This page has only limited features, please log in for full access.

Unclaimed
Michal Munk
Department of Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovakia

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 19 July 2021 in PeerJ Computer Science
Reads 0
Downloads 0

Research of the techniques for effective fake news detection has become very needed and attractive. These techniques have a background in many research disciplines, including morphological analysis. Several researchers stated that simple content-related n-grams and POS tagging had been proven insufficient for fake news classification. However, they did not realise any empirical research results, which could confirm these statements experimentally in the last decade. Considering this contradiction, the main aim of the paper is to experimentally evaluate the potential of the common use of n-grams and POS tags for the correct classification of fake and true news. The dataset of published fake or real news about the current Covid-19 pandemic was pre-processed using morphological analysis. As a result, n-grams of POS tags were prepared and further analysed. Three techniques based on POS tags were proposed and applied to different groups of n-grams in the pre-processing phase of fake news detection. The n-gram size was examined as the first. Subsequently, the most suitable depth of the decision trees for sufficient generalization was scoped. Finally, the performance measures of models based on the proposed techniques were compared with the standardised reference TF-IDF technique. The performance measures of the model like accuracy, precision, recall and f1-score are considered, together with the 10-fold cross-validation technique. Simultaneously, the question, whether the TF-IDF technique can be improved using POS tags was researched in detail. The results showed that the newly proposed techniques are comparable with the traditional TF-IDF technique. At the same time, it can be stated that the morphological analysis can improve the baseline TF-IDF technique. As a result, the performance measures of the model, precision for fake news and recall for real news, were statistically significantly improved.

ACS Style

Jozef Kapusta; Martin Drlik; Michal Munk. Using of n-grams from morphological tags for fake news classification. PeerJ Computer Science 2021, 7, e624 .

AMA Style

Jozef Kapusta, Martin Drlik, Michal Munk. Using of n-grams from morphological tags for fake news classification. PeerJ Computer Science. 2021; 7 ():e624.

Chicago/Turabian Style

Jozef Kapusta; Martin Drlik; Michal Munk. 2021. "Using of n-grams from morphological tags for fake news classification." PeerJ Computer Science 7, no. : e624.

Journal article
Published: 04 June 2021 in Sustainability
Reads 0
Downloads 0

The requirements imposed on schools and the competencies of teachers change depending on the development of society, and currently their constant growth is considerable. These facts lead to the need to continuously innovate pre-service teacher training, especially with a focus on creating professional digital literacy. The creation of a proposal of an optimal model of pre-service teacher training in the field of teacher trainees’ professional didactic-technological competency development was the subject of the research, which is described in the article. The described research examined the importance of the integration of various kinds of digital didactic tools into pre-service teacher training curricula with regard to the successful performance of the teaching profession. The necessary research data were obtained on the basis of screening the opinions of teacher trainees in Slovakia and the Czech Republic (n = 280). The respondents of the research survey expressed, in terms of various aspects, their opinions on the importance of integrating the issue of working with specified kinds of the given digital means into the curricula of teacher trainees’ study programs. The obtained research data were analysed depending on three segmentation factors of the respondents, which were the nationality of the student (i.e., the COUNTRY of his/her study), the GENDER of the respondent, and the combination of these two factors, i.e., COUNTRY X GENDER. According to the achieved results, there is a need to include or strengthen the teaching of software applications such as ActivInspire, FreeMind, SMART Notebook, Google Docs and, if possible, Prezi and Mindomo, and also a need to emphasize the methodological aspects of the use of these technical means in teaching.

ACS Style

Ján Záhorec; Alena Hašková; Adriana Poliaková; Michal Munk. Case Study of the Integration of Digital Competencies into Teacher Preparation. Sustainability 2021, 13, 6402 .

AMA Style

Ján Záhorec, Alena Hašková, Adriana Poliaková, Michal Munk. Case Study of the Integration of Digital Competencies into Teacher Preparation. Sustainability. 2021; 13 (11):6402.

