School closures and effective in-person learning during COVID-19

Area of Science:

• Education Policy
• Public Health
• Data Science

Background:

• COVID-19 pandemic disrupted traditional schooling globally.
• Existing trackers for in-person, hybrid, and remote learning exhibit significant discrepancies.
• Accurate measurement of in-person learning is crucial for policy evaluation.

Purpose of the Study:

• To highlight inconsistencies in current educational tracking methods during the COVID-19 pandemic.
• To introduce a novel, more accurate measure of effective in-person learning (EIPL).
• To analyze factors influencing in-person learning disparities using the new EIPL metric.

Main Methods:

• Documented temporal and geographical variations among prominent U.S. schooling trackers.
• Developed the effective in-person learning (EIPL) metric by integrating schooling modes with cell phone mobility data.
• Estimated EIPL for a large, representative sample of U.S. public and private schools.

Main Results:

• The proposed EIPL measure resolves discrepancies found in existing trackers.
• Confirmed associations between less in-person learning and higher shares of non-white students, lower pre-pandemic grades, and larger school size.
• Found lower EIPL in affluent, educated localities with higher pre-pandemic spending and emergency funding, largely due to regional political differences.

Conclusions:

• The EIPL metric offers a more reliable approach for quantifying in-person learning.
• Educational disparities during the pandemic were influenced by student demographics, school resources, and socio-political factors.
• Systematic regional differences, particularly political preferences, played a significant role in in-person learning variations.