An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network

TL;DR

This paper presents an innovative automated SQL query grading system that leverages an attention-based convolutional neural network architecture to improve understanding and grading accuracy of student SQL submissions.

Contribution

The paper introduces a novel CNN architecture with parameter sharing and attention mechanisms specifically designed for grading SQL queries, enhancing understanding over previous methods.

Findings

Improved accuracy in automated SQL grading.

Effective knowledge representation of SQL statements.

Potential for scalable and efficient grading process.

Abstract

Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an attempt to mitigate these challenges, but those systems have their own limitations. This paper describes our novel approach to automating the process of grading SQL queries. Unlike previous approaches, we employ a unique convolutional neural network architecture that employs a parameter-sharing approach for different machine learning tasks that enables the architecture to induce different knowledge representations of the data to increase its potential for understanding SQL statements.