Measure Student Aptitude in Learning Programming in Higher Education—A Data Analysis

This is the authors' abstract. We don't add key points for this paper.

Analyzing student performance in Introductory Programming courses in Higher Education is critical for early intervention and improved learning outcomes. This study explores the potential of a cognitive test for student success in an Introductory Programming course by analyzing data from 180 students, including Freshmen and Repeating Students, using descriptive statistics, correlation analysis, Categorical Principal Component Analysis and Item Response Theory models analysis. Analysis of the cognitive test revealed that some reasoning questions presented a statistically significant correlation, albeit of weak magnitude, with the course grades, particularly for freshman students. The development of models for predicting student performance in Introductory Programming using cognitive tests is also being explored. This study found that reasoning skills, namely logical reasoning and sequence completion, were more predictive of success in programming than general ability. The study also showed that a Programming Cognitive Test can be a useful tool for identifying students at risk of failure, particularly for freshmen students.