Effectiveness of Data-Driven Educational Guidance Based on Learning Analytics to Prevent School Dropout in High-Density Schools

Abstract

The study aimed to evaluate the effectiveness of educational guidance based on learning analytics in reducing school dropout among secondary school students in high-density classrooms (more than 60 students per class). It adopted a mixed-methods approach and was applied to 272 students in 4 Iraqi schools. Results showed a 20.4% reduction in dropout indicators and an inverse relationship between student density and effectiveness. The strongest predictors of dropout risk were "task completion" followed by "regularity". The study concluded that guidance is effective provided that classroom density does not exceed 70 students (preferably less than 60), along with a proposed early warning system framework.