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Predicting Students’ Metacognitive Reading Skills: A Multidimensional Analysis Using the PISA 2018 Data Set

Fri, April 12, 9:35 to 11:05am, Philadelphia Marriott Downtown, Floor: Level 3, Room 305

Abstract

This study examined the predictive power of students’ demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students’ reading scores. The dataset included 612,004 students in 80 countries who completed the 2018 PISA assessment. We tested three random forest models, each tailored to a specific metacognitive skill, revealing differing fits. Findings indicate that students’ self-guided reading strategies and socioeconomic status had highest predictive power, grade repetition and teacher-guided activities showed less impact. These findings have important implications for research, policy, and practice. We apply machine learning to identify best-fitting models for each metacognitive skill and compare the findings with the baseline models.

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