EXP: Automatically Synthesizing Valid, Personalized, Formative Assessments of CS1 Concepts

EXP：自动综合有效的、个性化的、形成性的 CS1 概念评估

• 批准号: 1735123
• 负责人: Amy Ko
• 金额: $ 54.99万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735123&HistoricalAwards=false
• 关键词: EXP; Automatically; Synthesizing; Valid; Personalized

Millions of people worldwide are trying to learn to code in schools, colleges, universities, and online. To do so successfully, they need significant practice and meaningful feedback that responds to their specific confusions and builds upon the knowledge they already have. Unfortunately, good practice content is challenging to create at scale and skilled teachers capable of providing meaningful feedback are rare and often inaccessible. Moreover, popular online learning technologies only provide a fixed amount of static content and do little to provide meaningful feedback. Because of this, many people give up learning to code, and only those with privileged access to friends, family, mentors, or teachers who can provide this support persist. This limits access to this critical 21st century literacy and ultimately harms the gender, racial, ethnic, and intellectual diversity of our computing workforce. This project seeks to address part of this problem, applying advances in programming languages research that enable the creation of infinite amounts of diverse practice content, and advances in machine learning to build models of what learners do and do not know. By applying these two advances in computer science, the project will create a novel online learning technology that automatically generates assessment content, provides detailed immediate feedback about solutions, and uses assessment information of learners' performance to generate more personalized practice that individually targets concepts that learners are struggling to master. In creating such a system, new techniques for generating practice problems and new approaches to modeling learner knowledge of introductory programming concepts will be realized. The project will also explore students' current approach to practice, then deploy the new system into a large introductory classroom setting to experimentally measure its impact on learning and confidence. If the system is effective, the discoveries have the potential to provide learners in a range of settings more effective practice and learning. This may in turn improve both the competency and diversity of learners who further engage in computing education.

全世界有数百万人正在学校、学院、大学和网上学习编程。为了成功地做到这一点，他们需要大量的实践和有意义的反馈，以回应他们的具体困惑，并建立在他们已经拥有的知识基础上。不幸的是，良好的实践内容是具有挑战性的规模和熟练的教师能够提供有意义的反馈是罕见的，往往无法访问。此外，流行的在线学习技术只提供固定数量的静态内容，几乎没有提供有意义的反馈。正因为如此，许多人放弃了学习编程，只有那些有特权接触朋友、家人、导师或老师的人才能提供这种支持。这限制了对这一关键的世纪文化的访问，并最终损害了我们计算劳动力的性别、种族、民族和智力多样性。该项目旨在解决这一问题的一部分，应用编程语言研究的进步，能够创建无限数量的多样化实践内容，以及机器学习的进步来构建学习者所知道和不知道的模型。通过应用计算机科学的这两项进步，该项目将创建一种新的在线学习技术，自动生成评估内容，提供有关解决方案的详细即时反馈，并使用学习者表现的评估信息来生成更个性化的实践，单独针对学习者正在努力掌握的概念。在创建这样一个系统，新的技术产生的实践问题和新的方法来建模学习者的知识介绍编程概念将实现。该项目还将探索学生目前的实践方法，然后将新系统部署到一个大型的入门课堂环境中，以实验方式衡量其对学习和信心的影响。如果该系统是有效的，这些发现有可能为学习者提供更有效的实践和学习。这反过来又可以提高进一步从事计算教育的学习者的能力和多样性。

项目成果

Pedagogical Content Knowledge for Teaching Inclusive Design

用于教学包容性设计的教学内容知识

• DOI: 10.1145/3230977.3230998
• 发表时间: 2018
• 期刊: Pedagogical Content Knowledge for Teaching Inclusive Design
• 作者: Oleson, Alannah;Mendez, Christopher;Steine-Hanson, Zoe;Hilderbrand, Claudia;Perdriau, Christopher;Burnett, Margaret;Ko, Andrew J.
• 通讯作者: Ko, Andrew J.

Comprehension First: Evaluating a Novel Pedagogy and Tutoring System for Program Tracing in CS1

理解第一：评估用于 CS1 程序跟踪的新颖教学法和辅导系统

• DOI: 10.1145/3105726.3106178
• 发表时间: 2017
• 期刊: ACM International Computing Education Research Conference
• 作者: Nelson, Greg L.;Xie, Benjamin;Ko, Andrew J.
• 通讯作者: Ko, Andrew J.

Investigating Novices' In Situ Reflections on Their Programming Process

调查新手对其编程过程的现场反思

• DOI: 10.1145/3328778.3366846
• 发表时间: 2020
• 作者: Loksa, Dastyni;Xie, Benjamin;Kwik, Harrison;Ko, Amy J.
• 通讯作者: Ko, Amy J.

A theory of instruction for introductory programming skills

入门编程技能的教学理论

• DOI: 10.1080/08993408.2019.1565235
• 发表时间: 2019
• 期刊: Computer Science Education
• 影响因子: 2.7
• 作者: Xie, Benjamin;Loksa, Dastyni;Nelson, Greg L.;Davidson, Matthew J.;Dong, Dongsheng;Kwik, Harrison;Tan, Alex Hui;Hwa, Leanne;Li, Min;Ko, Andrew J.
• 通讯作者: Ko, Andrew J.

Empowering Families Facing English Literacy Challenges to Jointly Engage in Computer Programming

帮助面临英语素养挑战的家庭共同参与计算机编程

• DOI: 10.1145/3173574.3174196
• 发表时间: 2018
• 期刊: ACM SIGCHI Conference on Human Factors in Computing
• 作者: Banerjee, Rahul;Ko, Andrew J.;Popovic, Zoran;Liu, Leanne;Sobel, Kiley;Pitt, Caroline;Lee, Kung Jin;Wang, Meng;Chen, Sijin;Davison, Lydia
• 通讯作者: Davison, Lydia