معرفی
Laura Ringienė is a Researcher and Information Technology Administrator at Vilnius University's Institute of Data Science and Digital Technologies, where she works in the Interdisciplinary Statistical Research Group. She holds a PhD in Computer Science Engineering obtained in 2014 from Vilnius University's Institute of Mathematics and Informatics. Her professional activities span research, teaching, and technical administration within the academic environment.
Dr. Ringienė earned her educational credentials at Vilnius Pedagogical University, completing her Bachelor's degree in Informatics in 2006 and Master's degree in Informatics in 2008, both from the Faculty of Mathematics and Informatics. Her doctoral dissertation, Hybrid Neural Network for Multidimensional Data Visualization, was defended in 2014 and falls under Technological Sciences, Computer Science Engineering.
Her research focuses on statistical analysis of educational data, with particular expertise in multilevel regression, structural equation modeling, and analysis of international large-scale assessment data (PISA, TIMSS, PIRLS). She has developed the R program package EFECTAS specifically designed for analyzing international education research data. Her work examines educational equity, reading achievement gaps across EU countries, socioeconomic factors affecting student performance, and the impact of preschool education on later academic outcomes.
Analysis of her publication record from 2020-2025 reveals a strong concentration on educational data science, with recurring themes of statistical modeling of international assessment data, educational equity analysis, and application of advanced statistical techniques to understand educational systems across Europe. Her work frequently employs PISA, TIMSS, and PIRLS datasets to examine cross-national educational patterns and policy implications.
Dr. Ringienė has been actively involved in the EFECTAS project (2018-2022), which was financed by the European Social Fund under a grant agreement with the Research Council of Lithuania. This project focused on analyzing the efficiency and productivity of education systems in EU countries using secondary large-scale data.
She teaches undergraduate courses at Vilnius University, including Big Data Analytics and Business Process Robotization since 2019. Her professional development includes extensive training in Robotic Process Automation (UIPath), Structural Equation Modeling, Multilevel Linear Modeling, and various Big Data technologies. She is part of the Interdisciplinary Statistical Research Group, which focuses on applying advanced statistical methods to solve complex problems in education and other domains.
