Masaryk University

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    2024

    1. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling
      PELÁNEK, Radek. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling. International Journal of Artificial Intelligence in Education. NEW YORK: SPRINGER, 2024, 26 pp. ISSN 1560-4292. Available from: https://dx.doi.org/10.1007/s40593-024-00400-6.
      More: https://is.muni.cz/publication/2396017/en
    2. Catalog of Code Quality Defects in Introductory Programming
      ŘECHTÁČKOVÁ, Anna, Radek PELÁNEK and Tomáš EFFENBERGER. Catalog of Code Quality Defects in Introductory Programming. Online. In Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1. Milan, Italy: Association for Computing Machinery, 2024, p. 59-65. ISBN 979-8-4007-0600-4. Available from: https://dx.doi.org/10.1145/3649217.3653638.
      More: https://is.muni.cz/publication/2423237/en
    3. Leveraging response times in learning environments: opportunities and challenges
      PELÁNEK, Radek. Leveraging response times in learning environments: opportunities and challenges. User Modeling and User-Adapted Interaction. DORDRECHT: Springer Netherlands, 2024, 24 pp. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-023-09386-7.
      More: https://is.muni.cz/publication/2385339/en
    4. Personalized recommendations for learning activities in online environments: a modular rule-based approach
      PELÁNEK, Radek, Tomáš EFFENBERGER and Petr JARUŠEK. Personalized recommendations for learning activities in online environments: a modular rule-based approach. User Modeling and User-Adapted Interaction. DORDRECHT: Springer Netherlands, 2024, 32 pp. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-024-09396-z.
      More: https://is.muni.cz/publication/2395999/en

    2023

    1. The Landscape of Computational Thinking Problems for Practice and Assessment
      PELÁNEK, Radek and Tomáš EFFENBERGER. The Landscape of Computational Thinking Problems for Practice and Assessment. ACM TRANSACTIONS ON COMPUTING EDUCATION. UNITED STATES: ASSOC COMPUTING MACHINERY, 2023, vol. 23, No 2, p. 1-29. ISSN 1946-6226. Available from: https://dx.doi.org/10.1145/3578269.
      More: https://is.muni.cz/publication/2385335/en

    2022

    1. Adaptive, Intelligent, and Personalized: Navigating the Terminological Maze Behind Educational Technology
      PELÁNEK, Radek. Adaptive, Intelligent, and Personalized: Navigating the Terminological Maze Behind Educational Technology. International Journal of Artificial Intelligence in Education. 2022, vol. 32, No 1, p. 151-173. ISSN 1560-4292. Available from: https://dx.doi.org/10.1007/s40593-021-00251-5.
      More: https://is.muni.cz/publication/1848100/en
    2. Code Quality Defects Across Introductory Programming Topics
      EFFENBERGER, Tomáš and Radek PELÁNEK. Code Quality Defects Across Introductory Programming Topics. Online. In Larry Merkle, Maureen Doyle. Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1. New York, NY, USA: Association for Computing Machinery, 2022, p. 941-947. ISBN 978-1-4503-9070-5. Available from: https://dx.doi.org/10.1145/3478431.3499415.
      More: https://is.muni.cz/publication/2227159/en
    3. Complexity and Difficulty of Items in Learning Systems
      PELÁNEK, Radek, Tomáš EFFENBERGER and Jaroslav ČECHÁK. Complexity and Difficulty of Items in Learning Systems. International Journal of Artificial Intelligence in Education. 2022, vol. 32, No 1, p. 196-232. ISSN 1560-4292. Available from: https://dx.doi.org/10.1007/s40593-021-00252-4.
      More: https://is.muni.cz/publication/1783839/en
    4. Design and analysis of microworlds and puzzles for block-based programming
      PELÁNEK, Radek and Tomáš EFFENBERGER. Design and analysis of microworlds and puzzles for block-based programming. Computer Science Education. Routledge, 2022, vol. 32, No 1, p. 66-104. ISSN 0899-3408. Available from: https://dx.doi.org/10.1080/08993408.2020.1832813.
      More: https://is.muni.cz/publication/1686057/en
    5. Improving Learning Environments: Avoiding Stupidity Perspective
      PELÁNEK, Radek and Tomáš EFFENBERGER. Improving Learning Environments: Avoiding Stupidity Perspective. IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES. UNITED STATES: IEEE COMPUTER SOC, 2022, vol. 15, No 1, p. 64-77. ISSN 1939-1382. Available from: https://dx.doi.org/10.1109/TLT.2022.3154936.
      More: https://is.muni.cz/publication/2267081/en
    6. Towards Design-Loop Adaptivity: Identifying Items for Revision
      PELÁNEK, Radek, Tomáš EFFENBERGER and Adam KUKUČKA. Towards Design-Loop Adaptivity: Identifying Items for Revision. Journal of Educational Data Mining. International Educational Data Mining Society, 2022, vol. 14, No 3, p. 1-25. ISSN 2157-2100. Available from: https://dx.doi.org/10.5281/zenodo.7357331.
      More: https://is.muni.cz/publication/2259757/en

