Sue White

Approaches to study in undergraduate nursing students in regional Victoria, Australia.

Abstract In developmental research to devise a strategy to identify students who may benefit from assistance with learning habits, approaches to study were explored in undergraduate nursing students (n=122) enrolled in a compulsory first-year course in physiology at a regional Australian university. The course constituted 30 credits (25%) of their first year of study. Using the Approaches and Study Skills Inventory (ASSIST), students were identified as adopting a deep (n=38, 31%), strategic (n= 30, 25%), or a surface (n=54, 44%) approach to study. Internal consistency (Cronbach’s alpha [α]) for deep, strategic, and surface was 0.85, 0.87, and 0.76, respectively. Subsequently, a cluster analysis was done to identify two groupings: a “surface” group (n=53) and a “deep/strategic” group (n=69). The surface group scored lower in deep (33.28±6.42) and strategic (39.36±6.79) approaches and higher in the surface (46.96±9.57) approach. Conversely, the deep/strategic group scored 46.10±6.81, 57.17±7.81, and 41.87±6.47 in deep, strategic, and surface styles, respectively. This application of the ASSIST questionnaire and cluster analysis thus differentiated students adopting a surface approach to study. This strategy may enable educators to target resources, for example additional tutorial opportunities, peer-assisted study support, and tutor-led seminar sessions aimed at encouraging students to adopt a less superficial approach to study.

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 (HA1 and LA1, respectively) and HA (n = 91) and LA (n = 42) in semester 2 (HA2 and LA2, respectively). In both semesters, HA and LA means for all inputs were different (all P < 0.001). Nineteen students moved from the HA1 group into the LA2 group, whereas 68 students moved from the LA1 group into the HA2 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.