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

Area of Science:

• Educational Psychology
• Nursing Education

Background:

• Undergraduate nursing programs require mastery of human anatomy and physiology.
• Identifying student achievement levels early is crucial for targeted academic support.

Purpose of the Study:

• To classify undergraduate nursing students into high-achieving (HA) and low-achieving (LA) clusters using assessment data.
• To evaluate the discriminative power of summative assessments between HA and LA student groups.
• To identify students requiring additional instruction and effective assessment tools.

Main Methods:

• Cluster analysis was applied to assessment scores from 157 undergraduate nursing students across two compulsory human anatomy and physiology courses.
• Five summative assessment tasks per course served as input variables for the cluster analysis.
• Student performance was categorized into HA and LA clusters for each semester.

Main Results:

• In semester 1, clusters were HA1 (n=42) and LA1 (n=115); in semester 2, they were HA2 (n=91) and LA2 (n=42).
• Significant differences (P < 0.001) were observed between HA and LA group means for all assessment tasks in both semesters.
• A substantial number of students transitioned between clusters (19 from HA1 to LA2, 68 from LA1 to HA2), indicating dynamic achievement patterns.

Conclusions:

• Cluster analysis effectively categorizes students based on academic achievement in anatomy and physiology.
• The study highlights variations in assessment effectiveness and the importance of tracking student progress over time.
• This data-driven approach can inform pedagogical strategies and identify at-risk students for timely interventions.