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Although Macau students have consistently been recognized as top performers in international assessments, little research has been conducted to explore the various factors that are associated with their achievement. This paper aimed to identify factors that could best predict Macau students' reading achievement using PISA 2018 data provided by 2,979 15-year-old students. An integrative theoretical model that considered the critical roles of demographic, personal and social-contextual factors was used to understand the relative importance of 41 different factors in predicting reading achievement. A machine learning approach, specifically Random Forest Algorithm, was used to analyse the data. Results indicated that variables classified under personal factors (e.g., metacognitive strategies, reading enjoyment and perceived difficulty) were the most important predictors of Macau students' achievement. A supplementary analysis using Hierarchical Linear Modelling confirmed the findings from the machine learning approach. Implications of the findings were discussed. [Spanish translation by Mercè Rius.]

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The present paper analyses the artificial intelligence future impact on mandatory representation in spanish civil proceedings. Firstly, the work studies the reasons for mandatory representantion and the constitucional limits to self-representation. Secondly, it analyses if artificial intelligence could condicionate these factors and what would be the adequate procedural acts to allow self-representantion with IA assistance. Finally, the work studies the disadvantages and perils which could create the conjuction between self-represented litigants and IA tools. [ABSTRACT FROM AUTHOR]

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