@article{WulffBuschhueterWestphaletal.2020,
  author    = {Wulff, Peter and Buschh{\"u}ter, David and Westphal, Andrea and Nowak, Anna and Becker, Lisa and Robalino, Hugo and Stede, Manfred and Borowski, Andreas},
  title     = {Computer-based classification of preservice physics teachers' written reflections},
  series = {Journal of science education and technology},
  volume    = {30},
  journal   = {Journal of science education and technology},
  number    = {1},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1059-0145},
  doi       = {10.1007/s10956-020-09865-1},
  pages     = {1 -- 15},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{PaulyNottbusch2020,
  author    = {Pauly, Dennis Nikolas and Nottbusch, Guido},
  title     = {The Influence of the German Capitalization Rules on Reading},
  series = {Frontiers in Communication},
  volume    = {5},
  journal   = {Frontiers in Communication},
  publisher = {Frontiers Media},
  address   = {Lausanne},
  issn      = {2297-900X},
  doi       = {10.3389/fcomm.2020.00015},
  pages     = {15},
  year      = {2020},
  abstract  = {German orthography systematically marks all nouns (even other nominalized word classes) by capitalizing their first letter. It is often claimed that readers benefit from the uppercase-letter syntactic and semantic information, which makes the processing of sentences easier (e.g., Bock et al., 1985, 1989). In order to test this hypothesis, we asked 54 German readers to read single sentences systematically manipulated by a target word (N). In the experimental condition (EXP), we used semantic priming (in the following example: sick -> cold) in order to build up a strong expectation of a noun, which was actually an attribute for the following noun (N+1) (translated to English e.g., "The sick writer had a cold (N) nose (N+1) ..."). The sentences in the control condition were built analogously, but word N was purposefully altered (keeping word length and frequency constant) to make its interpretation as a noun extremely unlikely (e.g., "The sick writer had a blue (N) nose (N+1) ..."). In both conditions, the sentences were presented either following German standard orthography (Cap) or in lowercase spelling (NoCap). The capitalized nouns in the EXP/Cap condition should then prevent garden-path parsing, as capital letters can be recognized parafoveally. However, in the EXP/NoCap condition, we expected a garden-path effect on word N+1 affecting first-pass fixations and the number of regressions, as the reader realizes that word N is instead an adjective. As the control condition does not include a garden-path, we expected to find (small) effects of the violation of the orthographic rule in the CON/NoCap condition, but no garden-path effect. As a global result, it can be stated that reading sentences in which nouns are not marked by a majuscule slows a native German reader down significantly, but from an absolute point of view, the effect is small. Compared with other manipulations (e.g., transpositions or substitutions), a lowercase letter still represents the correct allograph in the correct position without affecting phonology. Furthermore, most German readers do have experience with other alphabetic writing systems that lack consistent noun capitalization, and in (private) digital communication lowercase nouns are quite common. Although our garden-path sentences did not show the desired effect, we found an indication of grammatical pre-processing enabled by the majuscule in the regularly spelled sentences: In the case of high noun frequency, we post hoc located parafovea-on-fovea effects, i.e., longer fixation durations, on the attributive adjective (word N). These benefits of capitalization could only be detected under specific circumstances. In other cases, we conclude that longer reading durations are mainly the result of disturbance in readers' habituation when the expected capitalization is missing.},
  language  = {en}
}