INSIGHT: Combining Fixation Visualisations and Residual Neural Networks for Dyslexia Classification From Eye-Tracking Data.

Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase.

Relational and interactional dynamic network data from Czech lower-secondary school students.

This article introduces a network data set on the friendships and group work interactions among a convenience sample of 276 Czech Grade 6 students from twelve classrooms, supported by student-level demographic, literacy, motivation, and classroom communication data. Gathered longitudinally at the beginning and end of the 2021/2022 school year, the data provide a relational insight into the nature and evolution of early adolescents' friendships. Moreover, the data provide a unique relational and temporal insight into the verbal interaction of students during classroom group work.