    2021

    1. Analyzing and Visualizing Learning Data: A System Designer's Perspective
      PELÁNEK, Radek. Analyzing and Visualizing Learning Data: A System Designer's Perspective. Journal of Learning Analytics. 2021, vol. 8, No 2, p. 93-104. ISSN 1929-7750. Available from: https://dx.doi.org/10.18608/jla.2021.7345.
      More: https://is.muni.cz/publication/1847717/en
    2. Better Model, Worse Predictions: The Dangers in Student Model Comparisons
      ČECHÁK, Jaroslav and Radek PELÁNEK. Better Model, Worse Predictions: The Dangers in Student Model Comparisons. In Roll, Ido and McNamara, Danielle and Sosnovsky, Sergey and Luckin, Rose and Dimitrova, Vania. International Conference on Artificial Intelligence in Education. Cham: Springer, 2021, p. 500-511. ISBN 978-3-030-78291-7. Available from: https://dx.doi.org/10.1007/978-3-030-78292-4_40.
      More: https://is.muni.cz/publication/1780791/en
    3. Experimental Evaluation of Similarity Measures for Educational Items
      ČECHÁK, Jaroslav and Radek PELÁNEK. Experimental Evaluation of Similarity Measures for Educational Items. Online. In I-Han Hsiao, Shaghayegh Sahebi, François Bouchet, Jill-Jênn Vie. Proceedings of the 14th International Conference on Educational Data Mining. Neuveden: Neuveden, 2021, p. 553-558. ISBN 978-1-7336736-2-4.
      More: https://is.muni.cz/publication/1784377/en
    4. Interpretable Clustering of Students’ Solutions in Introductory Programming
      EFFENBERGER, Tomáš and Radek PELÁNEK. Interpretable Clustering of Students’ Solutions in Introductory Programming. Online. In Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748. Cham: Springer, 2021, p. 101-112. ISBN 978-3-030-78291-7. Available from: https://dx.doi.org/10.1007/978-3-030-78292-4_9.
      More: https://is.muni.cz/publication/1783837/en
    5. Validity and Reliability of Student Models for Problem-Solving Activities
      EFFENBERGER, Tomáš and Radek PELÁNEK. Validity and Reliability of Student Models for Problem-Solving Activities. Online. In Proceedings of the 11th International Conference on Learning Analytics and Knowledge. New York, NY, USA: Association for Computing Machinery, 2021, p. 1-11. ISBN 978-1-4503-8935-8. Available from: https://dx.doi.org/10.1145/3448139.3448140.
      More: https://is.muni.cz/publication/1759856/en
    6. Visualization of Student-Item Interaction Matrix
      EFFENBERGER, Tomáš and Radek PELÁNEK. Visualization of Student-Item Interaction Matrix. Online. In Muhittin Sahin, Dirk Ifenthaler. Visualizations and Dashboards for Learning Analytics. Cham: Springer, 2021, p. 439-456. Advances in Analytics for Learning and Teaching. ISBN 978-3-030-81221-8. Available from: https://dx.doi.org/10.1007/978-3-030-81222-5_20.
      More: https://is.muni.cz/publication/1820093/en

    2020

    1. A Classification Framework for Practice Exercises in Adaptive Learning Systems
      PELÁNEK, Radek. A Classification Framework for Practice Exercises in Adaptive Learning Systems. IEEE Transactions on Learning Technologies. 2020, vol. 13, No 4, p. 734-747. ISSN 1939-1382. Available from: https://dx.doi.org/10.1109/TLT.2020.3027050.
      More: https://is.muni.cz/publication/1750938/en
    2. Beyond binary correctness: Classification of students’ answers in learning systems
      PELÁNEK, Radek and Tomáš EFFENBERGER. Beyond binary correctness: Classification of students’ answers in learning systems. User Modeling and User-Adapted Interaction. Springer, 2020, vol. 30, No 5, p. 867-893. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-020-09265-5.
      More: https://is.muni.cz/publication/1685467/en
    3. Exploration of the Robustness and Generalizability of the Additive Factors Model
      EFFENBERGER, Tomáš, Radek PELÁNEK and Jaroslav ČECHÁK. Exploration of the Robustness and Generalizability of the Additive Factors Model. Online. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: Association for Computing Machinery, 2020, p. 472-479. ISBN 978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375491.
      More: https://is.muni.cz/publication/1607941/en
    4. Impact of Methodological Choices on the Evaluation of Student Models
      EFFENBERGER, Tomáš and Radek PELÁNEK. Impact of Methodological Choices on the Evaluation of Student Models. Online. In Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Cham: Springer, 2020, p. 153-164. ISBN 978-3-030-52236-0. Available from: https://dx.doi.org/10.1007/978-3-030-52237-7_13.
      More: https://is.muni.cz/publication/1685465/en
    5. Learning analytics challenges: trade-offs, methodology, scalability
      PELÁNEK, Radek. Learning analytics challenges: trade-offs, methodology, scalability. Online. In Christoph Rensing, Hendrik Drachsler. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. New York, NY, United States: Association for Computing Machinery, 2020, p. 554-558. ISBN 978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375463.
      More: https://is.muni.cz/publication/1750939/en
    6. Managing items and knowledge components: domain modeling in practice
      PELÁNEK, Radek. Managing items and knowledge components: domain modeling in practice. Educational Technology Research and Development. 2020, vol. 68, No 1, p. 529-550. ISSN 1042-1629. Available from: https://dx.doi.org/10.1007/s11423-019-09716-w.
      More: https://is.muni.cz/publication/1645578/en
    7. Measuring Similarity of Educational Items: An Overview
      PELÁNEK, Radek. Measuring Similarity of Educational Items: An Overview. IEEE Transactions on Learning Technologies. 2020, vol. 13, No 2, p. 354-366. ISSN 1939-1382. Available from: https://dx.doi.org/10.1109/TLT.2019.2896086.
      More: https://is.muni.cz/publication/1645576/en

