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Ivar Bråten; Helge I. Strømsø; Ladislao Salmerón – Reading Research Quarterly, 2025
This study addressed an issue relevant to information and research literacy: how students cope with the challenges of reading a one-sided informational text that is combined with ambiguous graphical information. The topic discussed in the text was the impact of digitalization on students' reading comprehension, and 120 university students were…
Descriptors: Graphs, Reading, Ambiguity (Context), College Students
Martin Abt; Katharina Loibl; Timo Leuders; Wim Van Dooren; Frank Reinhold – Educational Studies in Mathematics, 2025
In the boxplot, the box always represents -- regardless of its area -- the middle half of the data and thus a measure of variability (interquartile range). However, when students first learn about boxplots, they are usual already familiar with other forms of statistical representations (e.g., bar or circle graphs) in which a larger area represents…
Descriptors: College Students, Data Analysis, Graphs, Error Patterns
Leonie Disch; Angela Fessl; Simone Franza; Joachim Kimmerle; Viktoria Pammer-Schindler – Discover Education, 2025
Reading and understanding scientific texts is useful and necessary in many informal collaboration settings, including research or innovation collaborations, or when discussing public science in lay peer groups. In this work, we investigate whether an interactive predefined concept map that builds up gradually while reading a scientific research…
Descriptors: Reading Comprehension, Graphs, Concept Mapping, Cooperative Learning
Anca Muresan; Mihaela Cardei; Ionut Cardei – International Educational Data Mining Society, 2025
Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on-time graduation. In educational settings, AI-powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to…
Descriptors: Artificial Intelligence, At Risk Students, Learning Analytics, Technology Uses in Education
Camilo Andrés Rodríguez-Nieto; Flor Monserrat Rodríguez-Vásquez; Vicenç Font Moll; Sudirman Sudirman; Benilda María Cantillo-Rudas – Educational Process: International Journal, 2025
Background/purpose. One of the current problems facing mathematics students, graduates in mathematics, and engineering is the disconnection between the meanings, symbolic representations, and graphics of derivatives when solving problems, which hinders their understanding. This article analyzes engineering students' understanding activated by…
Descriptors: Knowledge Level, Problem Solving, Graphs, Mathematics Instruction
Li Hu; Yulu Chen; Lisi Chen – Education and Information Technologies, 2025
With the continuous development of higher education, the evaluation of college students' learning has received increasing attention. Understanding the impact of the multiple evaluation system on college students' sense of English learning acquisition is of great significance. It helps educators to better design teaching activities and evaluation…
Descriptors: College Students, Student Attitudes, Academic Achievement, Student Evaluation
Cynthia Bansak; Julie K. Smith; Christine L. Storrie – Journal of Economic Education, 2025
The authors of this article describe three student-driven learning modules developed from their respective College Fed Challenge courses. These activities are ready-to-apply in a wide range of courses to facilitate adoption without a large time commitment. In the "Pick a Policy Maker" module, students gain an understanding of the…
Descriptors: Macroeconomics, Economics Education, Teamwork, Graphs
Giulia Polverini; Jakob Melin; Elias Önerud; Bor Gregorcic – Physical Review Physics Education Research, 2025
[This paper is part of the Focused Collection in Artificial Intelligence Tools in Physics Teaching and Physics Education Research.] Artificial intelligence-based chatbots are increasingly influencing physics education because of their ability to interpret and respond to textual and visual inputs. This study evaluates the performance of two large…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Physics

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