COMPARATIVE STATISTICAL ANALYSIS OF ACHIEVEMENT GAPS ACROSS FIVE PIRLS CYCLES
1 Vilnius University, Institute of Data Science and Digital Technologies (LITHUANIA)
2 Vilnius University, Faculty of Philosophy (LITHUANIA)
3 Ministry of Education, Science and Sport (LITHUANIA)
About this paper:
Conference name: 18th International Conference on Education and New Learning Technologies
Dates: 29 June-1 July, 2026
Location: Palma, Spain
Abstract:
This paper analyses long-term trends in the reading achievement gap between high- and low-performing students (90th and 10th percentiles) across 12 EU and EEA countries. Using data from five cycles of the Progress in International Reading Literacy Study (PIRLS) from 2001 to 2021, the study examines how these performance disparities have evolved among fourth-grade students.
The study analyses long-term trends in the 10th and 90th percentiles and achievement gaps using multilevel growth models and the Sen slope, supplemented by the Mann-Kendall test to determine the significance of trends. Changes in achievement gaps are classified as favorable or unfavorable.
An analysis of PIRLS data from 12 EU and EEA countries reveals a widening achievement gap. The progress of the lowest-achieving students in 25 to 75% of countries led to a shrinking of the gap. However, in 63-71% of countries, the main reason for the widening of the gap was a decrease in the 10th percentile. Furthermore, statistical analysis did not reveal a significant correlation between a country's average performance and the achievement gap.
In the analysed countries, inequality in reading achievement among students is increasing, indicating that the policy measures implemented over two decades have failed to reduce educational inequality. The study confirms that isolated gap analysis is insufficient; it is necessary to assess the causality of changes to identify statistically unfavorable decreases or favorable increases. Furthermore, growth in a country's average achievements does not, in itself, indicate a reduction in inequality, so these indicators should be treated as independent variables.Keywords:
PIRLS, achievement gap, trend analysis.