How Stable Is Early Academic Performance? Using Cluster Analysis to Classify Low Achievement and EF

Area of Science:

• Developmental Psychology
• Educational Psychology
• Cognitive Neuroscience

Background:

• Academic performance classification often relies on limited academic measures and arbitrary cutoffs.
• The role of cognitive skills, specifically executive function (EF), in classifying academic achievement needs further exploration.

Purpose of the Study:

• To investigate the utility of executive function (EF) measures in classifying academic performance in Kindergarten and first grade.
• To determine if EF measures can differentiate between performance levels in reading and mathematics.

Main Methods:

• Nonparametric cluster analysis was employed to group students based on academic performance.
• Cognitive measures of executive function (EF) were integrated into the classification process.
• Longitudinal tracking of performance groups from Kindergarten to first grade was conducted.

Main Results:

• EF measures effectively classified low reading performance in both Kindergarten and first grade.
• Mathematics performance was successfully categorized into low, average, and high groups without EF measures.
• Membership in high-performing groups showed greater stability into first grade compared to low or average groups.

Conclusions:

• Executive function (EF) deficits are more strongly associated with low academic performance than EF strengths are with high performance.
• Simultaneous assessment of academic and cognitive skills is crucial for accurate achievement classification.
• Existing classification schemes may be improved by incorporating cognitive measures alongside academic data.