Abstract-Intelligent Tutoring Systems (ITSs) are inherently adaptive e-learning systems usually created for teaching well-defined domains (e.g., mathematics). Their objective is to guide the student towards a predefined goal such as completing a lesson, task, or mastering a skill. Defining goals and guiding students is more complex in ill-defined domains where the expert defines the model of the knowledge domain or the students have freedom to follow their own path through it. In this paper we present an overview of our system's architecture that integrates the ITS with data mining tools and performs a number of educational data mining processes to increase the adaptivity and, consequently, the efficiency of the ITS.Index Terms-e-learning, intelligent tutoring systems, educational data mining, adaptive e-learning.
Many companies, organizations, institutions and governments have recognized the operating advantages they can achieve by using new business intelligence (BI) technologies. However, to achieve those advantages, they need to choose a BI solution that best fits their needs. In this paper, we present an overview of twenty business intelligence solutions present on the market and describe their most essential and upcoming features. The features are presented in two groups-basic and advanced (upcoming) features. We present two tools that can facilitate the selection process of the organization's BI solution-the comparison table and the features pyramid. We detect four classes of maturity of BI tools reflected through the implementation of advanced features. Finally, we discuss obtained insights and provide some guidelines for choosing the right BI solution together with detected development trends.
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