Chicago/Turabian Style

Ján Záhorec; Alena Hašková; Adriana Poliaková; Michal Munk. 2021. "Case Study of the Integration of Digital Competencies into Teacher Preparation." Sustainability 13, no. 11: 6402.

Research article
Published: 22 April 2021 in Interactive Learning Environments
Reads 0
Downloads 0

The main aim of this paper is to present results of an experimental test focused on the validity and effectiveness of composed methodology aimed at increasing the student's attention in Virtual Learning Environment. Areas of presented methodology which were subject of our research is students behavior during learning. The behavioral part of methodology is focused on calculating estimated time for reading/learning specific learning material based on mathematical model. The model has been created as linear regression model trained and validated with student's access logs from Moodle instance used at the university. The methodology created by us, which is the subject of the experiment, uses the mentioned mathematical model in order to more optimally calculate the time-on-task and thus assume the time required for the correct understanding of the presented curriculum in the form of e-learning. In this paper, we will focus on confirming the didactic effectiveness of our proposed methodology for monitoring student attention. We describe an experiment with which we verified the proposed methodology. Methodology itself is focused on improving students learning experiences while using e-learning platforms like Moodle.

ACS Style

Dominik Halvoník; Jozef Kapusta; Michal Munk. Improve estimated time-on-task calculation in a Virtual Learning Environment. Interactive Learning Environments 2021, 1 -16.

AMA Style

Dominik Halvoník, Jozef Kapusta, Michal Munk. Improve estimated time-on-task calculation in a Virtual Learning Environment. Interactive Learning Environments. 2021; ():1-16.

Chicago/Turabian Style

Dominik Halvoník; Jozef Kapusta; Michal Munk. 2021. "Improve estimated time-on-task calculation in a Virtual Learning Environment." Interactive Learning Environments , no. : 1-16.

Journal article
Published: 25 March 2021 in Applied Sciences
Reads 0
Downloads 0

This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission’s (EC’s) tool, which was trained on a specific-domain.

ACS Style

Lucia Benkova; Dasa Munkova; Ľubomír Benko; Michal Munk. Evaluation of English–Slovak Neural and Statistical Machine Translation. Applied Sciences 2021, 11, 2948 .

AMA Style

Lucia Benkova, Dasa Munkova, Ľubomír Benko, Michal Munk. Evaluation of English–Slovak Neural and Statistical Machine Translation. Applied Sciences. 2021; 11 (7):2948.

Chicago/Turabian Style

Lucia Benkova; Dasa Munkova; Ľubomír Benko; Michal Munk. 2021. "Evaluation of English–Slovak Neural and Statistical Machine Translation." Applied Sciences 11, no. 7: 2948.

Journal article
Published: 01 February 2021 in IEEE Access
Reads 0
Downloads 0

Many contemporary studies realized in the Learning Analytics research field provide substantial insights into the virtual learning environment stakeholders’ behaviour on single-course or small-scale level. They used different knowledge discovery techniques, including frequent patterns analysis. However, there are only a few studies that have explored the stakeholders’ behaviour over a more extended period of several academic years in detail. This article contributes to filling in this gap and provides a novel approach to using homogeneous groups of frequent patterns for identifying the changes in stakeholders’ behaviour from the perspective of time. The novelty of this approach lies in fact, that even though the time variable is not directly involved, identification of homogeneous groups of frequent itemsets allows analysis and comparison of the stakeholders’ behavioral patterns and their changes over different observed periods. Found homogeneous groups of frequent itemsets, which conform minimal threshold of selected measures, showed, that it is possible to uncover the changes in stakeholders’ behaviour throughout the observed longer period. As a result, these homogenous groups of found frequent patterns allow a better understanding of the hidden changes in seasonality or trends in stakeholders’ behaviour over several academic years. This article discusses the possible implications of the results and proposed approach in the context of virtual learning environment management and educational content improvement.

ACS Style

Martin Drlik; Michal Munk; Jan Skalka. Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining. IEEE Access 2021, 9, 23795 -23813.

