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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
Amedeo Pachera; Stefania Dumbrava; Angela Bonifati; Andrea Mauri – ACM Transactions on Computing Education, 2025
Query languages are the foundations of database teaching and education practices. The broad adoption of graph databases contrasts with the limited research into how they are taught. Contrary to relational databases, graph databases allow navigational queries with higher expressivity and lack an a priori schema. In this article, we design a…
Descriptors: Error Patterns, Graphs, Programming Languages, Databases
Wenlong Yi; Xuan Huang; Sergey Kuzmin; Igor Gerasimov; Yun Luo – Education and Information Technologies, 2025
This study proposes a knowledge graph-based big data analysis model for course quality evaluation, aiming to address issues in online education course evaluations such as semantic bias, grammatical deficiencies, vocabulary limitations, false evaluations, information distortion, and imbalanced evaluation categories. The model incorporates three…
Descriptors: Electronic Learning, Online Courses, Course Evaluation, Concept Mapping

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