Curricular Analytics: A Probabilistic Approach

Many universities are putting graduation and retention rates under intensive inspection. This is motivated by various elements, including but not limited to the way that a bachelors degree has turned into an inexorably important necessity for prosperity in the labor market. To that end, colleges are gathering data related to student progress and achievement and implementing more data-driven frameworks in attempts to decide the most imperative features that impact attrition and persistence. In this research, we study student progress by exploring the basic properties of individual curricula and its relation to completion rate from a probabilistic approach. We applied a Monte Carlo method to assess curriculum efficiency by enrolling a large number of virtual students in a degree plan, statistically determining whether each student passes each course, reevaluating a student’s degree trajectory based on course failures and successes, and recording the amount of time it takes each student to complete a degree plan. As an example, we have considered two computer engineer degree plans offered by the Department of Electrical and Computer engineer at Valparaiso University. Even though these programs have identical program learning outcomes, it is readily apparent that their structures are quite dissimilar. In our simulation, we quantify these differences in a manner that leads to useful analytical results. For instance, what is the expected graduation rate for similarly prepared students in each curriculum? What is the most important course in each curriculum, and by how much would the success rates improve with small improvements in these courses