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Ko, Chia-Yin; Leu, Fang-Yie – IEEE Transactions on Education, 2021
Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how…
Descriptors: Undergraduate Students, Student Characteristics, Academic Achievement, Predictor Variables
Mao, Ye; Zhi, Rui; Khoshnevisan, Farzaneh; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2019
Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…
Descriptors: Difficulty Level, Learning Activities, Prediction, Programming
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring

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