Diagnosing Object-Oriented Programming Difficulties: Association Rule Mining and Block-Based Scaffolding - Andre Rangga Gintara; Lala Septem Riza; Wahyudin (IJIES (International Journal of Innovation in Enterprise System), 2026)
Logical thinking is fundamental for Object-Oriented Programming, yet vocational students frequently struggle with its abstract concepts. This study aims to overcome these cognitive barriers through a data-driven diagnostic approach and targeted intervention. Utilizing Educational Data Mining techniques, specifically Association Rule Mining, this research analyzed pretest patterns to map learning difficulties among 35 software engineering students in Indonesia. A Research and Development method with a One-Group Pretest-Posttest design was employed. The analysis revealed a "cognitive domino effect," identifying that failures in advanced topics are rooted in specific prerequisite weaknesses. Based on these findings, a remedial intervention using a Scaffolding model assisted by Block-Based Programming was implemented. The results demonstrated that this data-driven intervention significantly enhanced students' logical thinking skills (p<0.001), with a substantial increase in N-Gain scores. It is concluded that integrating algorithmic diagnosis with visual scaffolding effectively bridges the gap between abstract concepts and practical coding skills, offering a scalable model for vocational education.