AMA Style

Martin Drlik, Michal Munk, Jan Skalka. Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining. IEEE Access. 2021; 9 ():23795-23813.

Chicago/Turabian Style

Martin Drlik; Michal Munk; Jan Skalka. 2021. "Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining." IEEE Access 9, no. : 23795-23813.

Journal article
Published: 31 December 2020 in Applied Sciences
Reads 0
Downloads 0

The article deals with methods and methodology of pedagogical research in the didactics of informatics in the environment of the Massive Open Online Course (MOOC) based on the principles of connectivism, which offers open, non-formal, and community education. The MOOC was created as a support for the implementation of pedagogical research (primarily for final theses), specifically for solving a causal research problem, which is the most challenging research issue in pedagogical research. We found that the created MOOC is didactically effective for all study degrees. We also identified the parts of the course (which correspond to the stages of processing the experiment) causing the participants the biggest problems. The surprising finding was that the data understanding (data exploration phase) causes the problem the most. Its importance was underestimated when designing the MOOC. Although this phase is the least computationally demanding, it is very important since it is subsequently related to the correct determination of the null statistical hypotheses.

ACS Style

Miroslav Kadlečík; Michal Munk; Daša Munková. The Efficacy of MOOC to Support Students in Pedagogical Research. Applied Sciences 2020, 11, 328 .

AMA Style

Miroslav Kadlečík, Michal Munk, Daša Munková. The Efficacy of MOOC to Support Students in Pedagogical Research. Applied Sciences. 2020; 11 (1):328.

Chicago/Turabian Style

Miroslav Kadlečík; Michal Munk; Daša Munková. 2020. "The Efficacy of MOOC to Support Students in Pedagogical Research." Applied Sciences 11, no. 1: 328.

Journal article
Published: 04 June 2020 in Procedia Computer Science
Reads 0
Downloads 0
ACS Style

Jozef Kapusta; Petr Hájek; Michal Munk; Ľubomír Benko. Comparison of fake and real news based on morphological analysis. Procedia Computer Science 2020, 171, 2285 -2293.

AMA Style

Jozef Kapusta, Petr Hájek, Michal Munk, Ľubomír Benko. Comparison of fake and real news based on morphological analysis. Procedia Computer Science. 2020; 171 ():2285-2293.

Chicago/Turabian Style

Jozef Kapusta; Petr Hájek; Michal Munk; Ľubomír Benko. 2020. "Comparison of fake and real news based on morphological analysis." Procedia Computer Science 171, no. : 2285-2293.

Journal article
Published: 04 June 2020 in Procedia Computer Science
Reads 0
Downloads 0
ACS Style

Peter Svec; Lubomir Benko; Miroslav Kadlečík; Jan Kratochvíl; Michal Munk. Web Usage Mining: Data Pre-processing Impact on Found Knowledge in Predictive Modelling. Procedia Computer Science 2020, 171, 168 -178.

AMA Style

Peter Svec, Lubomir Benko, Miroslav Kadlečík, Jan Kratochvíl, Michal Munk. Web Usage Mining: Data Pre-processing Impact on Found Knowledge in Predictive Modelling. Procedia Computer Science. 2020; 171 ():168-178.

Chicago/Turabian Style

Peter Svec; Lubomir Benko; Miroslav Kadlečík; Jan Kratochvíl; Michal Munk. 2020. "Web Usage Mining: Data Pre-processing Impact on Found Knowledge in Predictive Modelling." Procedia Computer Science 171, no. : 168-178.

Journal article
Published: 04 June 2020 in Procedia Computer Science
Reads 0
Downloads 0
ACS Style

Dasa Munkova; Petr Hajek; Michal Munk; Jan Skalka. Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy. Procedia Computer Science 2020, 171, 1327 -1336.

AMA Style

Dasa Munkova, Petr Hajek, Michal Munk, Jan Skalka. Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy. Procedia Computer Science. 2020; 171 ():1327-1336.

