Adaptive e learning systems are expensive and their development is consuming both in the sense of time and human resources. Therefore, it is not realistic to expect that such systems can be developed in BiH environment in the very near future. On the other hand, even in BiH institutions involved in e learning, there exist elements of adaptive systems in the form of e content and independent applications that support e learning, such as forums, testing modules, e learning content management modules, and applications for testing the learning styles. In this paper, we present a model that uses the existing solutions combined with one additional module which is collecting data from the individual applications in order to use them for achieving certain level of adaptivity. The suggested solution is possible to implement in the BiH environment is as step in a fully adaptive system development.

Online knowledge-sharing communities are usually small. In this paper, we present the challenge of analyzing a large online community in order to determine if it is feasible for knowledge-sharing. We deploy social network techniques to analyze patterns of interactions critical to information- and knowledge-sharing among learners in a virtual community. Based on this, we determine the characteristics of the network and the roles of the actors. The research was carried out at the Faculty of Information Technologies in Mostar (FIT), after the end of the 2007/8 academic year, during which 293 freshmen were enrolled. We collected data from the FIT Community Server (FITCS), for the period from 1.10.2007 until 30.9.2008, and modeled them into three networks (N, N1 and N2). Network N (overall communication) has 273 vertices, N1 has 143 (fall semester course in Programming), and N2 has 99 (summer semester course in Programming). With regard to the number of enrolled freshmen, we estimate that 85 to 90% communicated via FITCS. The characteristics of the analyzed networks are as follows: density varied from 0.17 to 0.25, average distance from 1.80 to 1.93, cohesion from 0.53 to 0.63, betweenness varied from 7.83 for N to 56.23 for N1, and closeness varied from 52.66 to 56.78. The results for the characteristics of selected actors are degree (ranging from 10 to 119), betweenness (ranging from 0 to 88.17), and closeness (ranging from 16.93 to 60.62). The results of this research show that some educators do not have a proper roles in the online knowledgesharing community, but that can be due to the fact that the actors are freshmen. On the other hand, some of the most successful students, with extrovert personalities, were the stars of the three analyzed networks. Therefore, we can conclude that the analyzed part of FITCS is a knowledge-sharing community. Recommendations are that we should motivate educators to support online knowledge-sharing, that we should educate educators about their proper role in such a community, and that we should motivate successful students with introvert personalities to be more active in knowledge-sharing.

This paper presents the effects of curriculum changes for course on Mathematics at Faculty of Information Technologies, University “Dzemal Bijedic”, Mostar, BIH. Changes were introduced during Fall Semester 2006/7. They are aiming to improve knowledge deliverance and examination process. Effect of changes was measured by comparing actual results with the results of students from previous generations. The results of the study show significant positive effect of the change in examinations (effect size 2.1789), and of overall curriculum changes (effect size 1.1771) justifying the proposed changes.

In this paper we present an analysis of a cluster based inference in a particular computer network. The faculty forum on a real community server, where students and stuff share their knowledge and experiences, is used for this purpose. In order to better understand the structure of the network, we represent it as a graph, where vertices are represented by the members of the forum and the edges act as the links between the forum posts. As in many similar systems, this forum is organized in threads that are divided into sections (subjects), and sections are divided into groups (academic years). It is shown that the resulting network exhibits a scale-free distribution with large clustering coefficients following the small-world properties. As the clusters hold some important information about the nature of the network, we developed a special software agent that explores the background SQL database and automatically acquires the relevant information. Based on this data, detailed information including the graphs degree distribution, clustering coefficient, Laplacian, and normalized Laplacian eigenvectors and average distance are calculated. The resulting analysis gives us a better understanding of the nature of this particular network, which can be valuable information for the administrators.