A Bayesian Network Model for Supporting School Managers Decisions in the Pandemic Era

Area of Science:

• Educational Technology
• Public Health
• Organizational Management

Background:

• The COVID-19 pandemic necessitated extended emergency remote teaching in Italy, exposing significant digital divides.
• This situation accelerated digital transformation in education, presenting unique challenges for school modernization.

Purpose of the Study:

• To analyze the impact of emergency remote teaching on Italian schools from multiple stakeholder perspectives.
• To introduce a multivariate statistical method to aid school managers in identifying and addressing internal process challenges.
• To support policy development for educational resilience and modernization.

Main Methods:

• Multivariate statistical analysis applied to data collected during the Italian lockdown.
• Multi-actor research involving various stakeholders in the educational system.

Main Results:

• A decision-making model was developed to assist school managers.
• The model helps identify key challenges in adapting internal school processes.
• The study demonstrates the utility of statistical methods in educational management.

Conclusions:

• The experience of emergency remote teaching can drive organizational process improvements.
• Statistical modeling offers a valuable tool for school managers to enhance strategic decision-making.
• Addressing the digital divide is crucial for effective educational modernization.