expression datasets. The experimental results demonstrate that the proposed method not only achieves good performance, but also outperforms other state-of-the-art unsupervised feature selection methods.

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Research Article Thu, 28 Jun 2018 00:00:00 +0300
Does the Users' Tendency to Seek Information Affect Recommender Systems' Performance? https://lib.jucs.org/article/22983/ JUCS - Journal of Universal Computer Science 23(2): 187-207

DOI: 10.3217/jucs-023-02-0187

Authors: Umberto Panniello, Lorenzo Ardito, Antonio Petruzzelli

Abstract: Much work has been done on developing recommender system (RS) algorithms, on comparing them using business metrics (such as customers' trust or perception of recommendations' novelty) and on exploring users' reactions to recommendations. It was demonstrated that different recommender systems perform differently on several performance metrics and that different users react differently to the same kind of recommendations. As a consequence, some scholars challenged to explore how users with different tendency to seek information during their purchasing process may react to different kind of recommendations. To the best of our knowledge, none of the prior works studied if users' tendency to seek information has an effect on recommender systems' performance. Different users may traditionally have different propensity to seek information and to receive suggestions and therefore they may react differently to the same recommendations. To this aim, we performed a live experiment with real customers coming from a European firm.

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Research Article Mon, 28 Aug 2017 00:00:00 +0300
On Predicting Election Results using Twitter and Linked Open Data: The Case of the UK 2010 Election https://lib.jucs.org/article/23060/ JUCS - Journal of Universal Computer Science 23(3): 280-303

DOI: 10.3217/jucs-023-03-0280

Authors: Evangelos Kalampokis, Areti Karamanou, Efthimios Tambouris, Konstantinos Tarabanis

Abstract: The analysis of Social Media data enables eliciting public behaviour and opinion. In this context, a number of studies have recently explored Social Media's capability to predict the outcome of real-world phenomena. The results of these studies are controversial with elections being the most disputable phenomenon. The objective of this paper is to present a case of predicting the results of the UK 2010 through Twitter. In particular, we study to what extend it is possible to use Twitter data to accurately predict the percentage of votes of the three most prominent political parties namely the Conservative Party, Liberal Democrats, and the Labour Party. The approach we follow capitalises on (a) a theoretical Social Media data analysis framework for predictions and (b) Linked Open Data to enrich Twitter data. We extensively discuss each step of the framework to emphasise on the details that could affect the prediction accuracy.We anticipate that this paper will contribute to the ongoing discussion of understanding to what extend and under which circumstances election results are predictable through Social Media.

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Research Article Tue, 28 Mar 2017 00:00:00 +0300
RecSim: A Model for Learning Objects Recommendation using Similarity of Sessions https://lib.jucs.org/article/23431/ JUCS - Journal of Universal Computer Science 22(8): 1175-1200

DOI: 10.3217/jucs-022-08-1175

Authors: Tiago Wiedmann, Jorge Luis Victória Barbosa, Sandro Rigo, Débora Nice Ferrari Barbosa

Abstract: A learning object (LO) is any entity or resource that can be used in computer-aided learning. This can take the form of text, multimedia content, presentations, programs or any other type of digital content, generally made available through web portals or distance learning systems. The LOs consulted by a student while accessing such portals are related to the interests of the student for the duration of the session. This article proposes a model for LOs recommendation using similarity of sessions, called RecSim. The model receives the sequence of LOs consulted during the current user session along with sessions whose sequences are similar to the LOs consulted in the current session. LOs found in similar sessions are then recommended to the user. A prototype was developed and applied into two controlled experiments. The results were encouraging and show potential for implementing RecSim in real-life situations.

