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Kylie Anglin – AERA Open, 2024
Given the rapid adoption of machine learning methods by education researchers, and the growing acknowledgment of their inherent risks, there is an urgent need for tailored methodological guidance on how to improve and evaluate the validity of inferences drawn from these methods. Drawing on an integrative literature review and extending a…
Descriptors: Validity, Artificial Intelligence, Models, Best Practices
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Yoonjae Noh; YoonIl Yoon; Sangjin Kim – Measurement: Interdisciplinary Research and Perspectives, 2024
The default risk, one of the main risk factors for bonds, should be measured and reflected in the bond yield. Particularly, in the case of financial companies that treat bonds as a major product, failure to properly identify and filter customers' workout status adversely affects returns. This study proposes a two-stage classification algorithm for…
Descriptors: Prediction, Classification, Accuracy, Risk
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Andrew Swindell; Luke Greeley; Antony Farag; Bailey Verdone – Online Learning, 2024
The arrival of generative artificial intelligence (AI) is fundamentally different from prior technologies used in educational settings. Educators and researchers of online, blended, and in-person learning are still coming to grips with how to employ current AI technologies in the learning experience, let alone understanding the potential…
Descriptors: Artificial Intelligence, Technology Uses in Education, Ethics, Guidelines
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Mangino, Anthony A.; Smith, Kendall A.; Finch, W. Holmes; Hernández-Finch, Maria E. – Measurement and Evaluation in Counseling and Development, 2022
A number of machine learning methods can be employed in the prediction of suicide attempts. However, many models do not predict new cases well in cases with unbalanced data. The present study improved prediction of suicide attempts via the use of a generative adversarial network.
Descriptors: Prediction, Suicide, Artificial Intelligence, Networks
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C. M. Dubay; Melanie B. Richards – Marketing Education Review, 2024
Artificial intelligence (AI) has revolutionized various aspects of teaching and learning in higher education, with the potential to significantly enhance learning experiences, streamline administrative tasks, and foster personalized education. As the use of AI by students and instructors expands, it is crucial to carefully consider both its…
Descriptors: Artificial Intelligence, Student Projects, Active Learning, Technology Uses in Education
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Vishal Soodan; Avinash Rana; Anurag Jain; Deeksha Sharma – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This mixed-methods study aims to examine factors influencing academicians' intentions to continue using AI-based chatbots by integrating the Task-Technology Fit (TTF) model and social network characteristics. Background: AI-powered chatbots are gaining popularity across industries, including academia. However, empirical research on…
Descriptors: Artificial Intelligence, Social Networks, College Faculty, Computer Software
Taft, Laritza M. – ProQuest LLC, 2010
In its report "To Err is Human", The Institute of Medicine recommended the implementation of internal and external voluntary and mandatory automatic reporting systems to increase detection of adverse events. Knowledge Discovery in Databases (KDD) allows the detection of patterns and trends that would be hidden or less detectable if analyzed by…
Descriptors: Pregnancy, Risk, Patients, Program Effectiveness