    2019

    1. Difficulty and Complexity of Introductory Programming Problems
      EFFENBERGER, Tomáš, Jaroslav ČECHÁK and Radek PELÁNEK. Difficulty and Complexity of Introductory Programming Problems. Online. In 3rd Educational Data Mining in Computer Science Education Workshop. 2019, 8 pp.
      More: https://is.muni.cz/publication/1550357/en
    2. Item Ordering Biases in Educational Data
      ČECHÁK, Jaroslav and Radek PELÁNEK. Item Ordering Biases in Educational Data. Online. In Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin. International Conference on Artificial Intelligence in Education. Cham: Springer, 2019, p. 48-58. ISBN 978-3-030-23203-0. Available from: https://dx.doi.org/10.1007/978-3-030-23204-7_5.
      More: https://is.muni.cz/publication/1550036/en
    3. Measuring Difficulty of Introductory Programming Tasks
      EFFENBERGER, Tomáš, Jaroslav ČECHÁK and Radek PELÁNEK. Measuring Difficulty of Introductory Programming Tasks. Online. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (L@S '19). New York: ACM, 2019, p. „28:1“-„28:4“, 4 pp. ISBN 978-1-4503-6804-9. Available from: https://dx.doi.org/10.1145/3330430.3333641.
      More: https://is.muni.cz/publication/1550356/en
    4. Measuring Students’ Performance on Programming Tasks
      EFFENBERGER, Tomáš and Radek PELÁNEK. Measuring Students’ Performance on Programming Tasks. Online. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (L@S '19). New York: ACM, 2019, p. "26:1"-"26:4", 4 pp. ISBN 978-1-4503-6804-9. Available from: https://dx.doi.org/10.1145/3330430.3333639.
      More: https://is.muni.cz/publication/1550516/en

    2018

    1. Analysis and design of mastery learning criteria
      PELÁNEK, Radek and Jiří ŘIHÁK. Analysis and design of mastery learning criteria. New Review of Hypermedia and Multimedia. Taylor & Francis, 2018, vol. 24, No 3, p. 133-159. ISSN 1361-4568. Available from: https://dx.doi.org/10.1080/13614568.2018.1476596.
      More: https://is.muni.cz/publication/1448676/en
    2. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge
      PELÁNEK, Radek. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. Online. In Carolyn Penstein Rosé, Roberto Martínez Maldonado, Heinz Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce M. McLaren, Benedict du Boulay. Artificial Intelligence in Education. New York: Springer, 2018, p. 450-461. ISBN 978-3-319-93842-4. Available from: https://dx.doi.org/10.1007/978-3-319-93843-1_33.
      More: https://is.muni.cz/publication/1448696/en
    3. Exploring the Utility of Response Times and Wrong Answers for Adaptive Learning
      PELÁNEK, Radek. Exploring the Utility of Response Times and Wrong Answers for Adaptive Learning. Online. In Rose Luckin, Scott Klemmer, Kenneth R. Koedinger. Learning @ Scale. New York, NY, USA: ACM, 2018, p. "18:1"-"18:4", 4 pp. ISBN 978-1-4503-5886-6. Available from: https://dx.doi.org/10.1145/3231644.3231675.
      More: https://is.muni.cz/publication/1448716/en
    4. Measuring Item Similarity in Introductory Programming
      PELÁNEK, Radek, Tomáš EFFENBERGER, Matěj VANĚK, Vojtěch SASSMANN and Dominik GMITERKO. Measuring Item Similarity in Introductory Programming. Online. In Rose Luckin, Scott Klemmer, Kenneth R. Koedinger. Proceedings of the Fifth Annual ACM Conference on Learning at Scale. New York, NY, USA: ACM, 2018, p. "19:1"-"19:4", 4 pp. ISBN 978-1-4503-5886-6. Available from: https://dx.doi.org/10.1145/3231644.3231676.
      More: https://is.muni.cz/publication/1448736/en
    5. The details matter: methodological nuances in the evaluation of student models
      PELÁNEK, Radek. The details matter: methodological nuances in the evaluation of student models. User Modeling and User-Adapted Interaction. Springer, 2018, vol. 28, No 3, p. 207-235. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-018-9204-y.
      More: https://is.muni.cz/publication/1448657/en
    6. Towards making block-based programming activities adaptive
      EFFENBERGER, Tomáš and Radek PELÁNEK. Towards making block-based programming activities adaptive. Online. In Rose Luckin, Scott Klemmer, Kenneth R. Koedinger. Learning @ Scale. New York, NY, USA: ACM, 2018, p. "13:1"-"13:4", 4 pp. ISBN 978-1-4503-5886-6. Available from: https://dx.doi.org/10.1145/3231644.3231670.
      More: https://is.muni.cz/publication/1449096/en

