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Paassen, Benjamin; McBroom, Jessica; Jeffries, Bryn; Koprinska, Irena; Yacef, Kalina – Journal of Educational Data Mining, 2021
Educational data mining involves the application of data mining techniques to student activity. However, in the context of computer programming, many data mining techniques can not be applied because they require vector-shaped input, whereas computer programs have the form of syntax trees. In this paper, we present ast2vec, a neural network that…
Descriptors: Data Analysis, Programming Languages, Networks, Novices
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Boxuan Ma; Sora Fukui; Yuji Ando; Shinichi Konomi – Journal of Educational Data Mining, 2024
Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge…
Descriptors: Language Proficiency, Brain Hemisphere Functions, Language Processing, Task Analysis
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Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
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Pelánek, Radek; Effenberger, Tomáš; Kukucka, Adam – Journal of Educational Data Mining, 2022
We study the automatic identification of educational items worthy of content authors' attention. Based on the results of such analysis, content authors can revise and improve the content of learning environments. We provide an overview of item properties relevant to this task, including difficulty and complexity measures, item discrimination, and…
Descriptors: Item Analysis, Identification, Difficulty Level, Case Studies
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Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology