96 citations · 549 across the 34 of their papers we have counts for
36 papers
Assessment in Team Problem-Solving Exercises in Computing Education
Valdemar Švábenský, Jan Vykopal, Sukrit Leelaluk +3
This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable learner teams to prepare for workplace task…
PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses
Mahiro Ozaki, Li Chen, Shotaro Naganuma +3
This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curricu…
LECTOR: Summarizing E-book Reading Content for Personalized Student Support
Erwin Daniel López Zapata, Cheng Tang, Valdemar Švábenský +2
Educational e-book platforms provide valuable information to teachers and researchers through two main sources: reading activity data and reading content data. While reading activi…
Ranking-Based At-Risk Student Prediction Using Federated Learning and Differential Features
Shunsuke Yoneda, Valdemar Švábenský, Gen Li +2
Digital textbooks are widely used in various educational contexts, such as university courses and online lectures. Such textbooks yield learning log data that have been used in num…
Single-Agent vs. Multi-Agent LLM Strategies for Automated Student Reflection Assessment
Gen Li, Li Chen, Cheng Tang +4
We explore the use of Large Language Models (LLMs) for automated assessment of open-text student reflections and prediction of academic performance. Traditional methods for evaluat…
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance
Sukrit Leelaluk, Cheng Tang, Valdemar Švábenský +1
Educational data mining (EDM) is a part of applied computing that focuses on automatically analyzing data from learning contexts. Early prediction for identifying at-risk students…
Evaluating the Impact of Data Augmentation on Predictive Model Performance
Valdemar Švábenský, Conrad Borchers, Elizabeth B. Cloude +1
In supervised machine learning (SML) research, large training datasets are essential for valid results. However, obtaining primary data in learning analytics (LA) is challenging. D…
Cybersecurity Study Programs: What's in a Name?
Jan Vykopal, Valdemar Švábenský, Michael Tuscano Lopez +1
Improving cybersecurity education has become a priority for many countries and organizations worldwide. Computing societies and professional associations have recognized cybersecur…
Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning Environments
Valdemar Švábenský, Kristián Tkáčik, Aubrey Birdwell +6
This full paper in the research track evaluates the usage of data logged from cybersecurity exercises in order to predict students who are potentially at risk of performing poorly.…
Comparison of Large Language Models for Generating Contextually Relevant Questions
Ivo Lodovico Molina, Valdemar Švábenský, Tsubasa Minematsu +3
This study explores the effectiveness of Large Language Models (LLMs) for Automatic Question Generation in educational settings. Three LLMs are compared in their ability to create…
E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems
Yuma Miyazaki, Valdemar Švábenský, Yuta Taniguchi +3
Digital textbook (e-book) systems record student interactions with textbooks as a sequence of events called EventStream data. In the past, researchers extracted meaningful features…
Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
Valdemar Švábenský, Mélina Verger, Maria Mercedes T. Rodrigo +5
Algorithmic bias is a major issue in machine learning models in educational contexts. However, it has not yet been studied thoroughly in Asian learning contexts, and only limited w…
From Paper to Platform: Evolution of a Novel Learning Environment for Tabletop Exercises
Valdemar Švábenský, Jan Vykopal, Martin Horák +2
For undergraduate students of computing, learning to solve complex practical problems in a team is an essential skill for their future careers. This skill is needed in various fiel…
Research and Practice of Delivering Tabletop Exercises
Jan Vykopal, Pavel Čeleda, Valdemar Švábenský +2
Tabletop exercises are used to train personnel in the efficient mitigation and resolution of incidents. They are applied in practice to support the preparedness of organizations an…
Comparison of Three Programming Error Measures for Explaining Variability in CS1 Grades
Valdemar Švábenský, Maciej Pankiewicz, Jiayi Zhang +3
Programming courses can be challenging for first year university students, especially for those without prior coding experience. Students initially struggle with code syntax, but a…
Towards Generalizable Detection of Urgency of Discussion Forum Posts
Valdemar Švábenský, Ryan S. Baker, Andrés Zambrano +2
Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and…
Want to Raise Cybersecurity Awareness? Start with Future IT Professionals
Lydia Kraus, Valdemar Švábenský, Martin Horák +3
As cyber threats endanger everyone, from regular users to computing professionals, spreading cybersecurity awareness becomes increasingly critical. Therefore, our university design…
Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering
Valdemar Švábenský, Jan Vykopal, Pavel Čeleda +2
Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning envir…
Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
Valdemar Švábenský, Jan Vykopal, Pavel Čeleda +1
Cybersecurity professionals need hands-on training to prepare for managing the current advanced cyber threats. To practice cybersecurity skills, training participants use numerous…
Smart Environment for Adaptive Learning of Cybersecurity Skills
Jan Vykopal, Pavel Seda, Valdemar Švábenský +1
Hands-on computing education requires a realistic learning environment that enables students to gain and deepen their skills. Available learning environments, including virtual and…
Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
Pavel Seda, Jan Vykopal, Valdemar Švábenský +1
This paper presents how learning experience influences students' capability to learn and their motivation for learning. Although each student is different, standard instruction met…
Preventing Cheating in Hands-on Lab Assignments
Jan Vykopal, Valdemar Švábenský, Pavel Seda +1
Networking, operating systems, and cybersecurity skills are exercised best in an authentic environment. Students work with real systems and tools in a lab environment and complete…
Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training
Valdemar Švábenský, Jan Vykopal, Daniel Tovarňák +1
When learning cybersecurity, operating systems, or networking, students perform practical tasks using a broad range of command-line tools. Collecting and analyzing data about the c…
Evaluating Two Approaches to Assessing Student Progress in Cybersecurity Exercises
Valdemar Švábenský, Richard Weiss, Jack Cook +5
Cybersecurity students need to develop practical skills such as using command-line tools. Hands-on exercises are the most direct way to assess these skills, but assessing students'…