Conference Agenda

Low graduation rates and high dropout rates remain persistent challenges in STEM programs worldwide, often associated with a combination of demographic, academic, and institutional factors. While many studies focus on individual-level predictors of student success, fewer approaches analyze academic trajectories from cohort and organizational perspectives.

This study examines student academic trajectories in engineering programs at Universidad Austral de Chile using anonymized institutional records from 2004–2015. The research combines database technologies, statistical analysis, visualization tools, and Social Network Analysis (SNA) to identify structural patterns within student cohorts. Semi-structured interviews with academic stakeholders are conducted to interpret these patterns and relate them to institutional practices and program characteristics.

The analysis reveals distinct structural patterns in students’ academic trajectories across engineering programs and recurrent configurations associated with retention and dropout dynamics. Network-based visualizations highlight differences between programs and support discussions with stakeholders, enabling deeper insights into mechanisms influencing student progression.

These results illustrate how network-based analysis can complement traditional approaches to studying academic trajectories and contribute to emerging curricular analytics methodologies that support data-informed curriculum management and institutional decision-making.