    2017

    1. Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques
      PELÁNEK, Radek. Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Modeling and User-Adapted Interaction. 2017, vol. 27, 3-5, p. 313-350. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-017-9193-2.
      More: https://is.muni.cz/publication/1404956/en
    2. Elo-based Learner Modeling for the Adaptive Practice of Facts
      PELÁNEK, Radek, Jan PAPOUŠEK, Jiří ŘIHÁK, Vít STANISLAV and Juraj NIŽNAN. Elo-based Learner Modeling for the Adaptive Practice of Facts. User Modeling and User-Adapted Interaction. Springer Netherlands, 2017, vol. 26, No 1, p. 89-118. ISSN 0924-1868. Available from: https://dx.doi.org/10.1007/s11257-016-9185-7.
      More: https://is.muni.cz/publication/1359654/en
    3. Evaluation of Learners' Adjustment of Question Difficulty in Adaptive Practice of Facts
      PAPOUŠEK, Jan and Radek PELÁNEK. Evaluation of Learners' Adjustment of Question Difficulty in Adaptive Practice of Facts. Online. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. New York: ACM, 2017, p. 379-380. ISBN 978-1-4503-4635-1. Available from: https://dx.doi.org/10.1145/3079628.3079642.
      More: https://is.muni.cz/publication/1404953/en
    4. Experimental Analysis of Mastery Learning Criteria
      PELÁNEK, Radek and Jiří ŘIHÁK. Experimental Analysis of Mastery Learning Criteria. Online. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization. New York, NY, USA: ACM, 2017, p. 156-163. ISBN 978-1-4503-4635-1. Available from: https://dx.doi.org/10.1145/3079628.3079667.
      More: https://is.muni.cz/publication/1392193/en
    5. Measuring predictive performance of user models: The details matter
      PELÁNEK, Radek. Measuring predictive performance of user models: The details matter. Online. In Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. USA: ACM, 2017, p. 197-201. ISBN 978-1-4503-5067-9. Available from: https://dx.doi.org/10.1145/3099023.3099042.
      More: https://is.muni.cz/publication/1414250/en
    6. Measuring Similarity of Educational Items Using Data on Learners’ Performance
      ŘIHÁK, Jiří and Radek PELÁNEK. Measuring Similarity of Educational Items Using Data on Learners’ Performance. Online. In Proceedings of the 10th International Conference on Educational Data Mining. Wuhan, China.: International Educational Data Mining Society, 2017, p. 16-23.
      More: https://is.muni.cz/publication/1386577/en
    7. Should We Give Learners Control Over Item Difficulty?
      PAPOUŠEK, Jan and Radek PELÁNEK. Should We Give Learners Control Over Item Difficulty?. Online. In Personalization Approaches in Learning Environments, Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. New York: ACM, 2017, p. 299-303. ISBN 978-1-4503-5067-9. Available from: https://dx.doi.org/10.1145/3099023.3099080.
      More: https://is.muni.cz/publication/1404954/en

    2016

    1. Adaptive Geography Practice Data Set
      PAPOUŠEK, Jan, Radek PELÁNEK and Vít STANISLAV. Adaptive Geography Practice Data Set. Journal of Learning Analytics. 2016, vol. 3, No 2, p. 317-321. ISSN 1929-7750.
      More: https://is.muni.cz/publication/1359698/en
    2. Applications of the Elo rating system in adaptive educational systems
      PELÁNEK, Radek. Applications of the Elo rating system in adaptive educational systems. COMPUTERS & EDUCATION. 2016, vol. 98, No 1, p. 169-179. ISSN 0360-1315. Available from: https://dx.doi.org/10.1016/j.compedu.2016.03.017.
      More: https://is.muni.cz/publication/1376317/en
    3. Evaluation of an Adaptive Practice System for Learning Geography Facts
      PAPOUŠEK, Jan, Vít STANISLAV and Radek PELÁNEK. Evaluation of an Adaptive Practice System for Learning Geography Facts. Online. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge. Edinburgh, United Kingdom: ACM, 2016, p. 134-142. ISBN 978-1-4503-4190-5. Available from: https://dx.doi.org/10.1145/2883851.2883884.
      More: https://is.muni.cz/publication/1350108/en
    4. Evaluation of the Impact of Question Difficulty on Engagement and Learning
      PAPOUŠEK, Jan, Vít STANISLAV and Radek PELÁNEK. Evaluation of the Impact of Question Difficulty on Engagement and Learning. Brno, 2016, 13 pp.
      More: https://is.muni.cz/publication/1350113/en
    5. Choosing a Student Model for a Real World Application
      ŘIHÁK, Jiří and Radek PELÁNEK. Choosing a Student Model for a Real World Application. Online. In Workshop Proceedings of 13th International Conference on Intelligent Tutoring Systems. Zagreb: Intelligent Tutoring Systems, 2016, p. 188-198.
      More: https://is.muni.cz/publication/1351817/en
    6. Impact of Data Collection on Interpretation and Evaluation of Student Models
      PELÁNEK, Radek, Jiří ŘIHÁK and Jan PAPOUŠEK. Impact of Data Collection on Interpretation and Evaluation of Student Models. Online. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge. Edinburgh, United Kingdom: ACM, 2016, p. 40-47. ISBN 978-1-4503-4190-5. Available from: https://dx.doi.org/10.1145/2883851.2883868.
      More: https://is.muni.cz/publication/1350109/en
    7. Impact of Question Difficulty on Engagement and Learning
      PAPOUŠEK, Jan, Vít STANISLAV and Radek PELÁNEK. Impact of Question Difficulty on Engagement and Learning. Online. In Alessandro Micarelli, John Stamper, Kitty Panourgia. Intelligent Tutoring Systems: 13th International Conference. Zagreb, Croatia: Springer International Publishing, 2016, p. 267-272. ISBN 978-3-319-39582-1. Available from: https://dx.doi.org/10.1007/978-3-319-39583-8_28.
      More: https://is.muni.cz/publication/1350112/en
    8. Properties and Applications of Wrong Answers in Online Educational Systems
      PELÁNEK, Radek and Jiří ŘIHÁK. Properties and Applications of Wrong Answers in Online Educational Systems. Online. In Proceedings of the 9th International Conference on Educational Data Mining. Raleigh (USA, NC): International Educational Data Mining Society, 2016, p. 466-471.
      More: https://is.muni.cz/publication/1351814/en
    9. What is More Important for Student Modeling: Domain Structure or Response Times?
      ŘIHÁK, Jiří and Radek PELÁNEK. What is More Important for Student Modeling: Domain Structure or Response Times? In Intelligent Tutoring Systems : 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings. Zagreb: Springer International Publishing, 2016, p. 451-452. ISBN 978-3-319-39582-1. Available from: https://dx.doi.org/10.1007/978-3-319-39583-8.
      More: https://is.muni.cz/publication/1351820/en

