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Teachers' attitudes toward inclusion are frequently cited as being an important predictor of how successfully a given inclusive school system is implemented. At the same time, beliefs about the nature of teaching and learning are discussed as a possible predictor of attitudes toward inclusion. However, more recent research emphasizes the need of considering implicit processes, such as automatic evaluations, when describing attitudes and beliefs. Previous evidence on the association of attitudes toward inclusion and beliefs about teaching and learning is solely based on explicit reports. Therefore, this study aims to examine the relationship between attitudes toward inclusion, beliefs about teaching and learning, and the subsequent automatic evaluations of pre-service teachers (N = 197). The results revealed differences between pre-service teachers' explicit attitudes/beliefs and their subsequent automatic evaluations. Differences in the relationship between attitudes toward inclusion and beliefs about teaching and learning occur when teachers focus either on explicit measures or automatic evaluations. These differences might be due to different facets of the same attitude object being represented. Relying solely on either explicit measures or automatic evaluations at the exclusion of the other might lead to erroneous assumptions about the relation of attitudes toward inclusion and beliefs about teaching and learning.
Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.