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Shin-Yu Kim; Inseong Jeon; Seong-Joo Kang – Journal of Chemical Education, 2024
Artificial intelligence (AI) and data science (DS) are receiving a lot of attention in various fields. In the educational field, the need for education utilizing AI and DS is also being emerged. In this context, we have created an AI/DS integrating program that generates a compound classification/regression model using characteristics of compounds…
Descriptors: Chemistry, Science Instruction, Laboratory Experiments, Artificial Intelligence
Anna Khalemsky; Roy Gelbard; Yelena Stukalin – Journal of Statistics and Data Science Education, 2025
Classification, a fundamental data analytics task, has widespread applications across various academic disciplines, such as marketing, finance, sociology, psychology, education, and public health. Its versatility enables researchers to explore diverse research questions and extract valuable insights from data. Therefore, it is crucial to extend…
Descriptors: Classification, Undergraduate Students, Undergraduate Study, Data Science
Marianthi Grizioti; Chronis Kynigos – Informatics in Education, 2024
Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying…
Descriptors: Computation, Thinking Skills, Data Science, Classification
Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
Bui, Ngoc Van P. – ProQuest LLC, 2022
This research explores the use of eXplainable Artificial Intelligence (XAI) in Educational Data Mining (EDM) to improve the performance and explainability of artificial intelligence (AI) and machine learning (ML) models predicting at-risk students. Explainable predictions provide students and educators with more insight into at-risk indicators and…
Descriptors: Artificial Intelligence, At Risk Students, Prediction, Data Science
P. Janelle McFeetors – Sage Research Methods Cases, 2016
This case study describes an experience of using constructivist grounded theory to analyze data. The project investigated how high school students improved their approaches to learning mathematics. Over 4 months, students participated in processes which supported their learning while simultaneously generating data, including interactive writing,…
Descriptors: High School Students, Mathematics Education, Data Analysis, Data Interpretation