PrairieLearn: Mastery-based Online Problem Solving with Adaptive Scoring and Recommendations Driven by Machine Learning
PrairieLearn: Mastery-based Online Problem Solving with Adaptive Scoring and Recommendations Driven by Machine Learning
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PrairieLearn：通过机器学习驱动的自适应评分和建议来解决基于掌握的在线问题
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
C. Zilles
中科院分区：
文献类型：
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作者：
Matthew West;Geoffrey L. Herman;C. Zilles
Matthew West是伊利诺伊大学香槟分校机械科学与工程系的副教授。在加入伊利诺伊州，他是对院系的航空航天部在斯坦福大学和数学系在加州大学戴维斯分校。West教授拥有博士学位。他拥有加州理工学院控制与动力系统专业的学士学位和西澳大利亚大学纯数学与应用数学专业的B学位。他的研究是在科学计算和数值分析领域，在那里他工作的计算算法模拟复杂的随机系统，如大气气溶胶和反馈控制。West教授是NSF CAREER奖的获得者，是伊利诺伊大学杰出教师学者和工程教育创新研究员学院。
Matthew West is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. Prior to joining Illinois he was on the faculties of the Department of Aeronautics and Astronautics at Stanford University and the Department of Mathematics at the University of California, Davis. Prof. West holds a Ph.D. in Control and Dynamical Systems from the California Institute of Technology and a B.Sc. in Pure and Applied Mathematics from the University of Western Australia. His research is in the field of scientific computing and numerical analysis, where he works on computational algorithms for simulating complex stochastic systems such as atmospheric aerosols and feedback control. Prof. West is the recipient of the NSF CAREER award and is a University of Illinois Distinguished Teacher-Scholar and College of Engineering Education Innovation Fellow.