activity
20242026
most citedComparison of Large Language Models for Generating Contextually Relevant Questions

11 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CY2026

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…

cs.CY2026

Is Log-Traced Engagement Enough? Extending Reading Analytics With Trait-Level Flow and Reading Strategy Metrics

Erwin Lopez, Atsushi Shimada

Student engagement is a central construct in Learning Analytics, yet it is often operationalized through persistence indicators derived from logs, overlooking affective-cognitive s…

cs.HC2025

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…

cs.CL2024★ 11 cited

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…

cs.CY2024

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…