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Research Article Mon, 1 Aug 2016 00:00:00 +0300
Sentiment Classification of Spanish Reviews: An Approach based on Feature Selection and Machine Learning Methods https://lib.jucs.org/article/23209/ JUCS - Journal of Universal Computer Science 22(5): 691-708

DOI: 10.3217/jucs-022-05-0691

Authors: Mario Paredes-Valverde, Jorge Limon-Romero, Diego Tlapa, Yolanda Baez-Lopez

Abstract: Sentiment analysis aims to extract users' opinions from review documents. Nowadays, there are two main approaches for sentiment analysis: the semantic orientation and the machine learning. Sentiment analysis approaches based on Machine Learning (ML) methods work over a set of features extracted from the users' opinions. However, the high dimensionality of the feature vector reduces the effectiveness of this approach. In this sense, we propose a sentiment classification method based on feature selection mechanisms and ML methods. The present method uses a hybrid feature extraction method based on POS pattern and dependency parsing. The features obtained are enriched semantically through common-sense knowledge bases. Then, a feature selection method is applied to eliminate the noisy and irrelevant features. Finally, a set of classifiers is trained in order to classify unknown data. To prove the effectiveness of our approach, we have conducted an evaluation in the movies and technological products domains. Also, our proposal was compared with well-known methods and algorithms used on the sentiment classification field. Our proposal obtained encouraging results based on the F-measure metric, ranging from 0.786 to 0.898 for the aforementioned domains.

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Research Article Sun, 1 May 2016 00:00:00 +0300
Automatic Generation of Interactive Cooking Video with Semantic Annotation https://lib.jucs.org/article/23271/ JUCS - Journal of Universal Computer Science 22(6): 742-759

DOI: 10.3217/jucs-022-06-0742

Authors: Kyeong-Jin Oh, Myung-Duk Hong, Ui-Nyoung Yoon, Geun-Sik Jo

Abstract: Videos are one of the most frequently used forms of multimedia resources. People want to interact with videos to find a specific part or to obtain relevant information. To support user interactions, current videos should be transformed to interactive videos. This paper proposes an interactive cooking video system to generate automatically interactive cooking videos. To do this, the proposed system performs semantic video annotation on cooking videos. Semantic video annotation process includes three parts: synchronization between recipes and corresponding cooking videos based on a caption-recipe alignment algorithm, information extraction on food recipes using lexico-syntactic patterns, and semantic entity interconnection between recognized entities and semantic web entities. Cooking video annotation ontology is modeled to handle annotation data. To evaluate the proposed system, comparative experiments are performed on the caption-recipe alignment algorithm. The accuracy of information extraction and semantic entity interconnection is also measured. Experimental results show that the proposed system is superior to compared algorithms in alignment perspectives. Information extraction and semantic interconnection method also achieve high accuracy over 95%, respectively. Consequently, the proposed system generates interactive cooking videos in high accuracy and support user interactions by providing a user interface which allows users to easily find specific scenes and obtain detailed information on objects users have interested in.

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Research Article Sun, 1 May 2016 00:00:00 +0300
Metadata for Recommending Primary and Secondary Level Learning Resources https://lib.jucs.org/article/22968/ JUCS - Journal of Universal Computer Science 22(2): 197-227

DOI: 10.3217/jucs-022-02-0197

Authors: Jorge Bozo, Rosa Alarcon, Monserrat Peralta, Tomas Mery, Verónica Cabezas

Abstract: Recommender systems have been used in education to assist users in the discovery of learning resources. Unlike product-oriented recommender systems, the goals and behavior of users in education are influenced by their context; such influence may be stronger in formal scenarios such as primary and secondary education since context is highly regulated. Intuitively, we could assume that a biology teacher may be more interested in biology-related content rather than content from other fields. In this paper we explore such assumption by analyzing the impact of educational metadata that is associated to resources and teachers. We apply hierarchical clustering to determine clusters of interest and using a teacher profile, we classify new teachers and new items in order to predict their preferences. In order to validate our approach, we used a dataset derived from a repository of learning resources widely used by teachers in primary and secondary school in Chile in the role of old users, we also performed an experiment with teachers in training in the role of new users. Our results confirm the diverse impact of metadata on the formation of such clusters and on recommendation.