Chicago/Turabian Style

Dasa Munkova; Petr Hajek; Michal Munk; Jan Skalka. 2020. "Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy." Procedia Computer Science 171, no. : 1327-1336.

Conference paper
Published: 29 May 2020 in Collaboration in a Hyperconnected World
Reads 0
Downloads 0

Automated opinion mining of consumer reviews is becoming increasingly important due to the rising influence of reviews on online retail shopping. Existing approaches to automated opinion classification rely either on sentiment lexicons or supervised machine learning. Deep neural networks perform this classification task particularly well by utilizing dense document representation in terms of word embeddings. However, this representation model does not consider the sentiment polarity or sentiment intensity of the words. To overcome this problem, we propose a novel model of deep neural network with word-sentiment associations. This model produces richer document representation that incorporates both word context and word sentiment. Specifically, our model utilizes pre-trained word embeddings and lexicon-based sentiment indicators to provide inputs to a deep feed-forward neural network. To verify the effectiveness of the proposed model, a benchmark dataset of Amazon reviews is used. Our results strongly support integrated document representation, which shows that the proposed model outperforms other existing machine learning approaches to opinion mining of consumer reviews.

ACS Style

Petr Hajek; Aliaksandr Barushka; Michal Munk. Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations. Collaboration in a Hyperconnected World 2020, 419 -429.

AMA Style

Petr Hajek, Aliaksandr Barushka, Michal Munk. Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations. Collaboration in a Hyperconnected World. 2020; ():419-429.

Chicago/Turabian Style

Petr Hajek; Aliaksandr Barushka; Michal Munk. 2020. "Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations." Collaboration in a Hyperconnected World , no. : 419-429.

Journal article
Published: 01 May 2020 in www.amfiteatrueconomic.ro
Reads 0
Downloads 0
ACS Style

Juraj Cheben; Drahoslav Lancaric; Michal Munk; Peter Obdrzalek. Determinants of Economic Sustainability in Higher Education Institutions. www.amfiteatrueconomic.ro 2020, 22, 1 .

AMA Style

Juraj Cheben, Drahoslav Lancaric, Michal Munk, Peter Obdrzalek. Determinants of Economic Sustainability in Higher Education Institutions. www.amfiteatrueconomic.ro. 2020; 22 (54):1.

Chicago/Turabian Style

Juraj Cheben; Drahoslav Lancaric; Michal Munk; Peter Obdrzalek. 2020. "Determinants of Economic Sustainability in Higher Education Institutions." www.amfiteatrueconomic.ro 22, no. 54: 1.

Conference paper
Published: 18 March 2020 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Due to the fast development of digital technology applications, there is a need to innovate continually content of those parts of teacher trainees study programs which are aimed at the teacher trainee didactic technological competences. To find an optimal model of pre-gradual teachers training in the area of their didactic technological competence development, a research which is described in the paper has been done. Within the research, significance of incorporation of different kinds of software/hardware products into the teacher training study programs was examined. There were examined 9 kinds of digital products, in particular: ActivInspire, SMART Notebook, Flow!Works; Prezi; FreeMind, Mindomo, XMind; ActivExpression2, ActiVote, QRF700/900, Turning Point; Socrative 2.0; Google documents; Microsoft PowerPoint; Microsoft Excel; Microsoft Word. Necessary research data were obtained based on a screening of teacher trainees’ opinions on several aspects relating to each of the given kinds of digital software/hardware products, e.g. on significance of incorporation digital products of the relevant kind into the teacher trainee study programs (teacher pre-gradual preparation).

ACS Style

Ján Záhorec; Alena Hašková; Michal Munk. Integration of Digital Technology Applications into the Pre-gradual Teacher Training. Advances in Intelligent Systems and Computing 2020, 892 -901.

AMA Style

Ján Záhorec, Alena Hašková, Michal Munk. Integration of Digital Technology Applications into the Pre-gradual Teacher Training. Advances in Intelligent Systems and Computing. 2020; ():892-901.

