Martin Drlik

Journals

• Skalka, J. et al. (2026). Log-Based User Personas: A Typology-Guided Framework for Clustering Gameplay Behavior. IEEE ACCESS 14, pp. 14558-14578.
• Vojtek, M. et al. (2026). Dataset (2017-2023) of physical-geographical predictors for machine learning modelling of fluvial flooding: the Gidra river case, Slovakia. DATA IN BRIEF 64.
• Pecuchova, J., Benko, L., Drlik, M. (2025). Automated Grading of Open-Ended Questions in Higher Education Using GenAI Models. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 35(6), pp.3813-3846.
• Pecuchova, J., Drlik, M. (2024). Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students' Digital Traces. IEEE ACCESS 12, pp.159336-159367.
• Morze, N. - Smyrnova-Trybulska, E. - Drlik, M. - Buinytska, O. (2023). Development of Advanced Digital Ecosystems at Universities: A Study Comparing Experiences from Ukraine, Poland and Slovakia. In European Journal of Education, vol. 58, no. 4, pp. 647–664.
• Skalka, J., Drlik, M. (2023). Development of Automatic Source Code Evaluation Tests Using Grey-Box Methods: A Programming Education Case Study. In IEEE Access, vol. 11, pp. 106772-106792, doi: 10.1109/ACCESS.2023.3317694.
• Drlík, M., Munk, M., Skalka, J. (2021). Identification of Changes in VLE Stakeholders' Behavior Over Time Using Frequent Patterns Mining. In IEEE Access, vol. 9, pp. 23795-23813, doi: 10.1109/ACCESS.2021.3056191.
• Kapusta, J. - Drlik, M. - Munk, M. (2021). Using of N-grams From Morphological Tags for Fake News. In PeerJ Computer Science, vol. 2021, no. 7, pp. 1-27. 7:e624, doi:10.7717/peerj-cs.624.
• Pecuchova, J. Drlik, M. (2021). Towards Predicting Student's Dropout in University Courses Using Different Machine Learning Techniques. Appl. Sci. 2021, 11(7), 3130.
• Skalka, J. et al. (2021). Conceptual Framework for Programming Skills Development Based on Microlearning and Automated Source Code Evaluation in Virtual Learning Environment. Sustainability 2021, 13(6), 3293.
• Drlik, M., Munk, M. (2019). Understanding Time-Based Trends in Stakeholders' Choice of Learning Activity Type Using Predictive Models. IEEE Access, 2019, 7, 3106-3121.
• Kapusta, J., Munk, M., Halvonik, D., Drlik, M. (2019). User Identification in the Process of Web Usage Data Preprocessing. In: International Journal of Emerging Technologies in Learning. - ISSN 1868-8799, vol. 14, č. 9 (2019), s. 21-33. I.
• Kapusta, J., Munk, M., Drlik, M. (2018). Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods. International Journal of Information Technology & Decision Making, Nov 2018, 17(6), 1743-1776.
• Munk, M., Drlik, M., Benko, Ľ., Reichel, J. (2017). Quantitative and Qualitative Evaluation of Sequence Patterns Found by Application of Different Educational Data Preprocessing Techniques. IEEE Access. Access-2017-02629, IF = 1.270, SJR = 0.95
• Drlik, M., Švec, P., Kapusta, J., Munk, M. et al. (2017). Identification of Differences in University E-environment between Selected EU and Non-EU Countries Using Knowledge Mining Methods: Project IRNet Case Study. Int. Journal of Web-based Communities. Inderscience. DOI: 10.1504/IJWBC.2017.10004116.
• Noskova, T., Pavlova, T., Yakovleva, O., Morze, N., Drlík, M. (2016). Quality features of university information environment in its external indicators. International Journal of Continuing Engineering Education and Life-Long Learning, 26 (2), pp. 196-216.
