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Modern education is hard to imagine without the use of e-learning tools, but still the transition from traditional "paper-and-pencil" examining to electronic exams is to some extent cautious. Although course administration and exam evaluation are much easier with learning management systems, there are concerns among teaching staff, that electronic tests simplify examination process compared to paper tests and classic examining. At the University of Belgrade, School of Electrical Engineering, e-learning tools are used at several exams, mostly as a secondary tool to aid with laboratory exercises. In this paper, we show our experience with four courses from the computer engineering study program that are to various extent moved to Moodle LMS, and electronic examining. We mostly concentrate on certain aspects of transition from paper tests to electronic exams. We present 12 different transformations needed to conform to electronic examining and automated evaluation, and discuss benefits and drawbacks of such a transition. ß 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:775-786, 2016; View this article online at wileyonlinelibrary.com/journal/cae;

Artificial intelligence (AI) comprises a large spectrum of groups of algorithms: heuristic algorithms for search and planning, formal methods for representation of knowledge and reasoning, algorithms for machine learning and many more. Since these algorithms are complex, there is a need for a system which would enable their application both in everyday work and education processes. This paper describes a software system for learning AI algorithms called SAIL (Software System for AI Learning), which can be used both on computers and mobile devices. The paper gives examples of lab exercises and self‐study tasks that through graphic representation and detailed procedures help students master this area. Students can enter their examples into the system and obtain correct solutions for those examples. At any point when an example is simulated, a student can proceed to the next step or go back to the previous one, save the current simulation as a file, or print the detailed procedure as a task solution. SAIL helps lecturers go through the syllabus more efficiently and improve class material, while at the same time it helps students get a better grasp of implemented algorithms. SAIL can also benefit software engineers, who can select and simulate an adequate algorithm to solve a specific problem. The results of the SAIL system are verified within the AI introductory course at the School of Electrical Engineering University of Belgrade and they are presented in this paper.

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In this research, a method of developing a machine model for sentiment processing in the Serbian language is presented. The Serbian language, unlike English and other popular languages, belongs to the group of languages with limited resources. Three different data sets were used as a data source: a balanced set of music album reviews, a balanced set of movie reviews, and a balanced set of music album reviews in English—MARD—which was translated into Serbian. The evaluation included applying developed models with three standard algorithms for classification problems (naive Bayes, logistic regression, and support vector machine) and applying a hybrid model, which produced the best results. The models were trained on each of the three data sets, while a set of music reviews originally written in Serbian was used for testing the model. By comparing the results of the developed model, the possibility of expanding the data set for the development of the machine model was also evaluated.