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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
Xia, Qi; Chiu, Thomas K. F.; Chai, Ching Sing – Education and Information Technologies, 2023
Artificial intelligence (AI) has the potential to support self-regulated learning (SRL) because of its strong anthropomorphic characteristics. However, most studies of AI in education have focused on cognitive outcomes in higher education, and little research has examined how psychological needs affect SRL with AI in the K-12 setting. SRL is a…
Descriptors: Artificial Intelligence, Grade 9, Student Needs, Gender Differences
Yildiz, Muhammed Berke; Börekci, Caner – Journal of Educational Technology and Online Learning, 2020
Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data…
Descriptors: Grade Prediction, Academic Achievement, Artificial Intelligence, Grade 9
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
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries