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John Pace; John Hansen; John Stewart – Physical Review Physics Education Research, 2024
Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on…
Descriptors: Artificial Intelligence, Identification, At Risk Students, Physics
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Virginia Clinton-Lisell; Alison E. Kelly – International Journal of Technology in Education and Science, 2024
The use of online homework systems that require the purchase of an access code has become widespread. The purpose of this study is to examine student experiences with and perceptions of online homework systems with access codes. Postsecondary students (N = 966) completed a survey about the financial costs, perceptions of quality, engagement with,…
Descriptors: Homework, Educational Technology, Technology Uses in Education, Student Experience
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Blondin, Carolyn A.; Voils, Kyle; Galyon, Charles E.; Williams, Robert L. – Journal on Excellence in College Teaching, 2015
Concepts from the Response-to-Intervention (RTI) Model were used to promote a successful course outcome for students at risk for making low grades in an entry-level college course. The first exam served as a universal screener to identify students who could potentially benefit from RTI assistance. The researchers developed a tiered coaching…
Descriptors: Response to Intervention, Models, At Risk Students, Coaching (Performance)
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Abuya, Benta A.; Muhia, Nelson; Mokaya, Peter – Journal of Educational Research and Practice, 2019
This article explores the experiences of female mentees and their mentors in an afterschool support program in two informal urban settlements in Nairobi, Kenya. We sought the perspectives of mentees and mentors to identify what has changed concerning the education and social lives of the girls because of this education intervention. Data come from…
Descriptors: Foreign Countries, Females, Womens Education, Mentors
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Lu, Owen H. T.; Huang, Anna Y. Q.; Huang, Jeff C. H.; Lin, Albert J. Q.; Ogata, Hiroaki; Yang, Stephen J. H. – Educational Technology & Society, 2018
Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze…
Descriptors: Blended Learning, Educational Technology, Technology Uses in Education, Data Collection
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Butzler, Kelly B. – Computers in the Schools, 2016
Students at open-enrollment institutions enter college with a wide range of academic preparedness and are often required to take developmental classes to increase their academic skills to be successful in higher education. Further, few students possess self-regulated learning skills to aid in their learning. Researchers posited that academically…
Descriptors: Technology Uses in Education, Video Technology, Homework, Blended Learning
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Prevatt, Frances; Young, Joel L. – Journal of College Student Psychotherapy, 2014
Traditionally diagnosed in children, attention-deficit/hyperactivity disorder (ADHD) is now regarded as a life span condition. The academic difficulties experienced by children and adolescents with ADHD have been observed to continue into young adulthood. Treatment outcome studies demonstrate that behavioral and pharmacotherapeutic interventions…
Descriptors: Attention Deficit Hyperactivity Disorder, College Students, Behavior Modification, Drug Therapy
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Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis