Strategy variability in computational estimation and its association with mathematical achievement.

Area of Science:

• Cognitive Psychology
• Mathematics Education

Background:

• Computational estimation involves diverse strategies, with effectiveness depending on problem characteristics.
• Understanding strategy selection is crucial for improving estimation skills.

Purpose of the Study:

• To investigate strategy variability in young adults using the Test of Estimation Strategies (TES).
• To identify factors influencing the selection and application of estimation strategies.

Main Methods:

• Administered the TES, comprising 20 arithmetic problems, to adult participants.
• Categorized participants into high and average estimation performance groups.
• Analyzed strategy use, breadth, and relevance in relation to performance and achievement.

Main Results:

• High-performance estimators utilized a wider range of strategies and selected more relevant ones.
• Estimation accuracy correlated with mathematical achievement, strategy breadth, and relevance, but these links diminished after controlling for verbal achievement.
• The TES successfully elicited target strategies and indicated a potential new strategy, Partitioning.

Conclusions:

• Effective computational estimation relies on flexible strategy use and relevance.
• Verbal achievement plays a significant role in estimation performance.
• Findings support future instructional interventions to enhance students' estimation abilities.