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Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam – IEEE Transactions on Learning Technologies, 2017
Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and…
Descriptors: Computer Assisted Instruction, Problem Solving, Learning, Student Behavior
Hung, Jui-Long; Shelton, Brett E.; Yang, Juan; Du, Xu – IEEE Transactions on Learning Technologies, 2019
Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction…
Descriptors: Prediction, Models, At Risk Students, Identification
Kinnebrew John S.; Killingsworth, Stephen S.; Clark, Douglas B.; Biswas, Gautam; Sengupta, Pratim; Minstrell, James; Martinez-Garza, Mario; Krinks, Kara – IEEE Transactions on Learning Technologies, 2017
Digital games can make unique and powerful contributions to K-12 science education, but much of that potential remains unrealized. Research evaluating games for learning still relies primarily on pre- and post-test data, which limits possible insights into more complex interactions between game design features, gameplay, and formal assessment.…
Descriptors: Computer Games, Educational Games, Data Analysis, Science Education

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