• Kapusta, J., Munk, M., Drlik, M. (2015). Identification of Underestimated and Overestimated Web Pages Using Page Rank and Web Usage Mining Methods. Lecture Notes in Computer Science, TRANSACTIONS ON COMPUTATIONAL COLLECTIVE INTELLIGENCE XVIII, pp. 127-146.
• Munk, M., Drlík, M. (2014). Analysis of stakeholders' behaviour depending on time in virtual learning environment. Applied Mathematics and Information Sciences, 8 (2), pp. 773-785. 2013IF: 1,232
• Drlik, M., Pilkova, A., Munk, M., Švec, P. (2013). Modelling of domestic and foreign visitors' behaviour at commercial bank website during the recent financial crisis. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 61 (7), pp. 2065-2070.
• Munk, M., Drlík, M., Kapusta, J., Munková, D. (2013). Methodology design for data preparation in the process of discovering patterns of web users behaviour. Applied Mathematics and Information Sciences, 7 (1 L), pp. 27-36. 2013IF: 1,232
• Munk, M., Pilkova, A., Kapusta, J., Svec, P., Drlik, M. (2013). Pillar 3 and modelling of stakeholders' behaviour at the commercial bank website during the recent financial crisis. Procedia Computer Science, 18, pp. 1747-1756.
• Munk, M., Pilková, A., Drlík, M., Kapusta, J., Švec, P. (2012). Verification of the fulfilment of the purposes of basel ii, pillar 3 through application of the web log mining methods. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 60 (2), pp. 217-222.
• Drlik, M., Skalka, J. (2011). Virtual faculty development using top-down implementation strategy and adapted ees model. Procedia - Social and Behavioral Sciences, 28, pp. 616-621.
• Munk, M., Drlik, M. (2011). Impact of different pre-processing tasks on effective identification of users' behavioral patterns in web-based educational system. Procedia Computer Science, 4, pp. 1640-1649.
• Drlik, M., Munk, M., Skalka, J. (2011). Usage analysis of system for theses acquisition and plagiarism detection. Procedia Computer Science, 3, pp. 866-871.
• Hvorecky, J., Drlik, M., Munk, M. (2010). The effect of visual query languages on the improvement of information retrieval skills. Procedia - Social and Behavioral Sciences, 2 (2), pp. 717-723.

Books

• Drlik, M. (2024). Learning Analytics. 1. ed., Nitra, UKF, 2024. - 218 s. - ISBN 978-80-558-2232-7.
• Kommers, P. et al. (2024). Ethical and Social Aspects of AI. 1. ed., Nitra. UKF, 2024. - 230 s. - ISBN 978-80-558-2223-5.
• Smyrnova-Trybulska, E. - Kommers, P. - Drlik, M. - Skalka, J. (2022). Microlearning: New Approaches to a More Effective Higher Education. Springer Nature Switzerland AG. pp. 1–230, ISBN: 978-303113359-6 , doi: 10.1007/978-3-031-13359-6
• Drlik, M. (2020). Selected Topics of Data Analysis and Software Development in the Learning Analytics Domain. ITA Academy.
• Munk, M., Drlik, M. (2016). Methodology of Predictive Modeling of Students' Behavior in Virtual Learning EnvironmentIn Formative Assessment, Learning Data Analytics and Gamification: In ICT Education, Elsevier, Academic Press, pp. 187-216.
• Drlik, M. et al. (2013). Moodle: Complete Guideline for Development and Administration of Electronic Courses. 1. ed. - Brno: Computer Press, 2013. - 344 s. - ISBN 978-80-251-3759-8.