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Research Article Mon, 1 Feb 2016 00:00:00 +0200
Mining Models for Automated Quality Assessment of Learning Objects https://lib.jucs.org/article/22896/ JUCS - Journal of Universal Computer Science 22(1): 94-113

DOI: 10.3217/jucs-022-01-0094

Authors: Cristian Cechinel, Sandro da Silva Camargo, Miguel-Ángel Sicilia, Salvador Sánchez-Alonso

Abstract: The present paper presents the results of an alternative approach for automatically evaluating quality inside learning object repositories that considers lower-level measures of the resources as possible indicators of quality. It is known that current repositories face a difficult situation, as their amount of resources tends to increase more rapidly than the number of evaluations provided by the community of users and experts. Alternative approaches for automatically assessing quality can relieve human-work and provide temporary quality information before more time and consuming evaluation is performed. We propose a methodology to automatically generate quality information about learning resources inside repositories with Artificial Neural Networks models. For that, we considered 34 low-level measures as possible indicators of quality and we used available evaluative metadata inside two world recognized repositories (MERLOT and Connexions) as baseline information for the establishment of classes of quality. The preliminary findings point out the feasibility of such an approach and can be used as a starting point in the process of automatically generating internal quality information about learning objects inside repositories.

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Research Article Fri, 1 Jan 2016 00:00:00 +0200
Validity and Reliability of Tablet Supported Education Attitude and Usability Scale https://lib.jucs.org/article/22894/ JUCS - Journal of Universal Computer Science 22(1): 82-93

DOI: 10.3217/jucs-022-01-0082

Authors: Hüseyin Uzunboylu, Vasfi Tugun

Abstract: The use of mobile technologies in education has begun due to the increase in the use of mobile technologies. The attitudes of students, teachers and parents towards mobile learning and their opinions about the usability of mobile learning should be received in order to achieve mobile supported education in the schools. The aim of this study is to develop a scale about the attitudes of students in a private college towards usability of mobile supported education. This study was conducted with 150 students. Firstly, the students wrote composition about the issue, made a literature search and the statements were prepared and presented to expert opinion. The last version of the data collection tool was administered to 150 students and necessary analysis was made. Based on the obtained results, the scale was decided to have two dimensions. Besides, the results showed that the reliability and validity of the scale is high. Recommendations for future research were also provided.

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Research Article Fri, 1 Jan 2016 00:00:00 +0200
Towards a Semantic Repository for Learning Objects: Design and Evaluation of Core Services https://lib.jucs.org/article/22890/ JUCS - Journal of Universal Computer Science 22(1): 16-36

DOI: 10.3217/jucs-022-01-0016

Authors: João Gluz, Ederson Silveira, Luiz Rodrigo Jardim Da Silva, Jorge Luis Victória Barbosa

Abstract: Repositories form a central piece of the learning objects technology, providing storage spaces where the objects can be catalogued, located, and retrieved. However, repositories usually support only syntactical and morphological aspects of learning objects metadata for cataloguing and searching purposes. This article proposes two integrated systems, which provide the core services of a semantic repository of learning objects. MSSearch system uses ontology alignment techniques to create a semantic search engine and a semantic database for learning objects metadata. MSSearch supports the integration of multiple educational ontologies through a combination of ontology aligning and mapping mechanisms. In turn, Linnaeus system offers intelligent support for the creation and editing of learning object metadata. This tool employs the technologies of intelligent agents and educational ontologies to provide an intelligent semi-automatic metadata filling service. This article presents the main architectural components, ontological models and interface facilities of these systems. The text finishes with the presentation of the experiments conducted to validate Linnaeus and MSSearch.

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Research Article Fri, 1 Jan 2016 00:00:00 +0200
Determination of Secondary School Students' Attitudes towards Tablet PC Supported Education https://lib.jucs.org/article/22889/ JUCS - Journal of Universal Computer Science 22(1): 4-15

DOI: 10.3217/jucs-022-01-0004

Authors: Fezile Ozdamli, Tahir Tavukcu

Abstract: The aim of this study was to determine the attitudes of students towards tablet supported education and its effects on its usability. The study was designed as a one-group semi-experimental model using pre-test and post-test. The study group consisted of 319 students in 6th and 9th grade studying at a private college, where 160 of the students were females and 159 of them were males. Frequency, Paired t-test and Independent t-test were applied to analyze the data. The pre-test and post-test results indicated that there were significant positive differences in students' attitudes toward tablet supported education, students' attitudes towards usability of the tablets in education and also in their general attitudes. There was a significant difference between the mean scores of the female and male students' attitudes towards tablet-supported education. However, there was no significant difference between the students' attitudes towards the usability of the tablet and in their overall attitudes.

