One teacher's identity, emotions, and commitment to change: A case study into the cognitive–affective processes of a secondary school teacher in the context of reforms

This paper presents a cognitive social–psychological theoretical framework on emotions, derived from Richard Lazarus, to understand how teachers’ identity can be affected in a context of reforms. The emphasis of this approach is on the cognitive–affective processes of individual teachers, enabling us to gain a detailed understanding of what teachers have at stake or what their personal, moral, and social concerns are. To illustrate the usefulness of this approach, a case of a reform-enthusiast Dutch secondary school teacher of Dutch language and literature is presented. The analysis of his emotions of enthusiasm for the reforms, and his emotions of anxiety, anger, guilt, and shame related to the way the reforms unfold in his school and influence his work, show the many ways his identity and concerns are affected, resulting in a loss of reform enthusiasm. The paper ends with a reflection on the possible risks of current educational policies to the commitment and quality of the current and next generation of teachers.     

A Flexible Latent Class Approach to Estimating Test‐Score Reliability

The latent class reliability coefficient (LCRC) is improved by using the divisive latent class model instead of the unrestricted latent class model. This results in the divisive latent class reliability coefficient (DLCRC), which unlike LCRC avoids making subjective decisions about the best solution and thus avoids judgment error. A computational study using large numbers of items shows that DLCRC also is faster than LCRC and fast enough for practical purposes. Speed and objectivity render DLCRC superior to LCRC. A decisive feature of DLCRC is that it aims at closely approximating the multivariate distribution of item scores, which might render the method suited when test data are multidimensional. A simulation study focusing on multidimensionality shows that DLCRC in general has little bias relative to the true reliability and is relatively accurate compared to LCRC and classical lower bound methods coefficients α and λ2 and the greatest lower bound.