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With one of the lowest Latin-American secondary school completion rates, Uruguay aims for continuous and complete educational trajectories. To support this national goal, we set up a multiple-methods study. Thematic analysis is used to understand principals’ and teachers' data needs to support students’ trajectories. Stakeholders' concerns on the relation between students’ dropout risk, their trajectories and academic performance guide the design of a follow-up quantitative study. Sequence, cluster and regression analyses, together with machine learning techniques are conducted on a longitudinal database of pre-test 2013-2018 trajectories of a national sample of public 9th-grade large-scale test participants. Results show students pre-test trajectories can be classified in four groups, which are correlated with reading and math test performance.