SHF: Small: Computer-Aided Grading, Feedback, and Assignment Creating in Massive Online Programming Courses

SHF：小型：大规模在线编程课程中的计算机辅助评分、反馈和作业创建

• 批准号: 1320860
• 负责人: Swarat Chaudhuri
• 金额: $ 29.83万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320860&HistoricalAwards=false
• 关键词: SHF; Small; Computer; Aided; Grading

Massive Open Online Courses (MOOCs) are widely regarded as arevolutionary innovation; however, traditional instructionaltechniques do not scale to MOOCs with tens of thousands of students.This project aims to design a set of algorithmic tools to partiallyautomate three of the most basic instructional processes - gradingassignments, giving students personalized feedback, and creating newassignments - in MOOCs that teach computer programming. Such toolshave the potential to significantly raise the productivity ofinstructors in these courses, and give students a far more effectiveeducational experience than what they currently receive. They can alsoguide the development of future MOOCs on programming, and play a rolein making the MOOC model reach its full potential.The algorithms developed in this project draw on ideas from severaldifferent areas of computing. They leverage advances in automatedreasoning about software like automatically finding bugs instudent code and suggesting fixes, and exploit statistical learningtechniques that mine databases of previously-completed assignments andinfer aggregate statistics about a class. Finally, the research has adimension of human computation, for instance seeding logical andstatistical analysis techniques with data obtained through peerevaluation. These methods apply to any programming course; however,the investigators will evaluate them by deploying them in a specificMOOC on Python programming that they teach.

大规模开放式在线课程（MOOC）被广泛认为是一种革命性的创新;然而，传统的教学技术无法扩展到数万名学生的MOOC。该项目旨在设计一套算法工具，以部分自动化三个最基本的教学过程--在教授计算机编程的MOOC中评分作业、给学生个性化反馈和创建新作业。这样的工具有可能大大提高这些课程中教师的生产力，并给学生一个比他们目前接受的更有效的教育体验。它们也可以指导未来MOOC在编程方面的发展，并在使MOOC模式充分发挥其潜力方面发挥作用。本项目开发的算法借鉴了几个不同计算领域的思想。他们利用软件自动推理方面的进步，比如自动发现学生代码中的错误并提出修复建议，并利用统计学习技术，挖掘以前完成的作业数据库并推断关于一个班级的汇总统计数据。最后，这项研究有一个人类计算的维度，例如用通过同行评估获得的数据播种逻辑和统计分析技术。这些方法适用于任何编程课程;然而，研究人员将通过在他们教授的Python编程的特定MOOC中部署它们来评估它们。