Abstract: In previous solutions, the authors considered the uniform dispersal problem (or Filling problem) in which inexpensive robots had to disperse in order to cover an a priori not known area, as well as they also examined the possibilities of solving the Filling in two-dimensional regions. The swarm entities had to collectively solve a common task using the simplified cognitive abilities of the robots (i.e., their memory, visibility, and communication capabilities were restricted). In this paper, the authors investigate the possibilities to apply the method for three-dimensional regions. The need for such a solution emerged, as nowadays the number of low-priced flying robots, e.g., quadcopters, drones, has increased heavily. The main research direction is to minimize the hardware requirements of these robots, as doing so is crucial in order to maintain their cost-efficiency. The authors demonstrate that it is still possible to solve the Filling problem in three-dimensional space in the presence of obstacles, while the robots maintain the following hardware requirements: they have a constant amount of memory, minimal visibility, as well as there is no communication between them, and the algorithm terminates in linear runtime. Finally, simulations were carried out to prove the theoretical results.

Keywords: uniform dispersal; distributed robotics; autonomous robots

Abstract: This paper provides a general overview of the literature regarding advanced assistive technologies devoted to improving elders’ life. Recent studies on assistive robots and embodied conversational agents are carefully examined in order to identify main seniors’ preferences regarding their general design. While providing data on seniors’ preferences about the design of assistive devices, main evidence on both robots and virtual agent’s appearance, abilities/functionalities, personalities, and role features are summarized and commented.

Keywords: older user; assistive technology; robot; virtual agent; design; preferences

Abstract: Problem-solving is considered to be an essential everyday skill, in professional as well as in personal situations. In this paper, we investigate whether a predictive model for a problem-solving process based on data mining techniques can be derived from raw log-files recorded by a computer-based assessment system. Modern informatics-based education relies on electronic assessment systems for evaluating knowledge and skills. OECD’s PISA 2012 computer-based assessment database was used, which contains a rich problem-solving dataset. The dataset consists of detailed action logs and results for several problem-solving tasks. Two feature sets were extracted from the selected PISA 2012 Climate Control problem solving task: a set of time-based features and a set of features indicating the employment of the VOTAT problem-solving strategy. We evaluated both feature sets with six machine learning algorithms in order to predict the outcome of the problem-solving process, compared their performance and analyzed which algorithms yield better results with respect to the observed feature set. The approach presented in this paper can be used as a potential tool for better understanding of problem-solving patterns, and also for implementing interactive e-learning systems for training problem solving skills.

Keywords: machine learning; data mining; predictive model; problem solving

Abstract: The brain-computer interface is one of the most up-to-date communication options. The advances made in this area open up opportunities to help mentally or physically disadvantaged people. The brain-computer interface offers the possibility of re-acquiring communication skills by deaf individuals. Electroencephalography (EEG) based speech recognition is, therefore, a novel research topic, which is an important component in communication technologies. In this article, we propose a speech activity detector algorithm, which, as expected, should improve the performance of the EEG based speech recognition system. EEG data uploaded while pronouncing 50 different phrases were classified using a feed-forward neural network. As a result of detection, a 0.82 F1 score was achieved.

Keywords: electroencephalography; speech activity detection; EEG-based speech recognition; brain-computer interface

Abstract: During the observation, analysis, and examination of cognitive processes, human-computer interfaces are increasingly becoming widespread. Programming could also be seen as a complex cognitive process. This study aims to examine the efficiency of the clean code paradigm and compares it to the dirty code produced without the principles formulated in this technique. In addition to the traditional knowledge level test and subjective judgment, the readability and comprehensibility of the implemented code were determined by analysing the heatmap and gaze route besides measuring and evaluating eye movement parameters. Based on the statistical evaluation, it can be stated that there is a significant difference in the average number of fixations, the average fixation time, and the average length of routes between fixations measured by studying two differently written source codes. This means that in the case of the clean code, significantly less and shorter information recording and processing were necessary to understand the code.

Keywords: eye-tracking; programming; clean code paradigm; dirty code

Abstract: Self-study and self-efficacy are closely related concepts, and they are in connection even with lifelong learning. To develop real self-esteem in the learning process, it is important to be aware of one's abilities, weaknesses, and other characteristics that influence learning, which reinforces the foundations of learning motivation and lifelong learning. Self-efficacy and modern ICT solutions supporting self-directed learning become a more relevant role in self-learning outside the school context, i.e. in self-taught learning. This article examines self-efficacy in learning through a questionnaire survey for students at technical vocational secondary schools in Budapest (N = 1260) analyzing the answers to some of the self-efficacy statements based on the Motivated Strategies for Learning Questionnaire (MSLQ). The results can help develop modern ICT solutions that effectively support learning.

Keywords: self-efficacy; cognitive conditions; Motivated Strategies for Learning Questionnaire

Abstract: In the era of digital transformation, some questions occur whether the teaching methods used in higher education are suitable for the students belonging to generation Z or these methods are appropriate enough to make them more attentive or motivated. The diverse methods of information technology have already spread into university education and a pedagogical paradigm shift can be perceived. The latest methodology, like project method, e-learning, BYOD, gamification, MOOC are available in higher education as well; however, it is a question to what extent teachers apply these methods in their teaching process. The new techniques not only make the students more motivated but also enhance them with those sorts of skills that are indispensable to be successful in the field of labor. The research was aimed to find answers about how much engineer students are satisfied with the training they are given in higher education in the 21st Century, what kind of attitude they have towards educational technology.

Keywords: teaching-learning startegies and techniques; up-to-date teaching methodology; educational technology; generation Z learning style and attention; engineering education; career development