A Proposal of Python Programming Exercise Problems for Basic Statistics Learning

Statistical learning with programming can be used to improve statistical literacy, teach students, and promote educational innovations in information-driven universities through technology-mediated statistical course. However, many novice students struggle with studying it due to the complexities involved in learning statistics through programming. In this works, we propose a study of Python programming exercise problems focusing on the Element Fill-in-Blank Problem (EFP), Grammar-concept Understanding Problem (GUP), and ValueTrace Problem (VTP) as a tools for self-studying statistics. String matching methods were implemented to analyze and verify the proper solution in the each responses. For the evaluations, we generated 14 instances problem for introduction to statistics study, then assigned them to 67 first-year undergraduate students in UPN “Veteran” Jawa Timur, Indonesia. The empirical results proved that the proposed study is applicable for helping student to learn statistics using Python programming approach.