2022
DOI: 10.1371/journal.pone.0267040
|View full text |Cite
|
Sign up to set email alerts
|

PISA data clusters reveal student and school inequality that affects results

Abstract: The data from the PISA survey show that student performance correlates with socio-economic background, that private schools have higher results and more privileged students, and that this varies between countries. We explore this further and analyze the PISA data using methods from network theory and find clusters of countries whose students have similar performance and socio-economic background. Interestingly, we find a cluster of countries, including China, Spain and Portugal, characterized by less privilege… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(3 citation statements)
references
References 38 publications
(36 reference statements)
0
3
0
Order By: Relevance
“…This identification allows further in-depth investigation into these systems to shed light on possible practices and policies that lead to educational improvement. To explore the growing range of information derived from modern LSA, new techniques have been proposed [ 9 ] to add to the traditional statistical tools [ 4 , 15 , 50 ]. This paper aims to contribute to this literature by presenting a new approach based on the ML-supervised learning paradigm.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This identification allows further in-depth investigation into these systems to shed light on possible practices and policies that lead to educational improvement. To explore the growing range of information derived from modern LSA, new techniques have been proposed [ 9 ] to add to the traditional statistical tools [ 4 , 15 , 50 ]. This paper aims to contribute to this literature by presenting a new approach based on the ML-supervised learning paradigm.…”
Section: Discussionmentioning
confidence: 99%
“…This framework compares observed student achievement with predicted student achievement given the contextual educational system particularities and estimating the so-called “effectiveness metric.” Technically, this metric is often estimated by means of residuals of hierarchical linear models (HLMs) using the educational production function (EPF) [ 8 ], in which the LSA scores are the output and socioeconomic variables inputs. Recently, [ 9 ] used socioeconomic variables to build educational system clusters and, using network theory from the ecology field, assessed how the correlation between LSA score and socioeconomic background varies across countries to leverage insights into the success of local compensatory policies.…”
Section: Introductionmentioning
confidence: 99%
“…For the purposes of data collection, high-performing high school was defined as a district that reflects top 10 percent performance scores based on that state's standardized testing. This is important to the research because higher performing schools tend to reflect two criteria: (1) They are located in at least middle-income communities where parents are considered socioeconomically privileged (Magnus, 2022) and (2) they tend to have a larger percentage of well-paid, experienced teachers with over 10 years of experience, again, speaking to the privilege and resources of the school systems (Etim et al, 2020).…”
Section: Parameters Of the Studymentioning
confidence: 99%