Information visualization through visual images has been an effective way to convey both abstract and tangible ideas. This study explored students' preferred visualization method when using visual variables to show uncertainty in the data. In this research, uncertainty represents the discrepancy between the student's perceived and actual knowledge in an e-learning system. In specific, the study identified the most preferred visual variable among the three visual variables: opacity, line thickness, and added symbols. Viewing these discrepancies in the data can help students better understand the data depicted by visual variables and enhance their metacognitive skills. Properly chosen visual variables can spontaneously allow users to recognize the patterns illustrated by those visual variables without the need to divide their attention between two cognitive tasks. To answer the research question, the researcher followed an exploratory approach. The population of the study is all students at SQU. The sample size is 215 students enrolled in the university elective course titled the Fourth Industrial Revolution. Out of the 215 students, only 83 students remained the main sample because they completed both the Criterion References Test (CRT) and the paired comparison and passed the 80% on the CRT. Thurstone paired comparison and inferential statistics were used to analyze the data using R programming language. Thurstone test ranks the variables on a preferences scale from the most to the least preferred. The test ranked the three variables from most to least as follows: line thickness, added symbols, and opacity. The inferential statistics showed that there are no significant differences in preference among the three variables. However, based on college classification, there were significant differences between students from science colleges and humanities colleges regarding the variables line thickness and added symbols. On the other hand, there was no significant difference between different colleges when choosing opacity as their preferred method. The findings of the study can help system designers create visuals with preferred visuals that aid the students to visualize uncertainty when students interact within an e-learning system.