The Internet of Things (IoT) is becoming a regular part of our lives. The devices can be used in many sectors, such as education and in the learning process. The article describes the possibilities of using commonly available devices such as smart wristbands (watches) and eye tracking technology, i.e., using existing technical solutions and methods that rely on the application of sensors while maintaining non-invasiveness. By comparing the data from these devices, we observed how the students’ attention affects their results. We looked for a correlation between eye tracking, heart rate, and student attention and how it all impacts their learning outcomes. We evaluate the obtained data in order to determine whether there is a degree of dependence between concentration and heart rate of students.

The following case study was carried out on a sample of one experimental and one control group. The participants of the experimental group watched the movie section from the standardized LATEMO-E database via virtual reality (VR) on Oculus Rift S and HTC Vive Pro devices. In the control group, the movie section was displayed on the LCD monitor. The movie section was categorized according to Ekman's and Russell's classification model of evoking an emotional state. The range of valence and arousal was determined in both observed groups. Valence and arousal were measured in each group using a Self-Assessment Manikin (SAM). The control group was captured by a camera and evaluated by Affdex software from Affectiva in order to compare valence values. The control group showed a very high correlation (0.92) between SAM and Affdex results. Having considered the Affdex results as a reference value, it can be concluded that SAM participants evaluated their emotions objectively. The results from both groups show that the movie section is supposed to evoke negative emotion. Negative emotion was perceived more intensely than its counterpart, positive emotion. Using virtual reality to evoke negative emotion (anger) has confirmed that VR triggers a significantly stronger intensity of emotion than LCD.

Educational data preprocessing from log files represents a time-consuming phase of the knowledge discovery process. It consists of data cleaning, user identification, session identification, and path completion phase. This paper attempts to identify phases, which are necessary in the case of preprocessing of educational data for further application of learning analytics methods. Since the sequential patterns analysis is considered suitable for estimating of discovered knowledge, this paper tries answering the question, which of these preprocessing phases has a significant impact on discovered knowledge in general, as well as in the meaning of quality and quantity of found sequence patterns. Therefore, several data preprocessing techniques for session identification and path completion were applied to prepare log files with different levels of data preprocessing. The results showed that the session identification technique using the reference length, calculated from the sitemap, had a significant impact on the quality of extracted sequence rules. The path completion technique had a significant impact only on the quantity of extracted sequence rules. The found results together with the results of the previous systematic research in educational data preprocessing can improve the automation of the educational data preprocessing phase as well as it can contribute to the development of learning analytics tools suitable for different groups of stakeholders engaged in the educational data mining research activities.

Virtual learning environment can nowadays provide good foundation for students to successfully manage any subject that is educated with the help of virtual learning environment. Backward analysis of student behavior in electronic course depending on their final marks, may reveal weaknesses of the course, or possible problem areas of teaching. This research is focused on analyzing the influence of behavior of students in electronic course on their final marks at the end of term. The authors present a detailed log file preprocessing procedure that makes it possible to examine the behavior of students in a virtual learning environment during the term. Individual electronic course activities were divided into interrelated logical groups and based on that were identified association rules between the content - course activities - course modules and the final marks. The rules represent the access of a particular group of students, who were divided according their final marks, to the created activity groups. The results of the analysis showed that the final marks correspond to the behavior of students in virtual learning environment. Despite that the research did not confirm the high degree of dependency, the results could help improve the teaching using the electronic course. The results also showed that repeating autotest tasks to improve the knowledge necessary for the final examination, did not bring the expected effect.

At current e-learning platforms, is often seen non-efficient usage of their possibilities when creating educational content. This article deals with the possibilities of using adaptive tools that are offered by learning management system (LMS) Moodle when creating a personalised e-course. The methodology created by the authors of the article for personalised e-course adjusts the study content based on characteristics of each student stated by his or her initial knowledge, learning style, and motivation. The article is aimed at the presentation of the created methodology and its impact on the level of student's output knowledge as well as overall learning efficiency. By using the methodology, there was an opportunity to compare the impact of two different approaches - the personalised one and non-personalised. Statistical analysis revealed that the use of personalized e-course has a positive impact on students' activity, motivation, and their level of output knowledge. The results showed that the attended secondary school has no or only minimal impact on the output knowledge if the students studied through the personalized e-course. An interesting finding was that students in all surveys have a stronger tendency to prefer the same learning styles over the years.

This research explores the effectiveness of machine translation from Slovak to English for sentiment analysis, specifically focusing on the translation of movie subtitles. The study employs a parallel corpus of segmented movie subtitles in both languages and utilizes IBM Watson™ Natural Language Understanding service and Google Translate. The research aims to assess the correlation between human-generated text and machine-translated text in sentiment analysis. A comparative analysis was also conducted using OpenAI to evaluate the sentiment of the Slovak text directly, without translation into English. The findings reveal a strong correlation between human text and machine translation, with a Pearson correlation coefficient of 0.86, and a correlation with OpenAI’s GPT model evaluation at 0.72. Despite the relatively high accuracy of the end-to-end solution using OpenAI, the methodology comprising machine translation followed by sentiment analysis in English was found to be significantly more precise. The research further investigates the challenges in translating specific language nuances, such as humor and vulgarism, and their impact on sentiment analysis. The study concludes that machine translation can be effectively used for sentiment analysis in Slovak, a flective language, and highlights the potential of advanced language models in low-resource languages. Future research directions include expanding the study to other text types and comparable languages beyond Slovak.