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Laboratory based learning activities constitute an important part of students’ learning experiences today. As a consequence, it is interesting to investigate how to better support such activities using digital technologies. In this paper, the authors present a practical approach based on the use of Microsoft Families and artificial neural networks to analyse computer traces of students’ lab activities to identify students encountering difficulties and at risk of failure, and flag tutors for further corrective actions on demand. This work demonstrates how artificial neural networks allow the analysis of the traces of students’ work even outside of a Learning Management System and with no clue of possible algorithmic relationships between the traces and the students’ performance.
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