Data-driven decision-making in emergency remote teaching

Area of Science:

• Education
• Educational Psychology
• Information Science

Background:

• Effective decision-making is crucial for optimizing teaching and learning outcomes.
• The COVID-19 pandemic necessitated a shift to emergency remote teaching (ERT) for K-12 educators.
• Understanding teachers' data-driven decision-making (DDDM) during ERT is vital for pedagogical support.

Purpose of the Study:

• To investigate how K-12 teachers' data use changed during ERT.
• To identify specific data types teachers desired for enhancing their decision-making during ERT.
• To analyze teachers' DDDM through the lens of the Universal Design of Learning (UDL) model.

Main Methods:

• Employed a mixed-methods approach combining quantitative and qualitative data.
• Utilized a within-subject design to track changes in data use over time.
• Surveyed 302 K-12 teachers in Israel regarding their experiences with data use during the COVID-19 pandemic.

Main Results:

• A general decline in overall data use was observed among teachers, irrespective of experience or age.
• An increase in data use was noted for optimizing student technology access and promoting self-regulated learning.
• Teachers expressed a strong desire for comprehensive data on students' academic progress, social-emotional well-being, and home environments.

Conclusions:

• Emergency remote teaching contexts significantly impacted K-12 teachers' data-driven decision-making practices.
• The UDL framework provides valuable insights into how teachers adapt data use to diverse learner needs in remote settings.
• Future interventions should focus on providing teachers with targeted data to support student learning and well-being in evolving educational landscapes.