Assessing the Impact of Hybrid Teaching on Students' Academic Performance via Multilevel Propensity Score-based techniques
Monday 25th September 2023
Ragni, A.; Ippolito, D.; Masci, C.
This study employs multilevel propensity score techniques in an innovative analysis pipeline to assess the impact of hybrid teaching – a blend of face-to-face and online learning – on student performance within engineering programs at Politecnico di Milano. By analyzing students' credits earned and grade point average, the investigation compares outcomes of students engaged in hybrid teaching against those solely in face-to-face instruction that precedes the Covid-19 pandemic. Tailored multilevel models for earned credits and grade point averages are fitted onto meticulously constructed dataframes, effectively minimizing potential biases stemming from variables such as gender, age at career initiation, previous academic track, admission test scores, and student origins across the two groups. The methodology accounts for variations across distinct educational programs and investigates disparities among them. Our findings suggest marginal overall disparities in student performance, indicating, on average, a subtle inclination toward a modest rise in earned credits and a slight decrease in grade point averages among those exposed to hybrid teaching. The use of multilevel models to analyze data within the same institution revealed that the impact of hybrid teaching on students' performances can vary significantly across different engineering programs, providing valuable insights into its effectiveness in diverse educational contexts.