Factors associated with academic resilience in disadvantaged students: An analysis based on the PISA 2015 B-S-J-G (China) sample

Abstract

Academic resilience is evident in students who are living in vulnerable environments, yet achieve success in academic outcomes. As a result, substantial attention has been devoted to identifying the factors associated with academic resilience and supporting students to be resilient. This study used the Classification and Regression Tree and Multilevel Logistic Regression modeling to identify the potential factors related to students’ academic resilience. Using these tools, the study analyzed the B-S-J-G (China) sample in PISA 2015. The variables that significantly predicted whether a student is disadvantaged and resilient (DRS) or not resilient (DNRS) were shown to be: Proportion of teachers in school with master’s degrees, Proportion of teachers in school with bachelor’s degrees, Environmental awareness, Science learning time per week, Number of learning domains with additional instruction, and Students’ expected occupational status. These findings may enlighten governments, teachers, and parents on ways to assist students to be resilient.

A systematic review of academic resilience in East Asia: Evidence from the large‐scale assessment research

“…Structural equation modeling had the lowest utility rate ( n 4 = 1, 2.22%) to explore academic resilience in the LSA research. Interestingly, educational data mining techniques were used in cooperation with traditional logistic regression (e.g., Jin et al, 2022; She et al, 2019).…”

“…There was a methodological gap between international and national/regional LSA research. Concerning international LSA studies, researchers commonly adopted a person‐centered lens (i.e., definition‐driven method) (e.g., Cheung, 2017; Jin et al, 2022). However, researchers tended to employ a variable‐focused perspective (i.e., process‐driven method) in national/regional LSA studies (e.g., Chao et al, 2018; Lee et al, 2020).…”

More money does not necessarily help: relations of education expenditure, school characteristics, and academic resilience across 36 education systems

“…For example, the PISA data does not associate students with their teachers because students were randomly selected from sample schools (OECD, 2020). Thus, studies using PISA data to explore teacher factors tend to examine them at the school level, for example, the proportion of qualified teachers in a school (Jin et al, 2022). This has limited the depth of knowledge regarding the influence of teachers and teaching quality on academic resilience.…”