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Research Article Fri, 1 Jan 2016 00:00:00 +0200
Seeking Open Educational Resources to Compose Massive Open Online Courses in Engineering Education An Approach based on Linked Open Data https://lib.jucs.org/article/23201/ JUCS - Journal of Universal Computer Science 21(5): 679-711

DOI: 10.3217/jucs-021-05-0679

Authors: Nelson Piedra, Janneth Chicaiza, Jorge López, Edmundo Tovar

Abstract: The OER movement has tended to define "openness" in terms of access to use and reuse educational materials, and to address the geographical and financial barriers among students, teachers and self-learners with open access to high quality digital educational resources. MOOCs are the continuation of this trend of openness, innovation, and use of technology to provide learning opportunities for large numbers of learners. In the last years, the amount of Open Educational Resources on the Web has increased dramatically, especially thanks to initiatives like OpenCourseWare and other Open Educational Resources movements. The potential of this vast amount of resources is enormous. In this paper an architecture based on Semantic Web technologies and the Linked Data guidelines to support the inclusion of open materials in massive online courses is presented. Linked Data is considered as one of the most effective alternatives for creating global shared information spaces, it has become an interesting approach for discovering and enriching open educational resources data, as well as achieving semantic interoperability and re-use between multiple Open Educational Resources repositories. The notion of Linked Data refers to a set of best practices for publishing, sharing and interconnecting data in RDF format. Educational repositories managers are, in fact, realizing the potential of using Linked Data for describing, discovering, linking and publishing educational data on the Web. This work shows a data architecture based on semantic web technologies that support the discovery and inclusion of open educational materials in massive online courses in engineering education. The authors focus on a type of openness: open of contents as regards re-use and re-mix, i.e. freedom to reuse the material, to combine it with other materials, to adapt and to share it further under an open license.

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Research Article Fri, 1 May 2015 00:00:00 +0300
A Generic Architecture for Emotion-based Recommender Systems in Cloud Learning Environments https://lib.jucs.org/article/23855/ JUCS - Journal of Universal Computer Science 19(14): 2075-2092

DOI: 10.3217/jucs-019-14-2075

Authors: Derick Leony, Hugo A. Parada Gélvez, Pedro Muñoz-Merino, Abelardo Pardo, Carlos Delgado-Kloos

Abstract: Cloud technology has provided a set of tools to learners and tutors to create a virtual personal learning environment. As these tools only support basic tasks, users of learning environments are looking for specialized tools to exploit the uncountable learning elements available on the internet. Thus, one of the most common functionalities in cloud-based learning environments is the recommendation of learning elements and several approaches have been proposed to deploy recommender systems into an educational environment. Currently, there is an increasing interest in including affective information into the process to generate the recommendations for the learner; and services offering this functionality on cloud environments are scarce. Hence in this paper, we propose a generic cloud-based architecture for a system that recommends learning elements according to the affective state of the learner. Furthermore, we provide the description of some use cases along with the details of the implementation of one of them. We also provide a discussion on the advantages and disadvantages of the proposal.

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Research Article Thu, 1 Aug 2013 00:00:00 +0300
Teaching Innova Project: the Incorporation of Adaptable Outcomes in Order to Grade Training Adaptability https://lib.jucs.org/article/23627/ JUCS - Journal of Universal Computer Science 19(11): 1500-1521

DOI: 10.3217/jucs-019-11-1500

Authors: Ángel Fidalgo, María Sein-Echaluce, Dolores Lerís, Oscar Castañeda

Abstract: The education project presented in this paper endeavors to study the feasibility of incorporating adaptive systems into LMS systems, by using them both in training & learning process and at work. This case study is aimed at employability and job post improvement. For this purpose, we have created a process that is flexible both to the student pattern (and to the job pattern. The developed process is adaptable both to the student (via the incorporation of an adaptable system with an LMS system) and to the job model (via an adaptable system to the knowledge management). The evaluation was qualitative and measured the process (feasibility to apply adaptive systems) and the efficiency of the method (applicability and employability). The functionality of the specific developed tools allowed us to grade the degree of adaptability in the training process, to dynamically vary the training plan from the student's actions and to identify the resources that best met the job needs.

