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

基本信息

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

项目摘要

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.

大规模开放在线课程（MOOC）被广泛认为是革命性的创新；然而，传统的教学技术无法扩展到拥有数万名学生的 MOOC。该项目旨在设计一套算法工具，以部分自动化 MOOC 中的三个最基本的教学过程——评分作业、为学生提供个性化反馈和创建新作业。教授计算机编程。此类工具有可能显着提高教师在这些课程中的工作效率，并为学生提供比他们目前获得的更有效的教育体验。它们还可以指导未来编程慕课的发展，并在使慕课模式充分发挥潜力方面发挥作用。该项目开发的算法借鉴了多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，例如自动查找学生代码中的错误并提出修复建议，并利用统计学习技术来挖掘先前完成的作业的数据库并推断有关班级的汇总统计数据。最后，该研究具有人工计算的维度，例如利用通过同行评估获得的数据来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，研究人员将通过将它们部署在他们教授的 Python 编程特定 MOOC 中来评估它们。