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Computational thinking of socially disadvantaged pupils in ICILS data
KLOKOČKA, Matěj
This paper focuses on the analysis of data from the ICILS international comparative survey. The key objectives of my work were to divide the students in this research into two groups according to their social background and then to determine whether there are differences in computational thinking between these groups. Furthermore, I sought to determine whether these differences persisted within the different components of computational thinking. Approximately 46,000 students and 26,000 teachers from 13 countries participated in the ICILS 2018 research. The identification of pupils into groups was based on the variable of socio-economic background, which was calculated by the authors of the ICILS research from the questionnaire that was part of it. The research population was split so that the socially disadvantaged group represented the 5 % of pupils with the lowest socio-economic background index value. The first group was named "Socially Disadvantaged Pupils" for this paper and are referred to in the research as index "1". The second group is named "Other pupils" and they are denoted by the index "0". Statistical methods appropriate to the structure and scale of the data are used in the analysis, and weights have been applied to adequately balance the samples. The Mann-Whitney U test was used to statistically support differences between groups. To achieve these objectives, the following analytical tools were used: MS Excel for data visualization, IBM SPSS Statistics for statistical testing and manipulation of data sets, and IEA IDB Analyzer for comprehensive data analysis with respect to their original weights. The results of the analysis show that socially disadvantaged students scored lower than others in the area of computational thinking. This observation was statistically supported. During the research, groups of questions were identified that corresponded to two components of computational thinking. Subsequently, comparisons were made between the scores of socially disadvantaged and other students on these groups of questions. The results showed that on average, socially disadvantaged pupils scored worse than other pupils in each group of questions, which was also statistically supported. This finding highlights the need for specific educational interventions and strategies aimed at supporting these pupils. The results of this work offer valuable information for further research in the area of digital literacy.
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