This paper details an outlook on modern constraint programming (CP) education
through the lens of a CP instructor. A general overview of current CP courses
and instructional methods is presented, with a focus on online and
virtually-delivered courses. This is followed by a discussion of the novel
approach taken to introductory CP education for engineering students at large
scale at the Georgia Institute of Technology (Georgia Tech) in Atlanta, GA,
USA. The paper summarizes important takeaways from the Georgia Tech CP course
and ends with a discussion on the future of CP education. Some ideas for
instructional methods, promotional methods, and organizational changes are
proposed to aid in the long-term growth of CP education.

本研究探讨了当代约束编程（CP）教育的视角，提供了当前 CP 课程和教学方法的概述，并着重介绍了美国亚特兰大乔治亚理工学院（Georgia Tech）在大规模工程学生中进行初级 CP 教育的新方法和其重要收获，最后提出了一些促进 CP 教育长期发展的教学方法，宣传方法和组织变革的设想。

现代约束编程教育：未来的经验教训

Modern Constraint Programming Education: Lessons for the Future

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.

本研究提出了一种名为潜在技能嵌入（LSE）的概率模型，可以用于推荐个性化的课程序列，并通过历史学生 - 内容交互来对学生、课程和评估进行最大似然嵌入的规范化。对大规模数据的实证评估表明，这种方法具有与基准模型相当的评估预测能力，并能够区分导致掌握和失败的课程序列。