Predicting Factors that Affect East Asian Students’ Reading Proficiency in PISA


Low, Adeline Hui Min and Lim, Amy Hui Lan and Chua, Fang Fang (2023) Predicting Factors that Affect East Asian Students’ Reading Proficiency in PISA. JOIV : International Journal on Informatics Visualization, 7 (3-2). p. 2065. ISSN 2549-9610

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Teachers, schools, and parents contribute to equipping students with essential knowledge and skills during their education years. When students are approaching the end of their education, they are randomly selected to participate in Program for International Student Assessment (PISA) to assess their reading proficiency. Existing work on analyzing PISA achievement results concentrates solely on identifying factors related to Parent or in combination with Student. Limited work has been proposed on how factors related to Teacher and School affect the students’ reading proficiency in PISA. This study focuses on identifying the factors related to Teacher and/or School that affect East Asian students’ reading proficiency in PISA. The PISA achievement results from East Asian students are chosen as the domain study because they are consistently the top performers in PISA in the past decade. Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbors (KNN) and Random Forest (RF) are compared. Hamming score is used as the evaluation metric. The results indicate that RF produces the best predictive models with highest Hamming score of 0.8427. Based on the findings, Schoolrelated factors such as the number of school’s disciplinary cases, size of the school, the availability of computers with Internet facilities, the quality and educational qualifications of teachers have higher impact on the PISA achievement results. The identified factors can be used as a reference in assessing the current school’s teaching, learning environment, and organizing extra activities as part of intervention programs to cultivate reading habits and enhance reading abilities among students.

Item Type: Article
Uncontrolled Keywords: Data mining, PISA, reading domain, teacher.
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 27 Mar 2024 02:43
Last Modified: 27 Mar 2024 02:43


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