In-browser Solution using Machine Learning (ML) libraries to Prevent Procrastination in Web Browsing for University Students in Metropolitan Lima

Abstract:

Academic procrastination is a pervasive issue among university students. Recent evidence shows that 58.8% exhibit high levels of procrastination, whereas only 2.1% report minimal levels [1]. This behavioral pattern is strongly associated with Internet addiction, which affects 43.3% of students [1], and is further reinforced by emotional regulation difficulties, including poor task orientation and deficits in impulse control [2]. Research also indicates systematic differences across demographic groups: men tend to procrastinate more than women, and undergraduate students more than graduate students [3]. Moreover, procrastination is consistently linked to higher stress levels, reduced engagement in healthy habits, and overall impairment in academic performance and student well-being [4]. Collectively, these findings demonstrate that procrastination is not confined to isolated subpopulations; rather, it constitutes a widespread and cross-cutting challenge across diverse student groups [3].