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The COVID-19 pandemic brought immense challenges to global society. The entire social and work life had to be reorganized to accommodate for the restrictions imposed to limit the spread of COVID-19. These restrictions affected the eduFIGation worldwide as well. Face-to-face education was disrupted and alternatives had to be found. One of the questions raised with the disruption was the student outcomes in the times when the usual teaching and learning was disrupted by the lockdowns and remote classes took place. There are not many studies on the topic in Slovenia but, more importantly, the existing ones do not use representative data to investigate the depth of the problem. The aim of this article is to fill this gap in research through comprehensive and in-depth analyses of the Slovenian student, school and teacher data from the international Responses to Educational Disruption Survey (REDS), conducted in 2020/2021. The data analysis involves descriptive and multivariate statistical methods appropriate for the mostly categorical data available from REDS. The results show that students' perception on their learning and academic outcomes during the school disruptions depend on their background characteristics, i.e. the disruptions did not affect all students equally. These results are also supported by the findings from school principals' and teachers' data. In addition, the long-lasting issue of "grade inflation" in Slovenia has become even more severe, as shown by both student and teacher data. However, students and teachers are at contrasting opinions about student learning--while most of the students think they learned more at home during the disruptions and have shown more progress, teachers are of the opposite opinion. Furthermore, teachers tended to grade students' academic outcomes higher during the disruptions which has increased the issue of "grade inflation" in Slovenia.

This paper presents the R Analyzer for Large-Scale Assessments (RALSA), a newly developed R package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA's Trends in International Mathematics and Science Study and the OECD's Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, RALSA is the first comprehensive R package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native R data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in R and operates in any operating system where a full copy of R can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of RALSA allows for the quick addition of new studies, analysis types and features to the existing ones.

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