ERIC Number: ED664846
Record Type: Non-Journal
Publication Date: 2024-Apr-12
Pages: 26
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Predicting Students' Metacognitive Reading Skills: A Multidimensional Analysis Using the PISA 2018 Data Set
Nirmal Ghimire; Kouider Mokhtari
AERA Online Paper Repository, Paper presented at the Annual Meeting of the American Educational Research Association (Philadelphia, PA, Apr 11-14, 2024)
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.
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students, Metacognition, Reading Skills, Data, Predictor Variables, High School Students, Student Attitudes, Student Motivation, Reading Achievement, Data Analysis, Reading Instruction, Teaching Methods
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Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Secondary Education; High Schools
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Assessments and Surveys: Program for International Student Assessment
Grant or Contract Numbers: N/A