    2015

    1. An Analysis of Response Times in Adaptive Practice of Geography Facts
      PAPOUŠEK, Jan, Radek PELÁNEK, Jiří ŘIHÁK and Vít STANISLAV. An Analysis of Response Times in Adaptive Practice of Geography Facts. Online. In Proceedings of the 8th International Conference on Educational Data Mining. Madrid: International Educational Data Mining Society, 2015, p. 562-563. ISBN 978-84-606-9425-0.
      More: https://is.muni.cz/publication/1305107/en
    2. Exploring the Role of Small Differences in Predictive Accuracy using Simulated Data
      NIŽNAN, Juraj, Jan PAPOUŠEK and Radek PELÁNEK. Exploring the Role of Small Differences in Predictive Accuracy using Simulated Data. Online. In Second Workshop on Simulated Learners. Proceedings of the Workshops at the 17th International Conference on Artificial Intelligence in Education. Madrid: Sun SITE Central Europe, 2015, p. 21-30. ISSN 1613-0073.
      More: https://is.muni.cz/publication/1305108/en
    3. Impact of Adaptive Educational System Behaviour on Student Motivation
      PAPOUŠEK, Jan and Radek PELÁNEK. Impact of Adaptive Educational System Behaviour on Student Motivation. In Artificial Intelligence in Education. Madrid: Springer International Publishing, 2015, p. 348-357. ISBN 978-3-319-19772-2. Available from: https://dx.doi.org/10.1007/978-3-319-19773-9_35.
      More: https://is.muni.cz/publication/1301073/en
    4. Metrics for Evaluation of Student Models
      PELÁNEK, Radek. Metrics for Evaluation of Student Models. Journal of Educational Data Mining. 2015, vol. 7, No 3, p. 1-19. ISSN 2157-2100.
      More: https://is.muni.cz/publication/1338743/en
    5. Modeling Students' Memory for Application in Adaptive Educational Systems
      PELÁNEK, Radek. Modeling Students' Memory for Application in Adaptive Educational Systems. Online. In Proceedings of the 8th International Conference on Educational Data Mining. Madrid: International Educational Data Mining Society, 2015, p. 480-483. ISBN 978-84-606-9425-0.
      More: https://is.muni.cz/publication/1341171/en
    6. Student Modeling Based on Problem Solving Times
      PELÁNEK, Radek and Petr JARUŠEK. Student Modeling Based on Problem Solving Times. International Journal of Artificial Intelligence in Education. 2015, vol. 25, No 4, p. 493-519. ISSN 1560-4292. Available from: https://dx.doi.org/10.1007/s40593-015-0048-x.
      More: https://is.muni.cz/publication/1338740/en
    7. Student Models for Prior Knowledge Estimation
      ŘIHÁK, Jiří, Radek PELÁNEK and Juraj NIŽNAN. Student Models for Prior Knowledge Estimation. Online. In Proceedings of the 8th International Conference on Educational Data Mining. Madrid: International Educational Data Mining Society, 2015, p. 109-116. ISBN 978-84-606-9425-0.
      More: https://is.muni.cz/publication/1316789/en

    2014

    1. A Brief Overview of Metrics for Evaluation of Student Models
      PELÁNEK, Radek. A Brief Overview of Metrics for Evaluation of Student Models. Online. In Sergio Gutierrez-Santos, Olga C. Santos. Proceedings of the Workshops held at Educational Data Mining 2014. Velká Británie: RWTH Aachen University, 2014, p. 151-152. ISSN 1613-0073.
      More: https://is.muni.cz/publication/1232094/en
    2. Adaptive Practice of Facts in Domains with Varied Prior Knowledge
      PELÁNEK, Radek, Jan PAPOUŠEK and Vít STANISLAV. Adaptive Practice of Facts in Domains with Varied Prior Knowledge. Online. In John Stamper, Zachary Pardos, Manolis Mavrikis, Bruce M. McLaren. Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014). London, United Kingdom: International Educational Data Mining Society, 2014, p. 6-13. ISBN 978-0-9839525-4-1.
      More: https://is.muni.cz/publication/1199904/en
    3. Application of Time Decay Functions and the Elo System in Student Modeling
      PELÁNEK, Radek. Application of Time Decay Functions and the Elo System in Student Modeling. Online. In John Stamper, Zachary Pardos, Manolis Mavrikis, Bruce M. McLaren. Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014). 2014th ed. London, United Kingdom: International Educational Data Mining Society, 2014, p. 21-27. ISBN 978-0-9839525-4-1.
      More: https://is.muni.cz/publication/1232091/en
    4. Mapping Problems to Skills Combining Expert Opinion and Student Data
      NIŽNAN, Juraj, Radek PELÁNEK and Jiří ŘIHÁK. Mapping Problems to Skills Combining Expert Opinion and Student Data. In P. Hlineny et al. Proceedings of MEMICS'14. Německo: Springer, 2014, p. 113-124. ISBN 978-3-319-14895-3. Available from: https://dx.doi.org/10.1007/978-3-319-14896-0_10.
      More: https://is.muni.cz/publication/1205910/en
    5. Using Problem Solving Times and Expert Opinion to Detect Skills
      NIŽNAN, Juraj, Radek PELÁNEK and Jiří ŘIHÁK. Using Problem Solving Times and Expert Opinion to Detect Skills. Online. In John Stamper, Zachary Pardos, Manolis Mavrikis, Bruce M. McLaren. Proceedings of the 7th International Conference on Educational Data Mining. London: International Educational Data Mining Society, 2014, p. 433-434. ISBN 978-0-9839525-4-1.
      More: https://is.muni.cz/publication/1205735/en

