The persistent issue of student dropout negatively impacts the educational sector and society at large. This study presents a machine learning model that leverages data from the National Household Survey to predict student dropout in Peru, integrating a wide range of socio-demographic variables. The research fills a gap in existing literature by providing a model that incorporates socio-demographic variables, an area not fully explored in previous studies. The predictive model aims to identify factors associated with student dropout, aiding educational stakeholders in implementing effective interventions. The findings underscore the model's potential to enhance educational outcomes by enabling early identification of at-risk students, thereby facilitating targeted support. This work contributes to refining predictive models of university dropout rates and sug- gests the use of ensemble m...

This article challenges traditional curriculum at engineering schools in Peru by moving engineering curriculum plans tobe reframed based on the amount of concentrated time a learner can spend on a subject without becoming distractedwith overloaded schedule by deliberate practice and less lecture rooms, learners gain a compelling expertise beforegraduation. After digging a little deeper into the student experience, we found the disconnect between what universities teach and the skills needed in the modern society. We have developed an empirical evidence for this estimatehinged on Bloom’s taxonomy in a case study at an engineering department. Our result has shown that, on average,5.5 hours is needed to reach the top level of Bloom's taxonomy immediately after one-hour lecture. From the resultsof this study and supported by Bloom's taxonomy and the forgetting curve theory, it is concluded...