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

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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.