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Shabnam Ara S. J.; Tanuja Ramachandriah; Manjula S. Haladappa – Online Learning, 2025
Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…
Descriptors: Learning Analytics, Privacy, Artificial Intelligence, Regression (Statistics)
Zikai Zhou; Sharon Rouse – Online Learning, 2024
Student satisfaction is a key performance indicator in evaluating any degree program's performance. In light of the vast difference between online and traditional degree programs, factors that may significantly affect student satisfaction and thus contribute to the success of online degree programs still need to be explored. Previous literature on…
Descriptors: Models, Evaluation Methods, Online Courses, Student Satisfaction
Wilson, Mark; Gochyyev, Perman; Scalise, Kathleen – Online Learning, 2016
This paper summarizes initial field-test results from data analytics used in the work of the Assessment and Teaching of 21st Century Skills (ATC21S) project, on the "ICT Literacy--Learning in digital networks" learning progression. This project, sponsored by Cisco, Intel and Microsoft, aims to help educators around the world enable…
Descriptors: Data Collection, Data Analysis, Technological Literacy, Social Networks