Didactic challenges in Descriptive Statistics: from manual to computational solutions using Python in the Software Engineering degree.

Abstract

This study analyzes the impact of implementing Python in learning Descriptive Statistics among third-semester Software Engineering students. Using a paired sample design (N = 27), performance in manual versus computational evaluations was compared. Results showed an arithmetic decrease in performance with the digital tool (Mean: 67.5) compared to the manual method (Mean: 75.2), without reaching statistical significance (p > .05), suggesting a competence adaptation despite initial cognitive overload. Likewise, a positive correlation (r = .413) was found between practical activity compliance and digital grades. It is concluded that programming acts as a discipline filter and the interleaved methodology permits mitigating the syntactic barrier.