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Okan Bulut; Tarid Wongvorachan; Surina He; Soo Lee – Discover Education, 2024
Despite its proven success in various fields such as engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school…
Descriptors: High School Students, Dropouts, Identification, Man Machine Systems
Dorothy Daniels – ProQuest LLC, 2021
In the United States the number of English Language Learner students is steadily increasing. Many ELLs speak and understand limited English, resulting in achievement that lags far behind that of their classmates (Thomas, 2015). Recruiting the support of educators who come in contact with ELL students on a daily basis promoted a solution to this…
Descriptors: Middle Schools, English Language Learners, Assistive Technology, Artificial Intelligence
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Soland, James; Domingue, Benjamin; Lang, David – Teachers College Record, 2020
Background/Context: Early warning indicators (EWI) are often used by states and districts to identify students who are not on track to finish high school, and provide supports/interventions to increase the odds the student will graduate. While EWI are diverse in terms of the academic behaviors they capture, research suggests that indicators like…
Descriptors: Identification, At Risk Students, Potential Dropouts, High School Students
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Psyridou, Maria; Tolvanen, Asko; Patel, Priyanka; Khanolainen, Daria; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Torppa, Minna – Scientific Studies of Reading, 2023
Purpose: We aim to identify the most accurate model for predicting adolescent (Grade 9) reading difficulties (RD) in reading fluency and reading comprehension using 17 kindergarten-age variables. Three models (neural networks, linear, and mixture) were compared based on their accuracy in predicting RD. We also examined whether the same or a…
Descriptors: Reading Difficulties, Networks, Artificial Intelligence, Predictor Variables
Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D.; Montalvo, Orlando; Nakama, Adam – Grantee Submission, 2013
We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in…
Descriptors: Artificial Intelligence, Inquiry, Middle School Students, Physical Sciences
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Shelley, Mack, Ed.; Akcay, Hakan, Ed.; Ozturk, Omer Tayfur, Ed. – International Society for Technology, Education, and Science, 2022
"Proceedings of International Conference on Research in Education and Science" includes full papers presented at the International Conference on Research in Education and Science (ICRES) which took place on March 24-27, 2022 in Antalya, Turkey. The aim of the conference is to offer opportunities to share ideas, to discuss theoretical and…
Descriptors: Educational Technology, Technology Uses in Education, Computer Peripherals, Equipment