What Video Games Have to Teach Us about Learning and Literacy

International Journal of Computer and Applications

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

/pdf/educational-data-mining-a-review-of-the-state-of-the-art-51aglgywl8.pdf

Educational Data Mining: A Review of the State of the Art

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The Uses Of Argument

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.

DKN: Deep Knowledge-Aware Network for News Recommendation

Community question-answering (CQA) systems, such as Yahooe Answers or Stack Overflow, belong to a prominent group of successful and popular Web 2.0 applications, which are used every day by millions of users to find an answer on complex, subjective, or context-dependent questions. In order to obtain answers effectively, CQA systems should optimally harness collective intelligence of the whole online community, which will be impossible without appropriate collaboration support provided by information technologies. Therefore, CQA became an interesting and promising subject of research in computer science and now we can gather the results of 10 years of research. Nevertheless, in spite of the increasing number of publications emerging each year, so far the research on CQA systems has missed a comprehensive state-of-the-art survey. We attempt to fill this gap by a review of 265 articles published between 2005 and 2014, which were selected from major conferences and journals. According to this evaluation, at first we propose a framework that defines descriptive attributes of CQA approaches. Second, we introduce a classification of all approaches with respect to problems they are aimed to solve. The classification is consequently employed in a review of a significant number of representative approaches, which are described by means of attributes from the descriptive framework. As a part of the survey, we also depict the current trends as well as highlight the areas that require further attention from the research community.

A Comprehensive Survey and Classification of Approaches for Community Question Answering

Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single user as well as group recommendation. Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users' satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items.

Personalized hybrid recommendation for group of users

The information overloading is one of the serious problems nowadays. We can see it in various domains including business. Especially news represent area where information overload currently prevents effective information gathering on daily basis. This is more significant in connection to the web and news web-based portals, where the quality of the news portal is commonly measured mainly by the amount of news added to the site. Then the most renowned news portals add hundreds of new articles daily. The classical solution usually used to solve the information overload is a recommendation, especially personalized recommendation. In this paper we present an approach for fast content-based news recommendation based on cosine-similarity search and effective representation of the news. We experimented with proposed method in an environment of largest electronic Slovakia newspaper and present results of the experiments.

/pdf/content-based-news-recommendation-23m2opkm8s.pdf

Content-Based News Recommendation

Sofsem 2008: Theory and Practice of Computer Science

The aim of Web service composition is to arrange multiple services into workflows supplying complex user needs Due to the huge amount of Web services and the need to supply dynamically varying user goals, it is necessary to perform the composition automatically The objective of this article is to overview the issues of automatic dynamic Web service composition We discuss the issues related to the semantics of services, which is important for automatic Web ser- vice composition We propose a problem formalization contributing to the formal definition of the pre-/post-conditions, with possible value restrictions, and their re- lation to the semantics of services We also provide an overview of several existing approaches dealing with the problem of Web service composition and discuss the current achievements in the field and depict some open research areas

/pdf/automatic-dynamic-web-service-composition-a-survey-and-46u07vajl0.pdf

Mária Bieliková

Papers

A Comprehensive Survey and Classification of Approaches for Community Question Answering

Personalized hybrid recommendation for group of users

Content-Based News Recommendation

Sofsem 2008: Theory and Practice of Computer Science

Automatic Dynamic Web Service Composition: A Survey and Problem Formalization