    2013

    1. Automatic Detection of Concepts from Problem Solving Times
      PELÁNEK, Radek, Petr BOROŠ, Juraj NIŽNAN and Jiří ŘIHÁK. Automatic Detection of Concepts from Problem Solving Times. In Lane, H.C.; Yacef, K.; Mostow, J.; Pavlik, P. Artificial Intelligence in Education. USA: Springer, 2013, p. 595-598. ISBN 978-3-642-39111-8. Available from: https://dx.doi.org/10.1007/978-3-642-39112-5_67.
      More: https://is.muni.cz/publication/1129497/en
    2. Modeling Students' Learning and Variability of Performance in Problem Solving
      PELÁNEK, Radek, Petr JARUŠEK and Matěj KLUSÁČEK. Modeling Students' Learning and Variability of Performance in Problem Solving. Online. In D’Mello, S. K., Calvo, R. A., and Olney, A. Educational Data Mining. USA: International Educational Data Mining Society, 2013, p. 256-259. ISBN 978-0-9839525-2-7.
      More: https://is.muni.cz/publication/1129493/en

    2012

    1. A Web-Based Problem Solving Tool for Introductory Computer Science
      PELÁNEK, Radek and Petr JARUŠEK. A Web-Based Problem Solving Tool for Introductory Computer Science. In Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education. New York. USA: ACM, 2012, p. 371-371. ISBN 978-1-4503-1246-2.
      More: https://is.muni.cz/publication/1076251/en
    2. Analysis of a simple model of problem solving times
      PELÁNEK, Radek and Petr JARUŠEK. Analysis of a simple model of problem solving times. In Springer. Proceeding ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems. Heidelberg: Springer Berlin / Heidelberg, 2012, p. 379-388. ISBN 978-3-642-30949-6. Available from: https://dx.doi.org/10.1007/978-3-642-30950-2_49.
      More: https://is.muni.cz/publication/1068895/en
    3. Modeling and Predicting Students Problem Solving Times
      PELÁNEK, Radek and Petr JARUŠEK. Modeling and Predicting Students Problem Solving Times. In Proceedings of the 38th International Conference on Current Trends in Theory and Practice of Computer Science. Czech republic: Springer, 2012, p. 637-648. ISBN 978-3-642-27659-0. Available from: https://dx.doi.org/10.1007/978-3-642-27660-6_52.
      More: https://is.muni.cz/publication/978265/en
    4. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems)
      PELÁNEK, Radek. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems). 1st ed. Brno: Masarykova univerzita, 2012, 236 pp. e-book. ISBN 978-80-210-5807-1.
      More: https://is.muni.cz/publication/977165/en
    5. Programátorská cvičebnice: algoritmy v příkladech
      PELÁNEK, Radek. Programátorská cvičebnice: algoritmy v příkladech. Brno: Computer Press, 2012, 175 pp. ISBN 978-80-251-3751-2.
      More: https://is.muni.cz/publication/1094541/en

    2011

    1. Difficulty Rating of Sudoku Puzzles by a Computational Model
      PELÁNEK, Radek. Difficulty Rating of Sudoku Puzzles by a Computational Model. In Philip M. McCarthy, R. Charles Murray. Twenty-Fourth International Florida Artificial Intelligence Research Society Conference. USA: Association for the Advancement of Artificial Intelligence (AAAI), 2011, p. 434-439. ISBN 978-1-57735-501-4.
      More: https://is.muni.cz/publication/941345/en
    2. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems)
      PELÁNEK, Radek. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems). 1st ed. Brno: Masarykova univerzita, 2011, 236 pp. mimo edice. ISBN 978-80-210-5318-2.
      More: https://is.muni.cz/publication/926402/en
    3. Problem Response Theory and its Application for Tutoring
      PELÁNEK, Radek and Petr JARUŠEK. Problem Response Theory and its Application for Tutoring. In Proceedings of the 4th International Conference on Educational Data Mining. Eindhoven: International Conference on Educational Data Mining 2011, 2011, p. 374-375. ISBN 978-90-386-2537-9.
      More: https://is.muni.cz/publication/950095/en
    4. Problem Solving Tutor (software)
      JARUŠEK, Petr and Radek PELÁNEK. Problem Solving Tutor. 2011.
      More: https://is.muni.cz/publication/964390/en
    5. What Determines Difficulty of Transport Puzzles?
      PELÁNEK, Radek and Petr JARUŠEK. What Determines Difficulty of Transport Puzzles? In Philip M. McCarthy, R. Charles Murray. Twenty-Fourth International Florida Artificial Intelligence Research Society Conference. USA: Association for the Advancement of Artificial Intelligence (AAAI), 2011, p. 428-433. ISBN 978-1-57735-501-4.
      More: https://is.muni.cz/publication/941424/en

