International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis and built decision trees (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n= 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools’ mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies.

The emergence of the Covid-19 pandemic has accelerated the wave of digital social transformation worldwide and pushed the “Accelerator Key” for the digital transformation of education in 2020. This transformation has also impacted in an all-around way in China. Taking Anhui province as a case study, this research explores socio-demographic factors influencing the digital competence level of pre- and in-service teachers of primary and secondary education in China. The quantitative methodological approach emphasizes the study subjects’ perception of their digital competencies in three factors: basic technology literacy, technical support learning, and technical support teaching. The study involved 250 pre-service teachers and 248 in-service teachers. The main findings are: (1) participants have good consciousness and attitudes towards using ICT in daily work, but their educational practice is weak; (2) in-service teachers have a digital competence level generally higher than pre-service teachers’, which might be their professional practice promote them to reflect on perceptions and attitudes regarding technological education; (3) for in-service teachers, there are significant differences between their digital competence level and age, years of teaching experience, educational background; (4) current ICT courses have no influencing on in-service teachers’ digital competence level, implying that current ICT training system may have problems. The study provides insights to improve pre-service teachers’ digital competence education in universities and develop well-designed in-service teachers’ ICT training courses.