The Impact of the Inherent Language Complexity on Academic Performance: Using Data Analytic Approach to Profile Diverse Student Learning

The study examines how language complexity in engineering courses affects students' academic performance to create a more inclusive learning environment. While math is crucial, language proficiency also impacts success, often overlooked. We aim to assess language complexity in chemical engineering courses, proposing a methodology to gauge curriculum progression from basic knowledge to problem-solving and design. Chemical engineering courses typically start with core concepts and advance to practical application. Our goal is to establish a method reflecting this progression and identify areas for improvement. Using natural language processing (NLP), we analyze course materials and final exams, defining features like word frequency and syntactic complexity. Performance data from a decade and 1100 graduates validate our analysis, showing increased language complexity in higher-level courses. International students initially outperform citizens, but this diminishes in advanced courses. Our study pioneers a framework for assessing course language difficulty, aiding curriculum evaluation and student support. By integrating NLP and data analytics, it identifies diverse learning challenges, enabling more inclusive education. Future research should expand to include more courses and disciplines, enhancing educational equity and effectiveness.