    2010

    1. Analýza obtížnosti logických úloh na základě modelů lidského chování (Application of computational models for difficulty rating of logic puzzles)
      JARUŠEK, Petr and Radek PELÁNEK. Analýza obtížnosti logických úloh na základě modelů lidského chování (Application of computational models for difficulty rating of logic puzzles). In Kognice a umělý život X. Neuveden: Slezská univerzita v Opavě, 2010, p. 171-176. ISBN 978-80-7248-589-5.
      More: https://is.muni.cz/publication/885248/en
    2. Difficulty Rating of Sokoban Puzzle
      JARUŠEK, Petr and Radek PELÁNEK. Difficulty Rating of Sokoban Puzzle. In STAIRS 2010, Proceedings of the Fifth Starting AI Researchers' Symposium. Lisbon, Portugal: IOS Press BV, 2010, p. 140-146, 6 pp. ISBN 978-1-60750-675-1.
      More: https://is.muni.cz/publication/890650/en

    2009

    1. EMMA: Explicit Model Checking Manager (Tool Presentation)
      PELÁNEK, Radek and Václav ROSECKÝ. EMMA: Explicit Model Checking Manager (Tool Presentation). In Model Checking Software. Německo: Springer, 2009, p. 169-173. ISBN 978-3-642-02651-5. Available from: https://dx.doi.org/10.1007/978-3-642-02652-2_15.
      More: https://is.muni.cz/publication/854482/en

    2008

    1. Complementarity of Error Detection Techniques
      PELÁNEK, Radek, Václav ROSECKÝ and Pavel MORAVEC. Complementarity of Error Detection Techniques. In Parallel and Distributed Methods in verifiCation (PDMC 2008). Nizozemsko: Elsevier, 2008, 14 pp. ISSN 1571-0661.
      More: https://is.muni.cz/publication/792712/en
    2. Estimating State Space Parameters
      ŠIMEČEK, Pavel and Radek PELÁNEK. Estimating State Space Parameters. In 7th International Workshop on Parallel and Distributed Methods in verifiCation. 2008.
      More: https://is.muni.cz/publication/795912/en
    3. Fighting State Space Explosion: Review and Evaluation
      PELÁNEK, Radek. Fighting State Space Explosion: Review and Evaluation. In Formal Methods for Industrial Critical Systems. Německo: Springer, 2008, 15 pp. ISBN 3-642-03239-7.
      More: https://is.muni.cz/publication/792699/en
    4. Model Classifications and Automated Verification
      PELÁNEK, Radek. Model Classifications and Automated Verification. In Formal Methods for Industrial Critical Systems. Německo: Springer, 2008, p. 149-163. ISBN 978-3-540-79706-7.
      More: https://is.muni.cz/publication/727677/en
    5. Properties of State Spaces and Their Applications
      PELÁNEK, Radek. Properties of State Spaces and Their Applications. International Journal on Software Tools for Technology Transfer (STTT). Springer-Verlag GmbH, 2008, vol. 10, No 5, p. 443-454. ISSN 1433-2779.
      More: https://is.muni.cz/publication/792695/en

    2007

    1. BEEM: Benchmarks for Explicit Model Checkers
      PELÁNEK, Radek. BEEM: Benchmarks for Explicit Model Checkers. In Model Checking Software. Německo: Springer, 2007, p. 263-267. ISBN 3-540-73369-8.
      More: https://is.muni.cz/publication/727675/en
    2. Predicate Abstraction with Under-Approximation Refinement
      PELÁNEK, Radek, Corina PASAREANU and Willem VISSER. Predicate Abstraction with Under-Approximation Refinement. Logical Methods in Computer Science. Germany: Technical University of Braunschweig, 2007, vol. 3, No 1, p. 1-22. ISSN 1860-5974.
      More: https://is.muni.cz/publication/713628/en

    2006

    1. Lower and Upper Bounds in Zone-Based Abstractions of Timed Automata
      PELÁNEK, Radek, Kim G. LARSEN, Gerd BEHRMANN and Patricia BOUYER. Lower and Upper Bounds in Zone-Based Abstractions of Timed Automata. International Journal on Software Tools for Technology Transfer (STTT). Springer-Verlag GmbH, 2006, vol. 8, No 3, p. 204-215. ISSN 1433-2779.
      More: https://is.muni.cz/publication/585922/en
    2. Reduction and Abstraction Techniques for Model Checking
      PELÁNEK, Radek. Reduction and Abstraction Techniques for Model Checking. Brno: Masarykova univerzita, 2006, 160 pp.
      More: https://is.muni.cz/publication/707475/en
    3. Test Input Generation for Java Containers using State Matching
      PELÁNEK, Radek, Corina PASAREANU and Willem VISSER. Test Input Generation for Java Containers using State Matching. In International Symposium on International Symposium on Software Testing and Analysis. USA: ACM, 2006, p. 37--48, 11 pp. ISBN 1-59593-263-1.
      More: https://is.muni.cz/publication/692604/en

