Towards a Modular Recommender System for Research Papers written in Albanian

In the recent years there has been an increase in scientific papers publications in Albania and its neighboring countries that have large communities of Albanian speaking researchers. Many of these papers are written in Albanian. It is a very time consuming task to find papers related to the researchers' work, because there is no concrete system that facilitates this process. In this paper we present the design of a modular intelligent search system for articles written in Albanian. The main part of it is the recommender module that facilitates searching by providing relevant articles to the users (in comparison with a given one). We used a cosine similarity based heuristics that differentiates the importance of term frequencies based on their location in the article. We did not notice big differences on the recommendation results when using different combinations of the importance factors of the keywords, title, abstract and body. We got similar results when using only the title and abstract in comparison with the other combinations. Because we got fairly good results in this initial approach, we believe that similar recommender systems for documents written in Albanian can be build also in contexts not related to scientific publishing.Comment: 8 page

A Multi-criteria Decision Support System for Ph.D. Supervisor Selection: A Hybrid Approach

Selection of a suitable Ph.D. supervisor is a very important step in a student’s career. This paper presents a multi-criteria decision support system to assist students in making this choice. The system employs a hybrid method that first utilizes a fuzzy analytic hierarchy process to extract the relative importance of the identified criteria and sub-criteria to consider when selecting a supervisor. Then, it applies an information retrieval-based similarity algorithm (TF/IDF or Okapi BM25) to retrieve relevant candidate supervisor profiles based on the student’s research interest. The selected profiles are then re-ranked based on other relevant factors chosen by the user, such as publication record, research grant record, and collaboration record. The ranking method evaluates the potential supervisors objectively based on various metrics that are defined in terms of detailed domain-specific knowledge, making part of the decision making automatic. In contrast with other existing works, this system does not require the professor’s involvement and no subjective measures are employed

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Information support in the area of software simulators in medical education

(česky) Předmětem této bakalářské práce je analýza vývoje a užití lékařských simulátorů z hlediska informační podpory, základní popis a úvod do problematiky samotných lékařských výukových simulátorů. V úvodu jsou popsány vlastní simulátory od počátku jejich historického vývoje až po současné využití. Následně se práce zabývá figurínami pro pacientské simulátory, jejichž nezbytnou součástí nutnou pro správné fungování jsou matematické modely na pozadí simulátorů. V kapitole o repositářích je popsána problematika dokumentace modelů a archivace článků popisujících modely. Jsou popsány referenční manažery pro správu literatury a základní srovnání jejich funkcí. Dále se práce zaměřuje na jeden vybraný referenční manažer - Docear, který je podrobněji popsán a použit v praktické části bakalářské práce. Ta se zabývá organizací vědecké literatury biomedicínských simulátorů v Oddělení biokybernetiky a počítačové podpory výuky na 1. lékařské fakultě Univerzity Karlovy. Klíčová slova (česky) - lékařské simulátory, informační podpora, výuka, informační základna, repozitář, referenční manažer, myšlenkové mapy.(english) The subject of this thesis is the analysis of the development and use of medical simulators in terms of information support, basic description and introduction to the issue of medical training simulators themselves. The introduction describes the simulators since the beginning of the historical development to the current use. Subsequently, the work deals with manikin for patient simulators, the essential components, required for proper functioning, which are mathematical models on the background of the simulators. The chapter on repositories, describes the problems of model documentation and archiving of articles which describes the models. There are described reference managers for managing the literature and made a basic comparison of their features. The thesis focuses on a selected reference manager - Docear, which is described and used in the practical part. It deals with the organization of the scientific literature of Biomedical simulators in the Department of Biocybernetics and Computer Aided Teaching at the 1st Medical Faculty of Charles University. Key words (english) - medical simulators, information support, education, information base, repository, reference manager, mind mapsÚstav informačních studií a knihovnictvíInstitute of Information Studies and LibrarianshipFilozofická fakultaFaculty of Art