Problem Solving and Learning Analytics in Mathematics: Design of an Indexing System Based on the Guidelines of the Programme for International Student Assessment (PISA)

Abstract

Mathematics education has shifted towards a competency-based approach, where learning is understood as students’ ability to interpret, model and solve problem situations in real-world contexts, in alignment with PISA guidelines. From this perspective, learning analytics assumes a significant role by enabling a deeper understanding of how students learn, through the analysis of data that reveals their learning trajectories, difficulties and modes of engagement with knowledge. The study, grounded in a documentary review of an analytical–interpretative nature, was aimed at critically examining the theoretical foundations related to problem solving and learning analytics, in order to establish conceptual criteria for an indexing system, without addressing its implementation. The findings highlight the persistence of difficulties in key processes such as comprehension, modelling and validation, which affect performance in standardised assessments. In this context, there is a need to move towards more comprehensive forms of assessment that recognise not only outcomes but also the processes that shape mathematical learning.