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Martin Drlík
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2020 – today
- 2024
- [j11]Janka Pecuchova, Martin Drlík:
Enhancing the Early Student Dropout Prediction Model Through Clustering Analysis of Students' Digital Traces. IEEE Access 12: 159336-159367 (2024) - 2023
- [j10]Ján Skalka, Martin Drlík:
Development of Automatic Source Code Evaluation Tests Using Grey-Box Methods: A Programming Education Case Study. IEEE Access 11: 106772-106792 (2023) - [c15]Janka Pecuchova, Martin Drlík:
Predicting Students at Risk of Early Dropping Out from Course Using Ensemble Classification Methods. KES 2023: 3223-3232 - 2021
- [j9]Martin Drlík, Michal Munk, Ján Skalka:
Identification of Changes in VLE Stakeholders' Behavior Over Time Using Frequent Patterns Mining. IEEE Access 9: 23795-23813 (2021) - [j8]Jozef Kapusta, Martin Drlík, Michal Munk:
Using of n-grams from morphological tags for fake news classification. PeerJ Comput. Sci. 7: e624 (2021) - 2020
- [c14]Ján Skalka, Martin Drlík, Juraj Obonya, Martin Cápay:
Architecture Proposal for Micro-Learning Application for Learning and Teaching Programming Courses. EDUCON 2020: 980-987
2010 – 2019
- 2019
- [j7]Martin Drlík, Michal Munk:
Understanding Time-Based Trends in Stakeholders' Choice of Learning Activity Type Using Predictive Models. IEEE Access 7: 3106-3121 (2019) - [j6]Jozef Kapusta, Michal Munk, Dominik Halvoník, Martin Drlík:
User Identification in the Process of Web Usage Data Preprocessing. Int. J. Emerg. Technol. Learn. 14(9): 21-33 (2019) - [c13]Ján Skalka, Martin Drlík, Juraj Obonya:
Automated Assessment in Learning and Teaching Programming Languages using Virtual Learning Environment. EDUCON 2019: 689-697 - 2018
- [j5]Jozef Kapusta, Michal Munk, Martin Drlík:
Website Structure Improvement Based on the Combination of Selected Web Structure and Web Usage Mining Methods. Int. J. Inf. Technol. Decis. Mak. 17(6): 1743-1776 (2018) - [c12]Martin Drlík:
Understanding of Time-Based Trends in Virtual Learning Environment Stakeholders' Behaviour. ICL (1) 2018: 321-332 - [c11]Ján Skalka, Martin Drlík:
Educational Model for Improving Programming Skills Based on Conceptual Microlearning Framework. ICL (1) 2018: 923-934 - 2017
- [j4]Michal Munk, Martin Drlík, Lubomír Benko, Jaroslav Reichel:
Quantitative and Qualitative Evaluation of Sequence Patterns Found by Application of Different Educational Data Preprocessing Techniques. IEEE Access 5: 8989-9004 (2017) - [j3]Martin Drlík, Peter Svec, Jozef Kapusta, Michal Munk, Tatiana Noskova, Tatiana Pavlova, Olga Yakovleva, Nataliia Morze, Eugenia Smyrnova-Trybulska:
Identification of differences in university e-environment between selected EU and non-EU countries using knowledge mining methods: project IRNet case study. Int. J. Web Based Communities 13(2): 236-261 (2017) - [c10]Martin Cápay, Ján Skalka, Martin Drlík:
Computer science learning activities based on experience. EDUCON 2017: 1363-1372 - [c9]Ján Skalka, Martin Drlík:
Conceptual Framework of Microlearning-Based Training Mobile Application for Improving Programming Skills. IMCL 2017: 213-224 - 2015
- [j2]Jozef Kapusta, Michal Munk, Martin Drlík:
Identification of Underestimated and Overestimated Web Pages Using PageRank and Web Usage Mining Methods. Trans. Comput. Collect. Intell. 18: 127-146 (2015) - [c8]Jozef Kapusta, Michal Munk, Martin Drlík:
Experimental Verification of the Dependence Between the Expected and Observed Visit Rate of Web Pages. ICIC (3) 2015: 637-648 - 2014
- [c7]Jozef Kapusta, Michal Munk, Martin Drlík:
Analysis of Differences between Expected and Observed Probability of Accesses to Web Pages. ICCCI 2014: 673-683 - 2013
- [c6]Ján Skalka, Martin Drlík, Peter Svec:
Knowledge discovery from university information systems for purposes of quality assurance implementation. EDUCON 2013: 591-596 - [c5]Michal Munk, Anna Pilkova, Jozef Kapusta, Peter Svec, Martin Drlík:
Pillar 3 and Modelling of Stakeholders' Behaviour at the Commercial Bank Website during the Recent Financial Crisis. ICCS 2013: 1747-1756 - 2011
- [c4]Michal Munk, Martin Drlík:
Influence of Different Session Timeouts Thresholds on Results of Sequence Rule Analysis in Educational Data Mining. DICTAP (1) 2011: 60-74 - [c3]Michal Munk, Martin Drlík, Marta Vrábelová:
Probability Modelling of Accesses to the Course Activities in the Web-Based Educational System. ICCSA (5) 2011: 485-499 - [c2]Martin Drlík, Michal Munk, Ján Skalka:
Usage analysis of system for theses acquisition and plagiarism detection. WCIT 2011: 866-871 - [c1]Michal Munk, Martin Drlík:
Impact of Different Pre-Processing Tasks on Effective Identification of Users' Behavioral Patterns in Web-based Educational System. ICCS 2011: 1640-1649
2000 – 2009
- 2007
- [j1]Jozef Hvorecký, Martin Drlík:
From Question to Queries. Int. J. Emerg. Technol. Learn. 2(4) (2007)
Coauthor Index
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