Assessing the impact of hybrid teaching on students’ academic performance via multilevel propensity score-based techniques

Abstract

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 implemented in response to Covid-19 pandemic. 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 preceding the Covid-19 waves. Tailored multilevel models for earned credits and grade point averages are fitted onto meticulously constructed dataframes, minimizing potential biases stemming from variables such as gender, age, previous instruction, admission scores, and student origins across the two groups. Accounting for program variations in the methodology, our findings suggest marginal overall disparities in student performance, indicating, on average, a subtle rise in earned credits and a slight decrease in grade point averages for those exposed to hybrid teaching. Multilevel models reveal significant program-specific variations, offering valuable insights into the effectiveness of hybrid teaching in diverse educational contexts.