Tracking undergraduate student achievement in a first-year physiology course using a cluster analysis approach

A cluster analysis data classification technique was used on assessment scores from 157 undergraduate nursing students who passed 2 successive compulsory courses in human anatomy and physiology. Student scores in five summative assessment tasks, taken in each of the courses, were used as inputs for a cluster analysis procedure. We aimed to group students into high-achieving (HA) and low-achieving (LA) clusters and to determine the ability of each summative assessment task to discriminate between HA and LA students. The two clusters identified in each semester were described as HA (n = 42) and LA (n = 115) in semester 1 (HA(1) and LA(1), respectively) and HA (n = 91) and LA (n = 42) in semester 2 (HA(2) and LA(2), respectively). In both semesters, HA and LA means for all inputs were different (all P < 0.001). Nineteen students moved from the HA(1) group into the LA(2) group, whereas 68 students moved from the LA(1) group into the HA(2) group. The overall order of importance of inputs that determined group membership was different in semester 1 compared with semester 2; in addition, the within-cluster order of importance in LA groups was different compared with HA groups. This method of analysis may 1) identify students who need extra instruction, 2) identify which assessment is more effective in discriminating between HA and LA students, and 3) provide quantitative evidence to track student achievement.