Real or fake? Connecting student learning and grades

Graduation rates have steadily increased since the 1990s (Denning et al., 2022). On the surface, this upward trend provides an optimistic view of the state of education; students are successfully reaching the standards necessary to obtain their degree, indicating that they are adequately prepared to enter the workforce and become productive members of society. Individuals with college degrees have been shown to receive higher incomes, have healthier lifestyles, and be more active in their communities (College Board, 2023). While this increase in collegiate success should ultimately mean an increase in student learning, suspicions have arisen as to whether this is truly the case. The upward trend in graduation rates observed over the past few decades is not sufficient to conclude that students are truly learning. In higher education, we make the explicit assumption that Grade Point Average (GPA) is a direct measure of student learning/achievement. Grades assigned to students are deemed to be reflective of demonstrated learning. Importantly, GPA is directly tied to graduation status, as students are required to obtain a certain GPA to graduate college. However, GPA has the potential to be susceptible to grade inflation (i.e., an increase in grades awarded without a subsequent increase in achievement). When grade inflation is present, grades begin to show a ceiling effect as they increase to a maximum (Kamber & Biggs, 2003). As the number of students receiving grades of A grows due to grade inflation, it becomes increasingly difficult to distinguish amongst those whose grades are valid versus those whose grades have been inflated. The question becomes, how do we determine if grade inflation is occurring? Said differently, how do we determine whether GPA is related to student learning and achievement? To be able to demonstrate true student learning across time, a measure of learning administered to the student body is necessary across multiple time points. Unfortunately, most colleges/universities are not able to access or implement such a measure. Fortunately for James Madison University, such measures are administered on a regular basis. The current study utilized longitudinal student learning data from James Madison University’s biannual Assessment Day to determine if GPA could be predicted by student learning while controlling for student demographics. Change scores on the Natural World Test, Version 9 (NW9) were used as an indicator of student learning over time. A multiple regression model containing NW9 Change Scores, NW9 Fall Exam Score, Sex, and Race was significant, with NW9 Change Scores significantly contributing to the prediction of GPA. Implications, limitations, and directions of future study are described.