Scale-Driven Automatic Hint Generation for Coding Style

Conference paper

DOI: 10.1007/978-3-319-39583-8_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)
Cite this paper as:
Roy Choudhury R., Yin H., Fox A. (2016) Scale-Driven Automatic Hint Generation for Coding Style. In: Micarelli A., Stamper J., Panourgia K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science, vol 9684. Springer, Cham

Abstract

While the use of autograders for code correctness is widespread, less effort has focused on automating feedback for good programming style: the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We present a system that can provide real-time actionable code style feedback to students in large introductory computer science classes. We demonstrate that in a randomized controlled trial, 70 % of students using our system achieved the best style solution to a coding problem in less than an hour, while only 13 % of students in the control group achieved the same. Students using our system also showed a statistically-significant greater improvement in code style than students in the control group.

Keywords

Coding style Autograding Automatic hint generation MOOCs 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of CaliforniaBerkeleyUSA

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