Chicago/Turabian Style

Ján Záhorec; Alena Hašková; Michal Munk. 2020. "Integration of Digital Technology Applications into the Pre-gradual Teacher Training." Advances in Intelligent Systems and Computing , no. : 892-901.

Journal article
Published: 01 February 2020 in Neural Computing and Applications
Reads 0
Downloads 0

Fake consumer review detection has attracted much interest in recent years owing to the increasing number of Internet purchases. Existing approaches to detect fake consumer reviews use the review content, product and reviewer information and other features to detect fake reviews. However, as shown in recent studies, the semantic meaning of reviews might be particularly important for text classification. In addition, the emotions hidden in the reviews may represent another potential indicator of fake content. To improve the performance of fake review detection, here we propose two neural network models that integrate traditional bag-of-words as well as the word context and consumer emotions. Specifically, the models learn document-level representation by using three sets of features: (1) n-grams, (2) word embeddings and (3) various lexicon-based emotion indicators. Such a high-dimensional feature representation is used to classify fake reviews into four domains. To demonstrate the effectiveness of the presented detection systems, we compare their classification performance with several state-of-the-art methods for fake review detection. The proposed systems perform well on all datasets, irrespective of their sentiment polarity and product category.

ACS Style

Petr Hajek; Aliaksandr Barushka; Michal Munk. Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Computing and Applications 2020, 32, 17259 -17274.

AMA Style

Petr Hajek, Aliaksandr Barushka, Michal Munk. Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Computing and Applications. 2020; 32 (23):17259-17274.

Chicago/Turabian Style

Petr Hajek; Aliaksandr Barushka; Michal Munk. 2020. "Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining." Neural Computing and Applications 32, no. 23: 17259-17274.

Conference paper
Published: 01 December 2019 in Learning and Analytics in Intelligent Systems
Reads 0
Downloads 0

The aim of the research is twofold: to evaluate the translation quality of the individual sentences of the MT output and also post-edited MT output on the basis of metrics of automatic MT evaluation from Slovak into the German language; and to compare the quality of MT output and post-edited MT output based on the same automatic metrics of MT evaluation. The icon graphs were used to visualize the results for individual sentences. A significant difference was found in sentence 36 in favor of the post-edited MT output and vice versa in sentence 5 in favor of MT output. Due to the error rate, a significant difference was in sentence 29 and 11 in favor of post-edited MT output and vice versa the sentence 26 in favor of MT output. Based on our results we can state that it is necessary to include into the evaluation of the quality of translation all automatic metrics for each sentence separately.

ACS Style

Daša Munková; Michal Munk; Jan Skalka; Karol Kasaš. Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages. Learning and Analytics in Intelligent Systems 2019, 416 -425.

AMA Style

Daša Munková, Michal Munk, Jan Skalka, Karol Kasaš. Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages. Learning and Analytics in Intelligent Systems. 2019; ():416-425.

Chicago/Turabian Style

Daša Munková; Michal Munk; Jan Skalka; Karol Kasaš. 2019. "Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages." Learning and Analytics in Intelligent Systems , no. : 416-425.

Journal article
Published: 14 May 2019 in International Journal of Emerging Technologies in Learning (iJET)
Reads 0
Downloads 0

If we are talking about user behavior analytics, we have to understand what the main source of valuable information is. One of these sources is definitely a web server. There are multiple places where we can extract the necessary data. The most common ways are to search for these data in access log, error log, custom log files of web server, proxy server log file, web browser log, browser cookies etc. A web server log is in its default form known as a Common Log File (W3C, 1995) and keeps information about IP address; date and time of visit; ac-cessed and referenced resource. There are standardized methodologies which contain several steps leading to extract new knowledge from provided data. Usu-ally, the first step is in each one of them to identify users, users’ sessions, page views, and clickstreams. This process is called pre-processing. Main goal of this stage is to receive unprocessed web server log file as input and after processing outputs meaningful representations which can be used in next phase. In this pa-per, we describe in detail user session identification which can be considered as most important part of data pre-processing. Our paper aims to compare the us-er/session identification using the STT with the identification of user/session us-ing cookies. This comparison was performed concerning the quality of the se-quential rules generated, i.e., a comparison was made regarding generation useful, trivial and inexplicable rules.