    2005

    1. Concrete Search with Abstract Matching and Refinement
      PELÁNEK, Radek, Corina PASAREANU and Willem VISSER. Concrete Search with Abstract Matching and Refinement. In Computer Aided Verification. Edinburgh: Springer, 2005, p. 52-66. ISBN 3-540-27231-3.
      More: https://is.muni.cz/publication/585982/en
    2. Deeper Connections between LTL and Alternating Automata
      PELÁNEK, Radek and Jan STREJČEK. Deeper Connections between LTL and Alternating Automata. In Implementation and Application of Automata. Berlin, Heidelberg: Springer-Verlag, 2005, p. 238-249. ISBN 978-3-540-31023-5.
      More: https://is.muni.cz/publication/586001/en
    3. Enhancing Random Walk State Space Exploration
      PELÁNEK, Radek, Tomáš HANŽL, Ivana ČERNÁ and Luboš BRIM. Enhancing Random Walk State Space Exploration. In Formal Methods for Industrial Critical Systems. Lisbon: ACM SIGSOFT, 2005, p. 98-105. ISBN 1-59593-148-1.
      More: https://is.muni.cz/publication/585981/en
    4. On Sampled Semantics of Timed Systems
      PELÁNEK, Radek and Pavel KRČÁL. On Sampled Semantics of Timed Systems. In Foundations of Software Technology and Theoretical Computer Science. India: Springer, 2005, p. 310-321. ISBN 978-3-540-30495-1.
      More: https://is.muni.cz/publication/585942/en
    5. Test input generation for red-black trees using abstraction
      PELÁNEK, Radek, Corina PASAREANU and Willem VISSER. Test input generation for red-black trees using abstraction. In Automated Software Engineering. USA: ACM, 2005, p. 414-417.
      More: https://is.muni.cz/publication/585961/en

    2004

    1. Lower and Upper Bounds in Zone Based Abstractions of Timed Automata
      PELÁNEK, Radek, Kim G. LARSEN, Gerd BEHRMANN and Patricia BOUYER. Lower and Upper Bounds in Zone Based Abstractions of Timed Automata. In Tools and Algorithms for Construction and Analysis of Systems (TACAS 2004). Barcelona (Španělsko): Springer-Verlag, 2004, p. 312-326. ISBN 3-540-21299-X.
      More: https://is.muni.cz/publication/562991/en
    2. Typical Structural Properties of State Spaces
      PELÁNEK, Radek. Typical Structural Properties of State Spaces. In SPIN Workshop 2004. Barcelona (Španělsko): Springer-Verlag, 2004, p. 5-22, 15 pp. ISBN 3-540-21314-7.
      More: https://is.muni.cz/publication/562993/en

    2003

    1. Distributed Explicit Fair Cycle Detection
      ČERNÁ, Ivana and Radek PELÁNEK. Distributed Explicit Fair Cycle Detection. In SPIN Workshop 2003. Portland (Oregon, USA): Springer-Verlag, 2003, p. 49-74, 25 pp. ISBN 3-540-40117-2.
      More: https://is.muni.cz/publication/489731/en
    2. LTL Hierarchies and Model Checking
      PELÁNEK, Radek. LTL Hierarchies and Model Checking. In Proceedings of the Eight ESSLLI Student Session. Wien: TU Wien, 2003, p. 245-254, 9 pp.
      More: https://is.muni.cz/publication/489822/en
    3. Relating Hierarchy of Temporal Properties to Model Checking
      ČERNÁ, Ivana and Radek PELÁNEK. Relating Hierarchy of Temporal Properties to Model Checking. In Mathematical Foundations of Computer Science (MFCS 2003). Bratislava (Slovensko): Springer-Verlag, 2003, p. 318-327. ISBN 3-540-40671-9.
      More: https://is.muni.cz/publication/489732/en
    4. To Store or Not To Store
      BEHRMANN, Gerd, Kim G. LARSEN and Radek PELÁNEK. To Store or Not To Store. In Computer Aided Verification (CAV 2003). Boulder (Colorado, USA): Springer-Verlag, 2003, p. 433-445. ISBN 3-540-40524-0.
      More: https://is.muni.cz/publication/489730/en

    2001

    1. Distributed LTL Model-Checking Based on Negative Cycle Detection
      BRIM, Luboš, Ivana ČERNÁ, Pavel KRČÁL and Radek PELÁNEK. Distributed LTL Model-Checking Based on Negative Cycle Detection. In FST-TCS 2001. Bangalore, India: Springer, 2001, p. 96-110. LNCS 2245. ISBN 3-540-43002-4.
      More: https://is.muni.cz/publication/371119/en
    2. How to Employ Reverse Search in Distributed Single-Source Shortest Paths (Distributed LTL Model-Checking Based on Negative Cycle Detection)
      BRIM, Luboš, Ivana ČERNÁ, Pavel KRČÁL and Radek PELÁNEK. How to Employ Reverse Search in Distributed Single-Source Shortest Paths (Distributed LTL Model-Checking Based on Negative Cycle Detection). How to Employ Reverse Search in Distributed Single-Source Shortest Paths. In SOFSEM 2001. Piestany: Springer, 2001, p. 191-200. LNCS 2234. ISBN 3-540-42912-3.
      More: https://is.muni.cz/publication/371120/en
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