The impact of reading metacognitive strategies on mathematics learning efficiency and performance: An analysis using PISA 2018 data in China

Abstract

The current study employed latent profile analysis to examine the application patterns of students' reading metacognitive strategies using PISA 2018 data in China. Subsequently, it explored the differences in students' mathematics learning efficiency and performance. The results revealed that: (1) Six types of reading metacognitive strategies application patterns were identified: “Novice - indifferent,” “Veteran - average,” “Novice - low evaluating,” “Veteran - skilled,” “Novice - mixed,” and “Novice - arbitrary.” (2) The primary factors that affect the classification of reading metacognitive strategies application patterns were gender, and family economic, social, and cultural statuses (ESCS). (3) Mathematics learning time could positively predict performance overall, but the mathematics learning time of “Veteran - skilled” and “Novice - mixed” students had no significant correlation with their mathematics performance. The findings suggests that educators should not blindly increase students' mathematics learning time but instead provide appropriate guidance based on their mastery patterns of reading metacognitive strategies to enhance mathematics learning efficiency and performance.

Keywords

• Mathematics learning efficiency
• Mathematics performance
• Reading metacognition
• PISA
• Potential profile analysis