2024 ASEE Annual Conference & Exposition

Numerous research studies have explored the influence of curriculum complexity on student performance, primarily focusing on factors like retention and graduation rates. Many of these investigations have employed conventional machine learning and data analysis methods, often yielding results that are challenging to interpret and convey effectively. Furthermore, these studies have generally lacked a comprehensive framework for elucidating how variables such as student gender and prior academic preparation contribute to the selection of specific university programs, each characterized by its own structural complexity. In essence, these studies have not presented foundational models to elucidate the fundamental mechanisms underlying the causal relationship between the complexity of university programs, student attributes, and success metrics.

In our present study, we introduce an innovative causal inference network model that conceptualizes the university as a dynamic system with interrelated causal relationships among its various components, encompassing students, faculty, programs, colleges, graduation rates, and more, each with their respective dependencies. This model affords us the ability to comprehend and visually represent the direction of causality between different variables, enabling us to investigate how changes in one variable, the causal factor, impact another variable. This implementation of causality not only facilitates predictive tasks, like other conventional machine learning models (i.e., hypothetical causation), but also enables us to conduct objective ``what-if" analyses (i.e., counterfactual causation) within the research context.

In this study, we leverage real-world student data from 30 different universities across the United States. The richness and diversity of our dataset empower us to draw robust insights into the causal relationships among various factors that influence student performance, particularly the complexity of the curriculum. One noteworthy preliminary finding from our application of this causal model is that students of certain genders, academic backgrounds, or socioeconomic conditions tend to gravitate toward university programs characterized by specific structural complexities. The breadth and scale of our dataset contribute significantly to our ability to derive substantive and compelling conclusions from our research endeavors.