@misc{StaubitzTeusnerMeinel2019, author = {Staubitz, Thomas and Teusner, Ralf and Meinel, Christoph}, title = {MOOCs in Secondary Education}, series = {2019 IEEE Global Engineering Education Conference (EDUCON)}, journal = {2019 IEEE Global Engineering Education Conference (EDUCON)}, publisher = {IEEE}, address = {New York}, isbn = {978-1-5386-9506-7}, issn = {2165-9567}, doi = {10.1109/EDUCON.2019.8725138}, pages = {173 -- 182}, year = {2019}, abstract = {Computer science education in German schools is often less than optimal. It is only mandatory in a few of the federal states and there is a lack of qualified teachers. As a MOOC (Massive Open Online Course) provider with a German background, we developed the idea to implement a MOOC addressing pupils in secondary schools to fill this gap. The course targeted high school pupils and enabled them to learn the Python programming language. In 2014, we successfully conducted the first iteration of this MOOC with more than 7000 participants. However, the share of pupils in the course was not quite satisfactory. So we conducted several workshops with teachers to find out why they had not used the course to the extent that we had imagined. The paper at hand explores and discusses the steps we have taken in the following years as a result of these workshops.}, language = {en} } @article{HakeBodenbergerGroth2023, author = {Hake, Tim and Bodenberger, Bernhard and Groth, Detlef}, title = {In Python available: St. Nicolas House Algorithm (SNHA) with bootstrap support for improved performance in dense networks}, series = {Human biology and public health}, volume = {1}, journal = {Human biology and public health}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, issn = {2748-9957}, doi = {10.52905/hbph2023.1.63}, pages = {16}, year = {2023}, abstract = {The St. Nicolas House Algorithm (SNHA) finds association chains of direct dependent variables in a data set. The dependency is based on the correlation coefficient, which is visualized as an undirected graph. The network prediction is improved by a bootstrap routine. It enables the computation of the empirical p-value, which is used to evaluate the significance of the predicted edges. Synthetic data generated with the Monte Carlo method were used to firstly compare the Python package with the original R package, and secondly to evaluate the predicted network using the sensitivity, specificity, balanced classification rate and the Matthew's correlation coefficient (MCC). The Python implementation yields the same results as the R package. Hence, the algorithm was correctly ported into Python. The SNHA scores high specificity values for all tested graphs. For graphs with high edge densities, the other evaluation metrics decrease due to lower sensitivity, which could be partially improved by using bootstrap,while for graphs with low edge densities the algorithm achieves high evaluation scores. The empirical p-values indicated that the predicted edges indeed are significant.}, language = {en} } @article{KrauseLindemann2014, author = {Krause, Florian and Lindemann, Oliver}, title = {Expyriment: A Python library for cognitive and neuroscientific experiments}, series = {Behavior research methods : a journal of the Psychonomic Society}, volume = {46}, journal = {Behavior research methods : a journal of the Psychonomic Society}, number = {2}, publisher = {Springer}, address = {New York}, issn = {1554-351X}, doi = {10.3758/s13428-013-0390-6}, pages = {416 -- 428}, year = {2014}, abstract = {Expyriment is an open-source and platform-independent lightweight Python library for designing and conducting timing-critical behavioral and neuroimaging experiments. The major goal is to provide a well-structured Python library for script-based experiment development, with a high priority being the readability of the resulting program code. Expyriment has been tested extensively under Linux and Windows and is an all-in-one solution, as it handles stimulus presentation, the recording of input/output events, communication with other devices, and the collection and preprocessing of data. Furthermore, it offers a hierarchical design structure, which allows for an intuitive transition from the experimental design to a running program. It is therefore also suited for students, as well as for experimental psychologists and neuro-scientists with little programming experience.}, language = {en} } @article{ManiKupschMuelleretal.2022, author = {Mani, Deepak and Kupsch, Andreas and M{\"u}ller, Bernd R. and Bruno, Giovanni}, title = {Diffraction Enhanced Imaging Analysis with Pseudo-Voigt Fit Function}, series = {Journal of imaging : open access journal}, volume = {8}, journal = {Journal of imaging : open access journal}, number = {8}, publisher = {MDPI}, address = {Basel}, issn = {2313-433X}, doi = {10.3390/jimaging8080206}, pages = {13}, year = {2022}, abstract = {Diffraction enhanced imaging (DEI) is an advanced digital radiographic imaging technique employing the refraction of X-rays to contrast internal interfaces. This study aims to qualitatively and quantitatively evaluate images acquired using this technique and to assess how different fitting functions to the typical rocking curves (RCs) influence the quality of the images. RCs are obtained for every image pixel. This allows the separate determination of the absorption and the refraction properties of the material in a position-sensitive manner. Comparison of various types of fitting functions reveals that the Pseudo-Voigt (PsdV) function is best suited to fit typical RCs. A robust algorithm was developed in the Python programming language, which reliably extracts the physically meaningful information from each pixel of the image. We demonstrate the potential of the algorithm with two specimens: a silicone gel specimen that has well-defined interfaces, and an additively manufactured polycarbonate specimen.}, language = {en} }