Decoding dynamics in global assessments of teaching : an introduction to global assessments for State Space Grids
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Abstract
Most state-of-the-art teaching analysis is based on global (i.e., whole lesson) assessment techniques, which cannot account for dynamic processes that characterize learning-teaching relationships. We aim to overcome these limitations by proposing the Global assessments for State Space Grids (GSSG) method, which evaluates the global assessment in terms of the temporal evolution of key indicators and extends Hollenstein's State Space Grids (SSGs). For fast and accessible interpretation, the GSSG method implements established and new stochastic parameters with corresponding graphical elements that go beyond the assessments of the SSGs. These include parameters to measure global relationships between indicators of teaching quality over time, cluster analysis, error estimates, alignment tests, and parameters of different dynamic behaviors that affect global assessments. We introduce this method using aspects of classroom management as an example.