Data Science and Machine Learning in Secondary Education: A Case Study of Barriers and Opportunities at Cambine Secondary School, Mozambique

Abstract

This study examines the potential of Data Science (DS) and Machine Learning (ML) to transform teaching and learning at Cambine Secondary School, a rural institution in Inhambane Province, Mozambique, characterized by limited technological infrastructure. Using a concurrent mixed-methods case study design, data were collected through semi-structured interviews with two school directors and nine teachers, alongside structured questionnaires administered to sixty students and nine teachers, analyzed thematically and through descriptive and inferential statistics. The findings reveal substantial barriers, including unreliable or absent internet connectivity, a shortage of computers, near-total absence of virtual learning environments, and low teacher familiarity with DS/ML concepts, with only a quarter of teachers reporting that they understand them. Nevertheless, participants expressed strong interest: most students want to learn more about DS and ML, and most teachers believe such tools could support personalized learning. Inferential comparisons between the two groups showed significant differences in perceived internet adequacy and in familiarity with DS/ML concepts (p < .05), while perceptions of resource adequacy and optimism about the personalizing potential of these tools were broadly shared. The study concludes that, despite significant infrastructural and training gaps, a coordinated strategy combining infrastructure investment, continuous teacher training, curriculum integration, and public-private partnerships could enable DS and ML to meaningfully improve teaching quality and school management. These findings contribute to the broader debate on digital transformation in resource-constrained settings and offer practical recommendations aligned with Mozambique’s national education strategy and international frameworks such as Sustainable Development Goal 4.