Data Envelopment Analysis (DEA) in the Educational Sciences.

Area of Science:

• Educational research
• Organizational management
• Quantitative analysis

Background:

• Traditional analytical models in education focus on explained variance and variable importance, offering limited insight into individual performance.
• Data envelopment analysis (DEA), a method from organizational management, provides a different perspective by evaluating individual efficiency.

Purpose of the Study:

• To introduce and demonstrate the application of Data Envelopment Analysis (DEA) in the field of education.
• To assess individual student efficiency in utilizing reading skills for reading comprehension.
• To identify underutilized skills and classify new reader subsets for targeted interventions.

Main Methods:

• Review of DEA history and applications in non-educational fields.
• Mathematical and procedural explanation of DEA.
• Application of DEA to the Project KIDS dataset (n=1987) using a reading ability framework.

Main Results:

• DEA successfully identified individual efficiency levels in reading skill utilization among students.
• The analysis pinpointed specific reading skills that were underutilized by students.
• Novel reader subsets were classified based on their efficiency profiles.

Conclusions:

• DEA is a viable and insightful method for educational research, moving beyond explained variance to individual efficiency.
• Identifying underutilized skills through DEA can inform the development of more precise and effective educational interventions.
• The classification of new reader subsets has significant implications for personalized learning and support strategies.