Rapid guessing, a behavior in which students select answers quickly with minimal cognitive engagement, can significantly bias performance metrics and weaken the effectiveness of educational interventions. By analyzing the results of several detection techniques adapted for research purposes and applied to micro-course data, this paper compares different methods for identifying rapid guessing behavior in a low-stakes microlearning environment. The study evaluates the performance of individual methods at the test level, highlighting differences in their effectiveness and accuracy in detecting rapid guessing. It also considers the potential for implementing these methods in a typical microlearning environment. The findings have significant implications for educators and instructional designers, helping refine microlearning assessments and improving the overall quality of educational experiences.