• Rabova, I., Drlik, M. (2013). Enterprise Information Systems: Architecture and Development. 1. Ed. Nitra. UKF, 2013. - 165 s. - ISBN 978-80-558-0287-9. (in Slovak)
• Turcani, M. et al. (2011). Methods of Data Modelling and Analysis in Information Systems.(in Slovak). Nitra . UKF, 2011 . - 208 s . - ISBN 978-80-558-0034-9. (in Slovak)
• Svec, P. Drlik, M. Munk, M., Noskova, T., Pavlova, T., Yakovleva, O. (2017). Analysis of University e-Environment in Selected European Countries – IRNet Case Study. In Sustainability, Green IT and Education Strategies in the 21st Century. Springer. ISBN: 978-3-319-57068-6
• Skalka, J., Drlik, M., Kapusta, J. (2013). Implementácia e-learningu v univerzitnom prostredí. 1. vyd. Nitra : UKF. 82 s. ISBN 978-80-558-0298-5 (in Slovak)
• Drlik, M., Svec, P., Kapusta, J., Mesarosova, M. (2013). Moodle Kompletní průvodce tvorbou a správou elektronických kurzů. Computer Press. pp. 344. (in Czech)

Conference Proceedings

• Drlik, M., Kapusta, J. (2025). Uncovering Time-Related Students' Behavioural Patterns in Virtual Learning Environments. In: Proceedings of the International Conferences on Mobile Learning 2025 and Educational Technologies 2025, pp. 185–191.
• Pecuchova, J., Drlik, M. (2025). The Role of Generative Artificial Intelligence in the Assessment of Open-Ended Questions. In: Futureproofing Engineering Education for Global Responsibility. (ICL 2024), Lecture Notes in Networks and Systems, 2025, 1260 LNNS, pp. 394–405.
• Skalka, J., Valko, M., Drlik, M. (2025). Rapid Guessing Behaviour Identification in Programming Courses. In: Proceedings of the International Conferences on Mobile Learning and Educational Technologies 2025, pp. 239–244.
• Drlik, M., Pecuchova, J. (2025). Unveiling Behavioral Patterns in Virtual Environments through Machine Learning Techniques. In: International Conference on Machine Learning and Data Engineering (ICMLDE-2025), Procedia Computer Science 2025.
• Ali, A., Drlik, M. Wadi, M., Elmasry, W. (2025). Enhancing Privacy in Smart Grids and IOTs Systems by Using Federated Learning: Case Study. In: 7th International Conference on Smart Applications Communications and Networking Smartnets 2025.
• Popovych, V., Drlik, M. (2023) Towards Development of Data Architecture for Learning Analytics Projects Using Data Engineering Approach. In Lecture Notes in Networks and Systems, 2023, 664 LNNS, pp. 517–529.
• Pecuchova J. Drlik, M. (2023) The Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models. In Lecture Notes in Electrical Engineering, 2023, 1011 LNEE, pp. 121–133.
• Skalka, J., Drlik, M., Obonya, J. (2019). Automated Assessment in Learning and Teaching Programming Languages using Virtual Learning Environment. In: EDUCON2019: the IEEE Global Engineering Education Conference, Dubai, April 9-11, 2019. - Dubai: IEEE, 2019. - ISBN 978-1-5386-9506-7, S. 689-697. I.
• Drlik, M. (2018). Identification of necessary tasks for effective Adoption of Learning Analytics at the University. Divai 2018: 12th International Scientific Conference on Distance Learning in Applied Informatics, 2018, 405-418.
• Skalka, J., Drlik, M. (2017). Conceptual Framework of Microlearning-Based Training Mobile Application for Improving Programming Skills. In: IMCL 2017: 11th International Conference on Interactive Mobile Communication Technologies and Learning, IMCL2017; Thessaloniki; Greece; 30 November 2017 - 1 December 2017; Auer M., Tsiatsos T. (eds). - Cham: Springer, 2018. - ISSN 978-3-319-75174-0, P. 213-224.
• Skalka, J., Drlik, M. (2018). Educational Model for Improving Programming Skills based on Conceptual Microlearning Framework. In: ICL 2018: 21th International Conference on Interactive Collaborative Learning, 25-28 September 2018, Kos. - ISSN 2194-5357, P. 490-501.
