Publications

Evaluation, Methodology, Terminology

• R. Pelánek. Adaptive Learning is Hard: Challenges, Nuances, and Trade-offs in Modeling International Journal of Artificial Intelligence in Education, 2024. open access Journal
• R. Pelánek. Leveraging response times in learning environments: opportunities and challenges. User Modeling and User-Adapted Interaction, 2023. open access Journal
• R. Pelánek, T. Effenberger, A. Kukučka. Towards Design-Loop Adaptivity: Identifying Items for Revision. Journal of Educational Data Mining, 2022. Journal
• R. Pelánek, T. Effenberger. Improving Learning Environments: Avoiding Stupidity Perspective. IEEE Transactions on Learning Technologies, 2022. preprint version Journal
• T. Effenberger, R. Pelánek. Visualization of Student-Item Interaction Matrix, chapter in Visualizations and Dashboards for Learning Analytics, 2021. preprint version Book chapter
• R. Pelánek. Adaptive, Intelligent, and Personalized: Navigating the Terminological Maze Behind Educational Technology. International Journal of Artificial Intelligence in Education, 2022. preprint version Journal
• R. Pelánek, T. Effenberger, J. Čechák. Complexity and Difficulty of Items in Learning Systems. International Journal of Artificial Intelligence in Education, 2022. preprint version Journal
• R. Pelánek. Analyzing and Visualizing Learning Data: A System Designer's Perspective, Journal of Learning Analytics, 2021. Journal
• J. Čechák, R. Pelánek. Better Model, Worse Predictions: The Dangers in Student Model Comparisons. Artificial Intelligence in Education (AIED), 2021. Long AIED paper
• T. Effenberger, R. Pelánek. Validity and Reliability of Student Models for Problem-Solving Activities.Learning Analytics & Knowledge (LAK), 2021. Long LAK paper Best paper nomination
• T. Effenberger, R. Pelánek. Impact of Methodological Choices on the Evaluation of Student Models. Artificial Intelligence in Education (AIED), 2020. Long AIED paper
• R. Pelánek. Learning Analytics Challenges: Trade-offs, Methodology, Scalability. Learning Analytics & Knowledge (LAK), 2020.
• R. Pelánek. Managing Items and Knowledge Components: Domain Modeling in Practice. Educational Technology Research and Development. preprint version Journal
• J. Čechák, R. Pelánek. Item Ordering Biases in Educational Data Artificial Intelligence in Education (AIED), 2019.Long AIED paper
• R. Pelánek. The details matter: methodological nuances in the evaluation of student models. User Modeling and User-Adapted Interaction, 2018. preprint version Journal
• R. Pelánek. Metrics for Evaluation of Student Models. Journal of Educational Data Mining, 2015. Journal
• R. Pelánek, J. Řihák, J. Papoušek. Impact of Data Collection on Interpretation and Evaluation of Student Models. Learning Analytics & Knowledge, 2016. Long LAK paper Best paper nomination
• R. Pelánek. Measuring Predictive Performance of User Models: The Details Matter. EvalUMAP: Towards Comparative Evaluation in User Modeling, Adaptation and Personalization, 2017

Introductory Programming, Computational Thinking

• R. Pelánek, T. Effenberger. The Landscape of Computational Thinking Problems for Practice and Assessment. ACM Transactions on Computing Education, 2023. preprint version Journal
• T. Effenberger, R. Pelánek. Code Quality Defects across Introductory Programming Topics. Technical Symposium on Computer Science Education, 2022. Long SIGCSE paper
• T. Effenberger, R. Pelánek. Interpretable Clustering of Students' Solutions in Introductory Programming. Artificial Intelligence in Education (AIED), 2021. Long AIED paper
• R. Pelánek, T. Effenberger. Design and Analysis of Microworlds and Puzzles for Block-Based Programming. Computer Science Education, 2020. preprint version Journal
• T. Effenberger, J. Čechák, R. Pelánek. Difficulty and Complexity of Introductory Programming Problems. Educational Data Mining in Computer Science Education (CSEDM), 2019
• T. Effenberger, J. Čechák, R. Pelánek. Measuring Difficulty of Introductory Programming Tasks. Learning@Scale, 2019
• T. Effenberger, R. Pelánek. Measuring Students’ Performance on Programming Tasks. Learning@Scale, 2019

