Enhancing Assessment Security and Fairness in Learning Management Systems through Fisher-Yates Shuffle Algorithm

Abstract

This research investigates the implementation of the Fisher-Yates shuffle algorithm to enhance assessment security and fairness in Learning Management Systems (LMS). The Fisher-Yates algorithm provides a mathematically proven approach to generating truly random question sequences, directly addressing the core challenge of maintaining academic integrity in digital assessment environments. The research methodology involved implementing the Fisher-Yates shuffle algorithm within an LMS assessment framework and conducting empirical testing to validate its effectiveness in preventing systematic cheating. Results demonstrate that the Fisher-Yates implementation successfully generates unique question sets with zero duplication rates and maintains statistical randomness across all test iterations. The findings indicate that implementing Fisher-Yates shuffle algorithm significantly improves assessment integrity by preventing pattern recognition and reducing opportunities for academic misconduct while ensuring fair difficulty distribution across all assessment instances.

Keywords: Fisher-Yates Shuffle Algorithm, Assessment Security, Question Randomization, Academic Integrity, Online Assessment, MCQ Testing, Educational Technology, Algorithmic Fairness, Learning Management System