• Drlik, M. (2018). Understanding of Time-based Trends in Virtual Learning Environment Stakeholders' Behaviour. In: ICL 2018: 21th International Conference on Interactive Collaborative Learning, 25-28 September 2018, Kos. - Berlin: Springer, 2018. - ISSN 2194-5357, P. 514-525.
• Skalka, J., Drlik, M. (2018). Priscilla - Proposal of System Architecture for Programming Learning and Teaching Environment. In: AICT2018: IEEE 12th International Conference on Application of Information and Communication Technologies, Almaty, 17-19 October 2018. - Almaty: IEEE, 2018. - ISBN 978-1-5386-6467-4, P. 414-419.
• Drlik, M. Skalka, J., Svec, P., Kapusta, J. (2018). Proposal of Learning Analytics Architecture Integration into University IT Infrastructure. In: AICT2018: IEEE 12th International Conference on Application of Information and Communication Technologies, Almaty, 17-19 October 2018 / Abzetdin Adamov. - Almaty: IEEE, 2018. - ISBN 978-1-5386-6467-4, P. 265-27₁
• Capay, M., Skalka, J. Drlik, M. (2017). Computer Science Learning Activities Based on Experience, 2017. In The IEEE Global Engineering Education Conference (EDUCON) 2017 : Conference Proceedings. Athens. IEEE. 2017. ISBN 978-1-5090-5466-4.
• Kapusta, J., Munk, M., Drlik, M. (2015). Experimental verification of the dependence between the expected and observed visit rate of web pages Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9227, pp. 637-648.
• Kapusta, J., Munk, M., Drlik, M. (2015). Identification of underestimated and overestimated web pages using pagerank and web usage mining methods. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9240, art. no. A7, pp. 127-146.
• Kapusta, J., Munk, M., Drlik, M. (2014). Analysis of differences between expected and observed probability of accesses to web pages. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8733, pp. 673-683.
• Drlik, M., Svec, P., Skalka, J. (2014). Comparison of Approaches to the Data Analysis in the Virtual Learning Environments. 10th International Scientific Conference on Distance Learning in Applied Informatics (DIVAI), pp. 561-572.
• Skalka, J., Drlik, M., Svec, P. (2013). Knowledge discovery from university information systems for purposes of quality assurance implementation. IEEE Global Engineering Education Conference, EDUCON, art. no. 6530165, pp. 591-596.
• Kapusta, J., Munk, M., Drlik, M. (2012). Cut-off time calculation for user session identification by reference length. 2012 6th International Conference on Application of Information and Communication Technologies, AICT 2012 - Proceedings, art. no. 6398500.
• Skalka, J., Drlik, M., Švec, P. (2012). E-learning courses quality evaluation framework as part of quality assurance in higher education. 15th International Conference on Interactive Collaborative Learning, ICL 2012, art. no. 6402173.
• Kapusta, J., Munk, M., Drlik, M. (2012). E-learning and Quality: The Quality Evaluation Model for E-learning Courses. 9th International Scientific Conference on Distance Learning in Applied Informatics (DIVAI), pp. 279-287.
• Skalka, J., Svec, P., Drlik, M. (2012). User Session Identification Using Reference Length. 9th International Scientific Conference on Distance Learning in Applied Informatics (DIVAI), pp. 175-184.
• Munk, M., Drlik, M., Vrabelova, M. (2011). Probability modelling of accesses to the course activities in the web-based educational system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6786 LNCS (PART 5), pp. 485-499.
• Munk, M., Drlik, M. (2011). Influence of different session timeouts thresholds on results of sequence rule analysis in educational data mining. Communications in Computer and Information Science, 166 CCIS (PART 1), pp. 60-74.
• Hvorecky, J., Drlik, M., Munk, M. (2010). Enhancing database querying skills by choosing a more appropriate interface. 2010 IEEE Education Engineering Conference, EDUCON 2010, art. no. 5492434, pp. 1897-1905.