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In the current time of globalization, collaboration among people in virtual environments is becoming an important precondition of success. This trend is reflected also in the educational domain where students collaborate in various short-term groups created repetitively but changing in each round (e.g. in MOOCs). Students in these kind of dynamic groups quite often encounter various difficulties, which are obvious mainly when students' characteristics do not complement each other. In spite of various group formation methods aimed to solve the group compatibility problem, most of the existing approaches do not consider dynamic groups. We describe (i) a proposal of a novel group formation method based on Group Technology approach, which considers feedback on students' collaboration to improve group formations; (ii) an application of the method as a part of a collaborative platform PopCorm, which provides students in the created groups with a set of real-time collaboration tools; (iii) a long-term experiment in which the groups created by our method achieved significantly better results in comparison with the reference approaches. Our results indicate that considering feedback from students' collaboration can improve the group formation process as the groups created by our method achieved higher collaboration quality with next iterations.

In this paper we present a method for adaptive selection of test questions according to the individual needs of students within a web-based educational system. It functions as a combination of three particular methods. The first method is based on the course structure and focuses on the selection of the most appropriate topic for learning. The second uses Item Response Theory to select the k-best questions with adequate difficulty for a particular learner. The last is based on the usage history and prioritizes questions according to specific strategies, e.g. to filter out the questions that were recently asked. We describe how these methods evaluate user answers to gather information concerning their characteristics for a more precise selection of further questions. We describe an evaluation of the impact of a proposed method through two different types of experiments in the domain of learning programming, which both showed that our method for adaptive test question selection increases the overall learning outcome, especially for lower than average performing students. (Contains 2 tables and 12 figures.)

Most of the research in the recommender systems domain is focused on the optimization of metrics based on historical data such as mean average precision or recall. However, there is a gap between the research and industry since the leading key performance indicators for businesses are revenue and profit. In this article, we explore the impact of manipulating the profit awareness of a recommender system. An average e-commerce business does not usually use a complicated recommender algorithm. We propose an adjustment of a predicted ranking for score-based recommender systems and explore the effect of the profit and customers' price preferences on two industry datasets from the fashion domain. In the experiments, we show the ability to improve both the precision and the generated recommendations' profit. Such an outcome represents a win–win situation when e-commerce increases the profit and customers get more valuable recommendations. [ABSTRACT FROM AUTHOR]

The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterising the fake news and devising automated methods for detecting them. The detection methods had so far only limited success, mostly due to the complexity of the news content and context and lack of properly annotated datasets. One possible way to boost the efficiency of automated misinformation detection methods is to imitate the detection work of humans. It is also important to understand the news consumption behaviour of online users. In this paper, we present an eye-tracking study, in which we let 44 lay participants to casually read through a social media feed containing posts with news articles, some of which were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We also describe a follow-up qualitative study with a similar scenario but this time with seven expert fake news annotators. We present the description of both studies, characteristics of the resulting dataset (which we hereby publish) and several findings. [ABSTRACT FROM AUTHOR]

The popularity of e-commerce is increasing day-by-day. In order to provide a seamless experience and tailored offer for the customers, the knowledge of their preferences and behavior is required. The demography of customers is one of the important information used for, e.g., segmentation. To provide optimal service, machine learning is often used for various tasks. However, the amount of data generated by customers and also large and changing product catalogs result in poorly performing models. In this paper we aim at introducing behavior-based abstraction, which includes item and event abstractions also. Our method reduces the number of unique items in the e-commerce catalog and in the next step encodes the user behavior. We performed extensive evaluation over the real-world dataset. The results suggest the usefulness of proposed abstraction and also resulted in the improvement of the demography prediction performance both from model complexity and performance point of view. [ABSTRACT FROM AUTHOR]

Byline: Maria Bielikova This paper presents an approach to learning programming by novice programmers through a web-based adaptive educational system called ALEA (Adaptive LEArning). ALEA supports learning programming by generating sequences of program examples that serve as exercises for a learner. The sequence is adapted to the needs of individual learners. At present, ALEA contains more than a hundred Lisp and Prolog program examples. The program examples are presented as specific instances of program schemata, which facilitate understanding of basic programming principles. We also discuss our experience in using web-based support for teaching programming.

Nowadays, personalized recommendations are widely used and popular. There are a lot of systems in various fields, which use recommendations for different purposes. One of the basic problems is the distrust of users of recommended systems. Users often consider the recommendations as an intrusion of their privacy. Therefore, it is important to make recommendations transparent and understandable to users. To address these problems, we propose a novel hybrid method of personalized explanation of recommendations. Our method is independent of recommendation technique and combines basic explanation styles to provide the appropriate type of personalized explanation to each user. We conducted several online experiments in the news domain. Obtained results clearly show that the proposed personalized hybrid explanation approach improves the users' attitude towards the recommender, moreover, we have observed the increase of recommendation precision. [ABSTRACT FROM AUTHOR]

The costs of eye-tracking technologies steadily decrease. This allows research institutions to obtain multiple eye-tracking devices. Already, several multiple eye-tracker laboratories have been established. Researchers begin to recognize the subfield of group eye-tracking. In comparison to the single-participant eye-tracking, group eye-tracking brings new technical and methodological challenges. Solutions to these challenges are far from being established within the research community. In this paper, we present the Group Studies system, which manages the infrastructure of the group eye-tracking laboratory at the User Experience and Interaction Research Center (UXI) at the Slovak University of Technology in Bratislava. We discuss the functional and architectural characteristics of the system. Furthermore, we illustrate our infrastructure with one of our past studies. With this paper, we also publish the source code and the documentation of our system to be re-used. [ABSTRACT FROM AUTHOR]

In the constantly growing blogosphere with no restrictions on form or topic, a number of writing styles and genres have emerged. Recognition and classification of these styles has become significant for information processing with an aim to improve blog search or sentiment mining. One of the main issues in this field is detection of informative and affective articles. However, such differentiation does not suffice today. In this paper we extend the differentiation and suggest a fine-grained set of subcategories for affective articles. We propose and evaluate a classification method employing novel lexical, morphological, lightweight syntactic and structural features of written text. The results show that our method outperforms the existing approaches. [ABSTRACT FROM AUTHOR]

Community question-answering (CQA) systems, such as Yahoo! 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. [ABSTRACT FROM AUTHOR]