ACS Style

Jozef Kapusta; Michal Munk; Dominik Halvoník; Martin Drlik. User Identification in the Process of Web Usage Data Preprocessing. International Journal of Emerging Technologies in Learning (iJET) 2019, 14, 21 -33.

AMA Style

Jozef Kapusta, Michal Munk, Dominik Halvoník, Martin Drlik. User Identification in the Process of Web Usage Data Preprocessing. International Journal of Emerging Technologies in Learning (iJET). 2019; 14 (9):21-33.

Chicago/Turabian Style

Jozef Kapusta; Michal Munk; Dominik Halvoník; Martin Drlik. 2019. "User Identification in the Process of Web Usage Data Preprocessing." International Journal of Emerging Technologies in Learning (iJET) 14, no. 9: 21-33.

Journal article
Published: 30 March 2019 in Agris on-line Papers in Economics and Informatics
Reads 0
Downloads 0
ACS Style

Renáta Krajčírová; Alexandra Ferenczi Vaňová; Michal Munk. What Is Relationship between Profits and Dividends in Agricultural Legal Entities? Agris on-line Papers in Economics and Informatics 2019, 11, 55 -64.

AMA Style

Renáta Krajčírová, Alexandra Ferenczi Vaňová, Michal Munk. What Is Relationship between Profits and Dividends in Agricultural Legal Entities? Agris on-line Papers in Economics and Informatics. 2019; 11 (1):55-64.

Chicago/Turabian Style

Renáta Krajčírová; Alexandra Ferenczi Vaňová; Michal Munk. 2019. "What Is Relationship between Profits and Dividends in Agricultural Legal Entities?" Agris on-line Papers in Economics and Informatics 11, no. 1: 55-64.

Conference paper
Published: 16 March 2019 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

Nowadays, post-editing of machine translation output represents a significant element in the translation market and industry. Subsequently, the preparation of future translators must cover not only all routine methods but must be cost-effective, efficient and in accordance with human resources available. That is the reason we use Internet-based technologies more and more. New emerging technologies are very often driven by the marketing power of companies developing and selling applications. Each of us experienced dozens of fantastic features available in teaching software and applications. The core skill of the online educator is to find a balance between our needs and ability to use technology. Since translation demand keeps growing every day, a large number of translators use various technical tools including translation memories, terminology management tools or Machine Translation (MT) technologies and thus increase their productivity and meet this high demand. The post-editing of MT should be only done by a person who is familiar with this method and knows exactly what, how and how much needs to be edited in the text. Otherwise, the sense of post-editing is losing importance, as the work of post-editor would not be more effective as a translator’s, who translates the text traditional way “from scratch”. The contribution of the paper is to create an online educational system tailored to translators’ needs; an online system in which students translate and revise a text, post-edit machine translation output and also assess the quality of the translation.

ACS Style

Daša Munková; Michal Munk; Ľubomír Benko; Jakub Absolon. From Old Fashioned “One Size Fits All” to Tailor Made Online Training. Advances in Intelligent Systems and Computing 2019, 365 -376.

AMA Style

Daša Munková, Michal Munk, Ľubomír Benko, Jakub Absolon. From Old Fashioned “One Size Fits All” to Tailor Made Online Training. Advances in Intelligent Systems and Computing. 2019; ():365-376.

Chicago/Turabian Style

Daša Munková; Michal Munk; Ľubomír Benko; Jakub Absolon. 2019. "From Old Fashioned “One Size Fits All” to Tailor Made Online Training." Advances in Intelligent Systems and Computing , no. : 365-376.

