Automatic question generation for propositional logical equivalences

TL;DR

This paper presents a novel automatic question generation method for propositional logical equivalences in Discrete Mathematics, aiming to personalize and improve learning experiences in online education.

Contribution

It introduces a new AQG approach using syntactic grammar and semantic attributes to generate logical equivalence questions tailored for first-year computer science students.

Findings

Generated questions match textbook difficulty levels.

The approach is practical for educational use.

Potential to enhance personalized learning.

Abstract

The increase in academic dishonesty cases among college students has raised concern, particularly due to the shift towards online learning caused by the pandemic. We aim to develop and implement a method capable of generating tailored questions for each student. The use of Automatic Question Generation (AQG) is a possible solution. Previous studies have investigated AQG frameworks in education, which include validity, user-defined difficulty, and personalized problem generation. Our new AQG approach produces logical equivalence problems for Discrete Mathematics, which is a core course for year-one computer science students. This approach utilizes a syntactic grammar and a semantic attribute system through top-down parsing and syntax tree transformations. Our experiments show that the difficulty level of questions generated by our AQG approach is similar to the questions presented to students in the textbook [1]. These results confirm the practicality of our AQG approach for automated question generation in education, with the potential to significantly enhance learning experiences.