Curricular complexity as a metric to forecast issues with transferring into a redesigned engineering curriculum

Abstract

This paper details quantifying the interconnectedness of a curriculum. We draw from Heileman's Curricular Analytics tool and the curricular complexity metric. We extend this metric to highlight how it can be used to forecast issues in transfer student experiences in redesigned curricula. We focus on structural complexity in this paper by consolidating transfer student pathways using plans of study from the Department of Electrical and Computer Engineering at a four-year institution, Virginia Tech, undergoing a large-scale programmatic change, with those from the department's feeder community colleges. We transformed the 24 pre and post-change prerequisite structures in the plans of study into networks, allowing for graph-theoretic metrics to be calculated and compared (pre-/post-change). These networks enabled us to identify bottlenecks in the curriculum and negotiate how transfer students could be supported in the new program. We discuss extensions to the curricular complexity approach, like using agent-based modeling to simulate student flow through a curriculum and predicting four, five, and six-year graduation rates.