Conference paper
Published: 01 March 2019 in Advances in Intelligent Systems and Computing
Reads 0
Downloads 0

The paper presents preliminary results of the research aim of which is to support modernization and optimation of teacher training study programs in their parts related to formation didactic technological professional competences of teacher trainees. The paper focuses on the research results of a screening in which practising teachers assessed significance of the use of various interactive educational activities and digital means in teaching process from the point of view of different aspects of education. Among these aspects were stated e.g. pupils motivation, easier understanding of the presented subject matter, skills to apply the acquired knowledge etc. The assessments given by teachers were tested in dependence on different factors, from which the most important was the factor of the teaching staff category the teachers belonged to. As the results show, there were identified some significant differences in the assessments of the contribution of the use of various interactive educational activities and digital means to the teaching process efficiency given by teachers of different levels of education.

ACS Style

Ján Záhorec; Alena Hašková; Michal Munk. First Results of a Research Focused on Teachers’ Didactic Technological Competences Development. Advances in Intelligent Systems and Computing 2019, 461 -472.

AMA Style

Ján Záhorec, Alena Hašková, Michal Munk. First Results of a Research Focused on Teachers’ Didactic Technological Competences Development. Advances in Intelligent Systems and Computing. 2019; ():461-472.

Chicago/Turabian Style

Ján Záhorec; Alena Hašková; Michal Munk. 2019. "First Results of a Research Focused on Teachers’ Didactic Technological Competences Development." Advances in Intelligent Systems and Computing , no. : 461-472.

Journal article
Published: 17 December 2018 in IEEE Access
Reads 0
Downloads 0

The learning analytics communities, as well as most learning analytics research, have not frequently focused on time-based trends in the same virtual learning environment over different years of deployment, or temporal trends in the selection of different activity types over a typical day. The paper contributes to this debate and provides a novel approach to learning analytics using a multinomial logit model for modelling the probabilities of students’ choice of learning activities during the hours of the day over several academic years. An abstraction called activity is introduced, which categorizes individual student’s log accesses to the virtual learning environment into more semantically meaningful categories. Consequently, the activity represents a sequence of semantically meaningful web accesses related to a particular activity or task that a student of the virtual learning environment performs. The paper includes a comprehensive explanation of the model and an evaluation of the model. The paper introduces a case study, which shows that the multinomial logit model can give useful insight into the course schedule, as it shows what the peak times are for different types of activities. The paper also discusses the possible implications of the results in the context of virtual learning environment management and content improvement at the institutional level.

ACS Style

Martin Drlik; Michal Munk. Understanding Time-Based Trends in Stakeholders’ Choice of Learning Activity Type Using Predictive Models. IEEE Access 2018, 7, 3106 -3121.

AMA Style

Martin Drlik, Michal Munk. Understanding Time-Based Trends in Stakeholders’ Choice of Learning Activity Type Using Predictive Models. IEEE Access. 2018; 7 (99):3106-3121.

Chicago/Turabian Style

Martin Drlik; Michal Munk. 2018. "Understanding Time-Based Trends in Stakeholders’ Choice of Learning Activity Type Using Predictive Models." IEEE Access 7, no. 99: 3106-3121.

Journal article
Published: 14 December 2018 in EURASIP Journal on Audio, Speech, and Music Processing
Reads 0
Downloads 0
ACS Style

Jiri Stastny; Michal Munk; Lubos Juranek. Automatic bird species recognition based on birds vocalization. EURASIP Journal on Audio, Speech, and Music Processing 2018, 2018, 1 .

AMA Style

Jiri Stastny, Michal Munk, Lubos Juranek. Automatic bird species recognition based on birds vocalization. EURASIP Journal on Audio, Speech, and Music Processing. 2018; 2018 (1):1.

Chicago/Turabian Style

Jiri Stastny; Michal Munk; Lubos Juranek. 2018. "Automatic bird species recognition based on birds vocalization." EURASIP Journal on Audio, Speech, and Music Processing 2018, no. 1: 1.