Adaptive Practice

• R. Pelánek, Tomáš Effenberger, Petr Jarušek. Personalized recommendations for learning activities in online environments: a modular rule-based approach. User Modeling and User-Adapted Interaction, 2024. open access Journal
• R. Pelánek. A Classification Framework for Practice Exercises in Adaptive Learning Systems. IEEE Transactions on Learning Technologies, 2020. preprint version Journal
• R. Pelánek, J. Řihák. Analysis and Design of Mastery Learning Criteria. New Review of Hypermedia and Multimedia, 2018. Preprint version. Journal
• R. Pelánek, J. Papoušek, J. Řihák, V. Stanislav, J. Nižnan. Elo-based Learner Modeling for the Adaptive Practice of Facts. User Modeling and User-Adapted Interaction, 2017, preprint version. Journal
• J. Papoušek, R. Pelánek, V. Stanislav. Adaptive Geography Practice Data Set. Journal of Learning Analytics, 2016.
• R. Pelánek. Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. Artificial Intelligence in Education, 2018. Long AIED paper
• R. Pelánek, J. Řihák. Experimental Analysis of Mastery Learning Criteria. User Modelling, Adaptation and Personalization, 2017. Long UMAP paper Best paper award
• J. Papoušek, R. Pelánek. Evaluation of Learners' Adjustment of Question Difficulty in Adaptive Practice of Facts. User Modelling, Adaptation and Personalization, 2017.
• J. Papoušek, R. Pelánek. Should We Give Learners Control Over Item Difficulty?. Personalization Approaches in Learning Environments, 2017.
• J. Papoušek, V. Stanislav, R. Pelánek. Impact of Question Difficulty on Engagement and Learning. Intelligent Tutoring Systems, 2016.
• J. Papoušek, V. Stanislav, R. Pelánek. Evaluation of the Impact of Question Difficulty on Engagement and Learning. Technical report FIMU-RS-2016-02, 2016.
• J. Papoušek, V. Stanislav, R. Pelánek. Evaluation of an Adaptive Practice System for Learning Geography Facts. Learning Analytics & Knowledge, 2016. Long LAK paper
• J. Papoušek, R. Pelánek. Impact of Adaptive Educational System Behaviour on Student Motivation. Artificial Intelligence in Education, 2015. Long AIED paper Best student paper
• J. Papoušek, R. Pelánek, V. Stanislav. Adaptive Practice of Facts in Domains with Varied Prior Knowledge. Educational Data Mining, 2014. Long EDM paper

Student and Domain Modeling

• J. Čechák, R. Pelánek. Experimental Evaluation of Similarity Measures for Educational Items, Educational Data Mining (EDM), 2021.
• R. Pelánek, T. Effenberger. Beyond binary correctness: Classification of students’ answers in learning systems. User Modeling and User-Adapted Interaction, 2020. preprint version Journal
• T. Effenberger, J. Čechák, R. Pelánek. Exploration of the Robustness and Generalizability of the Additive Factors Model. Learning Analytics & Knowledge, 2020.Long LAK paper
• R. Pelánek. Measuring Similarity of Educational Items: An Overview. IEEE Transactions on Learning Technologies, 2019, preprint version. Journal
  
