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Bobrovitz, Niklas; Noël, Kim; Li, Zihan; Cao, Christian; Deveaux, Gabriel; Selemon, Anabel; Clifton, David A.; Yanes-Lane, Mercedes; Yan, Tingting; Arora, Rahul K. – Research Synthesis Methods, 2023
Risk of bias (RoB) assessments are a core element of evidence synthesis but can be time consuming and subjective. We aimed to develop a decision rule-based algorithm for RoB assessment of seroprevalence studies. We developed the SeroTracker-RoB algorithm. The algorithm derives seven objective and two subjective critical appraisal items from the…
Descriptors: Decision Making, Algorithms, Risk, Bias
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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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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
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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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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
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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
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Kim, Sehwan; Wurster, Leslie; Williams, Charles; Hepler, Nancy – Journal of Drug Education, 1998
Presents a model for resource allocation of state-prevention funds based on the relative need for prevention services measured in terms of composite risk-factor index scores computed for each county. Suggests that funding be based on the resource-allocation algorithms rather than the suggested amount of funding computed for each county.…
Descriptors: Algorithms, Prevention, Programs, Resource Allocation