2024 ASEE Annual Conference & Exposition

Representing credit accumulation as a function of "time" (a student’s terms), rather than as a single cumulative number, unlocks potential for uncovering patterns in the accrual of credits. In this paper, we present an analysis into the credit hour usage pattern of university students as a function of time. However, not all credits accumulated by the students are usable towards their degree program of study. Even if they are usable, they may not be applicable. In this work we use a custom-built specialized audit tool to decompose student credits in the following way: "unusable" credits that do not match any degree requirement, "excess" credits that can be removed without changing the requirement satisfaction, and "applied" credits that contribute to requirement satisfaction without excess. We would like to remark that there is a dearth of studies that seek to explain the credit hour usage pattern of university students. The paucity of studies can be attributed to the use of inflexible and/or opaque commercial degree audit tools at universities, which curtails the possible scope of analytics on degree audit data. The credit decomposition allows us to consider the usability and applicability of credit hours towards the student’s degree program, enabling us to take a step further than most analyses concerning credit hours. At US universities, a large number of degree-seeking undergraduate students graduate with a higher number of credit hours than is required for graduation, thus incurring "extra" credits. Excess and unusable credit categories make up the extra credits. Preliminary analysis reveals that excess credits are the dominant extra credit category. Naturally, analysis of excess credits as a function of time, i.e., the excess credit sequences, serves as the focal point of this paper. The results presented reveal interesting excess credit accumulation patterns that help explain some of the reasons behind excess credits. In particular, excess credit sequences for different student groups were used to investigate the widely held notion of “transfer credit loss” and “program (major) change” as significant contributors to extra credits. The results present a more nuanced view of this notion. Moreover, we propose a novel feature engineering method as a way to study cooperation between a student feature sequence (e.g., financial aid, program change, etc.) and an outcome feature sequence (e.g., excess credits). As a result, each relevant student feature sequence is mapped into a feature value that attempts to capture information that is relevant to the outcome. This enables a data-driven way to analyze the effect of a large number of student features on excess credit accumulation.