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Chenguang Pan; Zhou Zhang – International Educational Data Mining Society, 2024
There is less attention on examining algorithmic fairness in secondary education dropout predictions. Also, the inclusion of protected attributes in machine learning models remains a subject of debate. This study delves into the use of machine learning models for predicting high school dropouts, focusing on the role of protected attributes like…
Descriptors: High School Students, Dropouts, Dropout Characteristics, Artificial Intelligence
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Amitabh Verma – Journal of Educators Online, 2025
This study provides a thorough bibliometric analysis of the research landscape concerning the application of soft computing in higher education. This study collects 5,140 pieces including books, book chapters, journal articles published in respected journals, and conference papers presented at notable international conferences that were published…
Descriptors: Bibliometrics, Computer Uses in Education, Computer Science, Higher Education
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Carlson, Patricia A. – Journal of Computing in Higher Education, 1991
Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…
Descriptors: Artificial Intelligence, Associative Learning, Computer Assisted Instruction, Computer Oriented Programs
Lawlor, Joseph – 1984
Artificial intelligence (AI) is the field of scientific inquiry concerned with designing machine systems that can simulate human mental processes. The field draws upon theoretical constructs from a wide variety of disciplines, including mathematics, psychology, linguistics, neurophysiology, computer science, and electronic engineering. Some of the…
Descriptors: Artificial Intelligence, Chemistry, Cognitive Processes, Computer Science
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Sabbah, Daniel – Cognitive Science, 1985
Summarizes an initial foray in tackling artificial intelligence problems using a connectionist approach. The task chosen is visual recognition of Origami objects, and the questions answered are how to construct a connectionist network to represent and recognize projected Origami line drawings and the advantages such an approach would have. (30…
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Graphics, Geometry