Mastery Learning In Programming Professors in Global

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C. Green
University of Wisconsin–Madison United States of America
motor learning
human learning
cognitive learning
depth learning
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Yinbei Li
Singapore Institute of Technology Singapore
robot learning
reinforcement learning
imitation learning
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Nicholas Rhinehart
University of Toronto Canada
robot learning
reinforcement learning
imitation learning
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Yuhong Guo
Carleton University Canada
reinforcement learning
continual learning
self-supervised learning
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Pascal Poupart
University of Waterloo Canada
reinforcement learning
continual learning
self-supervised learning
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Yuhong Guo
Carleton University Canada
reinforcement learning
continual learning
self-supervised learning
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Olav Krigolson
University of Victoria Canada
motor learning
learning
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Johanna Van Schaik
Radboud University Netherlands
programming
learning
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Laurence Frank
Utrecht University Netherlands
programming
statistical learning
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Ali Dadras
Umeå University Sweden
mathematical programming
statistical learning
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Hoomaan Maskan
Umeå University Sweden
mathematical programming
statistical learning
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Nick Harvey
University of British Columbia Canada
mathematical programming
statistical learning
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Ashwin Pananjady
Georgia Institute of Technology United States of America
mathematical programming
reinforcement learning
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Philip Loewen
University of British Columbia Canada
mathematical programming
reinforcement learning
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Mucahit Cevik
Toronto Metropolitan University Canada
mathematical programming
reinforcement learning
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Nick Turk-browne
Yale University United States of America
learning
statistical learning
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Tamara Van Gog
Utrecht University Netherlands
learning
multimedia learning
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Christopher Atkeson
Carnegie Mellon University United States of America
memory-based learning
reinforcement learning
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Yichen Zhang
Purdue University United States of America
multi-task learning
reinforcement learning
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Benedek Kurdi
University of Illinois United States of America
learning
reinforcement learning
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Qingyun Wu
Pennsylvania State University United States of America
learning
reinforcement learning
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