Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.
Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
Technion, Israel Institute of Technology（以色列理工学院）；Rambam Health Care Campus（兰巴姆医疗中心）；Massachusetts Institute of Technology（麻省理工学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；The Ministry of Health（卫生部）
Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University（教育部道路与交通工程重点实验室，同济大学）；Cooperative Vehicle Infrastructure System (CVIS), Intelligent Transportation Products Department, AI Cloud Group, Baidu Inc.（协同车路系统（CVIS）、智能交通产品部、AI云团队，百度公司）；V2X AI Road Open Platform, AI Cloud Group, Baidu Inc.（V2X AI道路开放平台，AI云团队，百度公司）
Data Science Education in Undergraduate Physics: Lessons Learned from a Community of Practice
Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗森多夫亥姆霍兹中心高级系统理解中心）；University of Mount Union（芒特尤尼恩大学）；American Association of Physics Teachers（美国物理教师协会）；University of Maryland（马里兰大学）；National Institute of Standards and Technology（美国国家标准与技术研究院）；Questrom School of Business at Boston University（波士顿大学奎斯特罗姆商学院）
"GAN I hire you?" -- A System for Personalized Virtual Job Interview Training
Augsburg University（奥格斯堡大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）
