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Seiyon M. Lee; Sami Baral; Hongming Chip Li; Li Cheng; Shan Zhang; Carly S. Thorp; Jennifer St. John; Tamisha Thompson; Neil Heffernan; Anthony F. Botelho – Journal of Educational Data Mining, 2025
Teachers often use open-ended questions to promote students' deeper understanding of the content. These questions are particularly useful in K-12 mathematics education, as they provide richer insights into students' problem-solving processes compared to closed-ended questions. However, they are also challenging to implement in educational…
Descriptors: Feedback (Response), Taxonomy, Data Analysis, Middle School Mathematics
Liu, Ran; Koedinger, Kenneth R. – Journal of Educational Data Mining, 2017
As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis
Rupp, André A.; Nugent, Rebecca; Nelson, Brian – Journal of Educational Data Mining, 2012
In recent years the educational community has increasingly embraced digital technologies for the purposes of developing alternative learning environments, providing diagnostic feedback, and fostering the development of so-called 21st-century skills. This special issue is dedicated to bridging recent work from the disciplines of educational and…
Descriptors: Electronic Learning, Psychometrics, Educational Environment, Educational Technology
A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns
Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam – Journal of Educational Data Mining, 2013
Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…
Descriptors: Data Analysis, Middle School Students, Information Retrieval, Student Behavior

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