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Kylie L. Anglin – Annenberg Institute for School Reform at Brown University, 2025
Since 2018, institutions of higher education have been aware of the "enrollment cliff" which refers to expected declines in future enrollment. This paper attempts to describe how prepared institutions in Ohio are for this future by looking at trends leading up to the anticipated decline. Using IPEDS data from 2012-2022, we analyze trends…
Descriptors: Validity, Artificial Intelligence, Models, Best Practices
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
Charlotte Z. Mann; Adam C. Sales; Johann A. Gagnon-Bartsch – Grantee Submission, 2025
Combining observational and experimental data for causal inference can improve treatment effect estimation. However, many observational data sets cannot be released due to data privacy considerations, so one researcher may not have access to both experimental and observational data. Nonetheless, a small amount of risk of disclosing sensitive…
Descriptors: Causal Models, Statistical Analysis, Privacy, Risk
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
Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
Xuandong Zhao – ProQuest LLC, 2024
The rapid advancement of powerful Large Language Models (LLMs), such as ChatGPT and Llama, has revolutionized the world by bringing new creative possibilities and enhancing productivity. However, these advancements also pose significant challenges and risks, including the potential for misuse in the form of fake news, academic dishonesty,…
Descriptors: Computational Linguistics, Intellectual Property, Artificial Intelligence, Productivity
WenHua Cui; Yiming Fang; Yan Ma – International Journal of Web-Based Learning and Teaching Technologies, 2024
A framework was proposed to identify the at-risk factors of college courses in blended mode, offering suggestions for continuous improvement. An indicator system concerning teaching quality characteristics was constructed based on context, input, process, and product (CIPP) model. Subsequently, the group Analytic Hierarchy Process (AHP) algorithm…
Descriptors: Higher Education, Blended Learning, Risk Assessment, Risk
Christine Ladwig; Dana Schwieger – Information Systems Education Journal, 2024
Hollywood screenwriters worry about Artificial Intelligence (AI) replacements taking over their jobs. Famous museums litigate to protect their art from AI infringement. A major retailer scraps a machine-learning based recruitment program that was biased against women. These are just a few examples of how AI is affecting the world of work,…
Descriptors: Computer Science Education, Curriculum Development, Information Systems, Information Science Education
Ahmed Alkaabi; Asma Abdallah; Shamma Alblooshi; Fatima Alomari; Sara Alneaimi – Journal of Education and e-Learning Research, 2025
This study examines the opportunities and challenges of employing ChatGPT in higher education, identifies essential user competencies, and evaluates its impact in the absence of formal policy guidelines. A qualitative case study design involved interviews with 10 faculty members and 10 students at a federal university in the United Arab Emirates.…
Descriptors: Artificial Intelligence, Teaching Methods, Computer Software, Higher Education