ABSTRACT
Teachers’ ability to notice relevant classroom situations is crucial to teaching students effectively. This ability is acquired during teacher education and shaped during the teaching profession. It is essential to assess teachers’ noticing ability reliably and validly to provide targeted support in teacher training and professional development. One promising approach is written reflections on videotaped classroom situations. However, instruments that allow a comprehensive, flexible, standardized, and efficient assessment of written reflections are lacking. First, this chapter provides an overview of existing instruments to analyze noticing to clarify challenges that current approaches face. Second, the need for systematic basic research to analyze data from written reflections is outlined. It is argued that the resulting findings of human-rated written reflections will also help to establish technology-assisted (e.g., machine-learning [ML]) assessments of teachers’ noticing ability, which, in turn, provide a more time-efficient assessment procedure. Finally, the project, an intelligent feedback system for observing videotaped classroom situations (INFER), is introduced. This project is one of the first in which systematic basic research on fundamental open questions related to ratings of written reflections is conducted and in which corresponding results will be used to apply ML algorithms to analyze written reflections for assessing teachers’ noticing ability.
