Identifying User Types in Gamified Learning Management Systems Based on Behavior Data

Abstract

In recent years, considerable effort has been devoted to identifying and categorizing different user types in gamified systems. However, many studies predominantly rely on scales to identify user types, posing challenges such as removing users from the system to complete scale-based questionnaires, difficulty scaling applications for large groups, and needing to periodically re-administer the scales. Addressing this issue, we explore the possibility of identifying user types within a gamified learning management system based on users' behavioral data. We conducted an exploratory data-driven case study (N = 479) using machine learning to uncover behavioral patterns among users of the system. Our research identifies three distinct groups, providing insights into gamification by demonstrating how user behavior data in a gamified learning management system can be used to map user types.