Date on Honors Thesis
Spring 5-2025
Major
Computer Science/Mathematics
Minor
Data Science
Examining Committee Member
Dr. Jason Owen, Advisor
Examining Committee Member
Dr. Christopher Mecklin, Committee Member
Examining Committee Member
Dr. Abdellatif Aboualy, Committee Member
Abstract/Description
This paper investigated students’ perceptions of their proficiency with statistical software applications and their preferences regarding software features. Results indicated that students’ statistical and coding experience, as well as the specific application used, did not significantly influence their self-perceived proficiency. This suggests that it may be more effective to focus on building student skills within a chosen application, rather than tailoring the application to match existing student capabilities. While students showed clear preferences for certain features, favoring clarity over depth, flexibility over safeguards, and built-in checks over unrestricted freedom, these preferences generally leaned toward balanced design rather than extremes. This paper also covers development of a statistical software application that takes a balanced approach to its design.
Recommended Citation
White, Sabrina, "Investigating students’ proficiency across statistical software and preferences of statistical software design" (2025). Honors College Theses. 264.
https://digitalcommons.murraystate.edu/honorstheses/264