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Fuhai An; Jiawei Guo – Education and Information Technologies, 2024
Peer relationships play important roles in middle-school students' individual development. Peer support is indispensable in computer-supported learning contexts. This study is designed to explore the connection between perceived peer support and deeper learning, while examining the mediating role of computer self-efficacy and perceived classroom…
Descriptors: Peer Relationship, Goal Orientation, Mastery Learning, Prediction
Zhang, Qiao; Maclellan, Christopher J. – International Educational Data Mining Society, 2021
Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…
Descriptors: Electronic Learning, Mastery Learning, Computer Simulation, Intelligent Tutoring Systems
Taft, Seth A. – Music Educators Journal, 2019
Challenges are a constant across all kinds of musical contexts, and teachers are always looking for ways to help students overcome them. While there are many technical and pedagogical approaches to helping students achieve in music, psychology also plays a significant role in the struggles that students face. Counteractive optimism, the act of…
Descriptors: Music, Music Education, Positive Attitudes, Prediction
Johnson, Jerraco Leontae – ProQuest LLC, 2019
It is widely accepted that fundamental motor skills (FMS) are considered the building blocks for successful participation in sports and physical activity for children, adolescents, and adults (Haywood & Getchell, 2014), and that individuals must be able to master these skill patterns before they can engage in more complex movement patterns,…
Descriptors: Psychomotor Skills, Physical Activities, Mastery Learning, Preschool Children
Sales, Adam C.; Botelho, Anthony; Patikorn, Thanaporn; Heffernan, Neil T. – International Educational Data Mining Society, 2018
Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data--often highdimensional and longitudinal--to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a…
Descriptors: Courseware, Data Analysis, Causal Models, Prediction