Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference

TL;DR

This paper presents a human-in-the-loop rubric sampling method combined with deep learning inference to provide accurate, autonomous feedback for early students in programming courses, outperforming data-hungry algorithms and approaching human-level quality.

Contribution

It introduces a novel zero shot feedback approach using rubric sampling with deep learning, requiring minimal teacher effort and effective for early-stage students.

Findings

Outperforms data-hungry algorithms in early student feedback accuracy.

Can associate feedback with specific solution parts and misconceptions.

Demonstrated on a large dataset from Code.org.

Abstract

In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of the machine learning community, with many new algorithms attempting to autonomously provide feedback to help future students learn. But what about those first hundred thousand students? In most educational contexts (i.e. classrooms), assignments do not have enough historical data for supervised learning. In this paper, we introduce a human-in-the-loop "rubric sampling" approach to tackle the "zero shot" feedback challenge. We are able to provide autonomous feedback for the first students working on an introductory programming assignment with accuracy that substantially outperforms data-hungry algorithms and approaches human level fidelity. Rubric sampling requires minimal teacher effort, can associate feedback with specific parts of a student's solution and can articulate a student's misconceptions in the language of the instructor. Deep learning inference enables rubric sampling to further improve as more assignment specific student data is acquired. We demonstrate our results on a novel dataset from Code.org, the world's largest programming education platform.