Data-driven decision-making in emergency remote teaching

Maya Botvin, Arnon Hershkovitz, Alona Forkosh-Baruch

Research output: Contribution to journalArticlepeer-review


Decision-making is key for teaching, with informed decisions promoting students and teachers most effectively. In this study, we explored data-driven decision-making processes of K-12 teachers (N = 302) at times of emergency remote teaching, as experienced during the COVID-19 pandemic outbreak in Israel. Using both quantitative and qualitative methodologies, and a within-subject design, we studied how teachers' data use had changed during COVID-19 days, and which data they would like to receive for improving their decision-making. We based our analysis of the data on the Universal Design of Learning (UDL) model that characterizes the diverse ways of adapting teaching and learning to different learners as a means of understanding teachers' use of data. Overall, we found a decline in data use, regardless of age or teaching experience. Interestingly, we found an increase in data use for optimizing students' access to technology and for enabling them to manage their own learning, two aspects that are strongly connected to remote learning in times of emergency. Notably, teachers wished to receive a host of data about their students' academic progress, social-emotional state, and familial situations.

Original languageEnglish
Pages (from-to)489-506
Number of pages18
JournalEducation and Information Technologies
Issue number1
StatePublished - Jan 2023


  • COVID-19
  • Data-driven decision-making
  • Emergency remote teaching
  • Teachers
  • Universal design for learning

All Science Journal Classification (ASJC) codes

  • Education
  • Library and Information Sciences


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