• R. Pelánek. Bayesian Knowledge Tracing, Logistic Models, and Beyond: An Overview of Learner Modeling Techniques. User Modeling and User-Adapted Interaction, 2017, preprint version. Journal
• R. Pelánek. Applications of the Elo Rating System in Adaptive Educational Systems. Computers & Education, 2016. Journal
• R. Pelánek, T. Effenberger, M. Vaněk, V. Sassmann, D. Gmiterko. Measuring Item Similarity in Introductory Programming, Learning@Scale, 2018; full version
• R. PelánekExploring the Utility of Response Times and Wrong Answers for Adaptive Learning. Learning@Scale, 2018.
• T. Effenberger, R. Pelánek. Towards Making Block-based Programming Activities Adaptive. Learning@Scale, 2018.
• J. Řihák, R. Pelánek. Measuring Similarity of Educational Items Using Data on Learners' Performance. Educational Data Mining, 2017. Long EDM paper
• R. Pelánek, J. Řihák. Properties and Applications of Wrong Answers in Online Educational Systems. Educational Data Mining, 2016.
• J. Řihák, R. Pelánek. What is More Important for Student Modeling: Domain Structure or Response Times? Intelligent Tutoring Systems, 2016.
• J. Řihák, R. Pelánek. Choosing a Student Model for a Real World Application. Building ITS Bridges Across Frontiers (ITS Workshop), 2016.
• J. Nižnan, R. Pelánek, J. Řihák. Student Models for Prior Knowledge Estimation. Educational Data Mining, 2015. Long EDM paper
• R. Pelánek. Modeling Students' Memory for Application in Adaptive Educational Systems. Educational Data Mining, 2015.
• R. Pelánek. Modeling Student Learning: Binary or Continuous Skill?. Educational Data Mining, 2015.
• J. Nižnan, J. Papoušek, R. Pelánek. Exploring the Role of Small Differences in Predictive Accuracy using Simulated Data. AIED Workshop on Simulated Learners, 2015.
• J. Papoušek, R. Pelánek, J. Řihák, V. Stanislav. An Analysis of Response Times in Adaptive Practice of Geography Facts. Educational Data Mining, 2015.
• J. Nižnan. Modeling Speed-Accuracy Tradeoff in Adaptive System for Practicing Estimation. Educational Data Mining, Doctoral Consortium, 2015.
• J. Řihák. Use of Time Information in Models behind Adaptive System for Building Fluency in Mathematics. Educational Data Mining, Doctoral Consortium, 2015.
• R. Pelánek. Application of Time Decay Functions and the Elo System in Student Modeling. Educational Data Mining, 2014. Long EDM paper
• R. Pelánek. A Brief Overview of Metrics for Evaluation of Student Models. BKT20y Workshop - Approaching Twenty Years of Knowledge Tracing, 2014.

Problem Solving Times

• R. Pelánek, P. Jarušek. Student Modeling Based on Problem Solving Times. International Journal of Artificial Intelligence in Education, 2015. Journal
• J. Nižnan, R. Pelánek, J. Řihák. Mapping Problems to Skills Combining Expert Opinion and Student Data. Mathematical and Engineering Methods in Computer Science, 2014.
• J. Nižnan, R. Pelánek, J. Řihák. Using Problem Solving Times and Expert Opinion to Detect Skills. Educational Data Mining, 2014.
• P. Jarušek, M. Klusáček, R. Pelánek. Modeling Students' Learning and Variability of Performance in Problem Solving. Educational Data Mining, 2013.
• P. Boroš, J. Nižnan, R. Pelánek, J. Řihák. Automatic Detection of Concepts from Problem Solving Times. Artificial Intelligence in Education, 2013.
• P. Jarušek, R. Pelánek. Analysis of a Simple Model of Problem Solving Times. Intelligent Tutoring Systems, 2012. Long ITS paper
• P. Jarušek, R. Pelánek. A Web-Based Problem Solving Tool for Introductory Computer Science. Innovation and technology in computer science education, 2012.
• P. Jarušek, R. Pelánek. Modeling and Predicting Students Problem Solving Times. Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM), 2012.
• P. Jarušek, R. Pelánek. Problem Response Theory and its Application for Tutoring. Educational Data Mining, 2011.

Difficulty of Logic Puzzles

• R. Pelánek. Difficulty Rating of Sudoku Puzzles: An Overview and Evaluation, arXiv:1403.7373, 2014.
• R. Pelánek. Difficulty rating of Sudoku Puzzles by a Computational Model . Florida Artificial Intelligence Research Society Conference, 2011.
• P. Jarušek, R. Pelánek. What Determines Difficulty of Transport Puzzles? Florida Artificial Intelligence Research Society Conference, 2011.
• P. Jarušek, R. Pelánek. Difficulty Rating of Sokoban Puzzle. European Starting AI Researcher Symposium, 2010.
• P. Jarušek, R. Pelánek. Analýza obtížnosti logických úloh na základě modelů lidského chování. Kognice a umělý život, 2010.
• R. Pelánek. Human Problem Solving: Sudoku Case Study. Technical report FIMU-RS-2011-01, 2011.
• P. Jarušek, R. Pelánek. Human Problem Solving: Sokoban Case Study. Technical report FIMU-RS-2010-01, 2010.

PhD Theses

• J. Papoušek. Computerized Adaptive Practise of Factual Knowledge, 2017.
• J. Řihák. Modeling Techniques for Adaptive Practice Systems, 2017.
• P. Jarušek. Modeling Problem Solving Times in Tutoring Systems, 2013.