<p class="R-AbstractKeywords"><span lang="EN-GB">The purpose of our study was to identify reasons for high dropout of students enrolled in the first year of the computer science study program to make it possible to determine students, who are potentially in risk. Several factors that could affect attrition, as it was originally assumed, were studied: high school grades (admission score), compensative course in high school mathematics, intermediate grades for core courses, prior knowledge of programming. However, the results of our study indicate that none of the studied factors is determinant to identify those students, who are going to abandon their studies, with great precision. The majority of the studied students drop out in the 1st semester of the 1st year, and the dropout consists mostly of those, who do not really begin studies. Therefore, one of the main conclusions is such that the planned activities of informing about the contents of the program should be carried out, and the perspective students should be offered a possibility to evaluate their potential to study computer science before choosing a study program. </span></p>
Abstract. This paper presents an OLAP reporting tool and an approach for determining and processing user OLAP preferences, which are useful for generating recommendations on potentially interesting reports. We discuss the metadata layers of the reporting tool including our proposed OLAP preferences metamodel, which supports various scenarios of formulating preferences of two different types: schema-specific and report-specific. The process of semantic metadata usage at the stage of formulating user preferences is also considered. The methods for processing schema-specific and report-specific OLAP preferences are outlined.
Abstract. Data warehouses tend to evolve, because of changes in data sources and business requirements of users. All these kinds of changes must be properly handled, therefore, data warehouse development is never-ending process. In this paper we propose the evolution-oriented user-centric data warehouse design, which on the one hand allows to manage data warehouse evolution automatically or semi-automatically, and on the other hand it provides users with the understandable, easy and transparent data analysis possibilities. The proposed approach supports versions of data warehouse schemata and data semantics.
The measuring of research results can be used in different ways e.g. for assignment of research grants and afterwards for evaluation of project’s results. It can be used also for recruiting or promoting research institutions’ staff. Because of a wide usage of such measurement, the selection of appropriate measures is important. At the same time there does not exist a common view which metrics should be used in this field, moreover many existing metrics that are widely used are often misleading due to different reasons, e.g. computed from incomplete or faulty data, the metric’s computation formula may be invalid or the computation results can be interpreted wrongly. To produce a good framework for research evaluation, the mentioned problems must be solved in the best possible way by integrating data from different sources to get comprehensive view of academic institutions’ research activities and to solve data quality problems. We will present a data integration system that integrates university information system with library information system and with data that are gathered through API from Scopus and Web of Science databases. Data integration problems and data quality problems that we have faced are described and possible solutions are presented. Metrics that are defined and computed over these integrated data and their analysis possibilities are also discussed.
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