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Research Article Sat, 1 Jun 2013 00:00:00 +0300
Learning to Classify Neutral Examples from Positive and Negative Opinions https://lib.jucs.org/article/23918/ JUCS - Journal of Universal Computer Science 18(16): 2319-2333

DOI: 10.3217/jucs-018-16-2319

Authors: María-Teresa Martín-Valdivia, Arturo Montejo-Ráez, Alfonso Ureña-López, Mohammed Saleh

Abstract: Sentiment analysis is a challenging research area due to the rapid increase of subjective texts populating the web. There are several studies which focus on classifying opinions into positive or negative. Corpora are usually labeled with a star-rating scale. However, most of the studies neglect to consider neutral examples. In this paper we study the effect of using neutral sample reviews found in an opinion corpus in order to improve a sentiment polarity classification system. We have performed different experiments using several machine learning algorithms in order to demonstrate the advantage of taking the neutral examples into account. In addition we propose a model to divide neutral samples into positive and negative ones, in order to incorporate this information into the construction of the final opinion polarity classification system. Moreover, we have generated a corpus from Amazon in order to prove the convenience of the system. The results obtained are very promising and encourage us to continue researching along this line and consider neutral examples as relevant information in opinion mining tasks.

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Research Article Tue, 28 Aug 2012 00:00:00 +0300
OER Development and Promotion. Outcomes of an International Research Projecton the OpenCourseWare Model https://lib.jucs.org/article/22857/ JUCS - Journal of Universal Computer Science 18(1): 123-141

DOI: 10.3217/jucs-018-01-0123

Authors: Edmundo Tovar, Nelson Piedra, Janneth Chicaiza, Jorge Lopez, Oscar Martinez-Bonastre

Abstract: In this paper, we describe the successful results of an international research project focused on the use of Web technology in the educational context. The article explains how this international project, funded by public organizations and developed over the last two academic years, focuses on the area of open educational resources (OER) and particularly the educational content of the OpenCourseWare (OCW) model. This initiative has been developed by a research group composed of researchers from three countries. The project was enabled by the Universidad Politécnica de Madrid OCW Office’s leadership of the Consortium of Latin American Universities and the distance education know-how of the Universidad Técnica Particular de Loja (UTPL, Ecuador). We give a full account of the project, methodology, main outcomes and validation. The project results have further consolidated the group, and increased the maturity of group members and networking with other groups in the area. The group is now participating in other research projects that continue the lines developed here.

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Research Article Sun, 1 Jan 2012 00:00:00 +0200
Usage-based Object Similarity https://lib.jucs.org/article/29768/ JUCS - Journal of Universal Computer Science 16(16): 2272-2290

DOI: 10.3217/jucs-016-16-2272

Authors: Katja Niemann, Maren Scheffel, Martin Friedrich, Uwe Kirschenmann, Hans-Christian Schmitz, Martin Wolpers

Abstract: Recommender systems are widely used online to support users in finding relevant information. They can be based on different techniques such as content-based and collaborative filtering. In this paper, we introduce a new way of similarity calculation for item-based collaborative filtering. Thereby we focus on the usage of an object and not on the object's users as we claim the hypothesis that similarity of usage indicates content similarity. To prove this hypothesis we use learning objects accessible through the MACE portal where students can query several architectural repositories. For these objects, we generate object profiles based on their usage monitored within MACE. We further propose several recommendation techniques to apply this usagebased similarity calculation in real systems.

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Research Article Sat, 28 Aug 2010 00:00:00 +0300
The 3A Personalized, Contextual and Relation-based Recommender System https://lib.jucs.org/article/29759/ JUCS - Journal of Universal Computer Science 16(16): 2179-2195

DOI: 10.3217/jucs-016-16-2179

Authors: Sandy Helou, Christophe Salzmann, Denis Gillet

Abstract: This paper discusses the 3A recommender system that targets CSCL (computer-supported collaborative learning) and CSCW (computer-supported collaborative work) environments. The proposed system models user interactions in a heterogeneous graph. Then, it applies a personalized, contextual, and multi-relational ranking algorithm to simultaneously rank actors, activity spaces, and assets. The results of an empirical evaluation carried out on an Epinions dataset indicate that the proposed recommendation approach exploiting the trust and authorship networks performs better than user-based collaborative filtering in terms of recall.

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Research Article Sat, 28 Aug 2010 00:00:00 +0300