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Michael L. Chrzan; Francis A. Pearman; Benjamin W. Domingue – Annenberg Institute for School Reform at Brown University, 2025
The increasing rate of permanent school closures in U.S. public school districts presents unprecedented challenges for administrators and communities alike. This study develops an early-warning indicator model to predict mass closure events -- defined as a district closing at least 10% of its schools -- five years in advance. Leveraging…
Descriptors: Artificial Intelligence, Electronic Learning, School Districts, School Closing
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Chaewon Lee; Lan Luo; Shelbi L. Kuhlmann; Robert D. Plumley; Abigail T. Panter; Matthew L. Bernacki; Jeffrey A. Greene; Kathleen M. Gates – Journal of Learning Analytics, 2025
The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…
Descriptors: Electronic Learning, Prediction, Data Analysis, Artificial Intelligence
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Austin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
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Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
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Frances Edwards; Bronwen Cowie; Suzanne Trask – Professional Development in Education, 2025
This paper reports on teachers developing their own data literacy and then acting as data coaches for colleagues in their schools. The 13 teachers from 7 schools in the study analysed standardised data using a data conversation protocol to identify students with significant mathematical misconceptions. They then took data-informed action with…
Descriptors: Coaching (Performance), Peer Teaching, Statistics Education, Knowledge Level
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Rachel Elizabeth Fish; Kenneth Shores; João M. Souto Maior – Exceptional Children, 2026
This essay provides a two-pronged critical assessment of a subset of the literature on racial disproportionality in special education: that which aims to estimate racial disparities among otherwise similar children. This body of research has shown that Black students are less likely than "comparable" White students to receive special…
Descriptors: Disproportionate Representation, Race, Special Education, African American Students
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Yan Lu – International Journal of Web-Based Learning and Teaching Technologies, 2025
Under the background of educational informatization, data-driven teaching decision-making has become a key means to improve the quality of education, but there are some shortcomings in the use of data in college English teaching decision-making. This study constructs an evaluation system of college English teaching decision-making ability,…
Descriptors: Learning Analytics, Decision Making, Teaching Methods, Educational Improvement