[{"title":"Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?","main_file_link":[{"open_access":"1","url":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tea.70001"}],"doi":"10.1002/tea.70001","oa":"1","date_updated":"2025-03-04T08:08:42Z","date_created":"2025-03-04T08:08:37Z","author":[{"full_name":"Zeller, Jannis","id":"99022","orcid":"0000-0002-1834-5520","last_name":"Zeller","first_name":"Jannis"},{"last_name":"Riese","orcid":"0000-0003-2927-2619","full_name":"Riese, Josef","id":"429","first_name":"Josef"}],"year":"2025","citation":{"ieee":"J. Zeller and J. Riese, “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?,” <i>Journal of Research in Science Teaching</i>, 2025, doi: <a href=\"https://doi.org/10.1002/tea.70001\">10.1002/tea.70001</a>.","chicago":"Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical Analyses?” <i>Journal of Research in Science Teaching</i>, 2025. <a href=\"https://doi.org/10.1002/tea.70001\">https://doi.org/10.1002/tea.70001</a>.","ama":"Zeller J, Riese J. Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses? <i>Journal of Research in Science Teaching</i>. Published online 2025. doi:<a href=\"https://doi.org/10.1002/tea.70001\">10.1002/tea.70001</a>","apa":"Zeller, J., &#38; Riese, J. (2025). Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses? <i>Journal of Research in Science Teaching</i>. <a href=\"https://doi.org/10.1002/tea.70001\">https://doi.org/10.1002/tea.70001</a>","short":"J. Zeller, J. Riese, Journal of Research in Science Teaching (2025).","mla":"Zeller, Jannis, and Josef Riese. “Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of PPCK Emerge From Non-Hierarchical Analyses?” <i>Journal of Research in Science Teaching</i>, 2025, doi:<a href=\"https://doi.org/10.1002/tea.70001\">10.1002/tea.70001</a>.","bibtex":"@article{Zeller_Riese_2025, title={Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?}, DOI={<a href=\"https://doi.org/10.1002/tea.70001\">10.1002/tea.70001</a>}, journal={Journal of Research in Science Teaching}, author={Zeller, Jannis and Riese, Josef}, year={2025} }"},"publication_status":"published","publication_identifier":{"issn":["0022-4308"],"eissn":["1098-2736"]},"article_type":"original","keyword":["computational grounded theory","language analysis","machine learning","pedagogical content knowledge","unsupervised learning"],"language":[{"iso":"eng"}],"_id":"58885","user_id":"99022","department":[{"_id":"299"}],"abstract":[{"lang":"eng","text":"There have been several attempts to conceptualize and operationalize pedagogical content knowledge (PCK) in the context of teachers' professional competencies. A recent and popular model is the Refined Consensus Model (RCM), which proposes a framework of dispositional competencies (personal PCK—pPCK) that influence more action-related competencies (enacted PCK—ePCK) and vice versa. However, descriptions of the internal structure of pPCK and possible knowledge domains that might develop independently are still limited, being either primarily theoretically motivated or strictly hierarchical and therefore of limited use, for example, for formative feedback and further development of the RCM. Meanwhile, a non-hierarchical differentiation for the ePCK regarding the plan-teach-reflect cycle has emerged. In this study, we present an exploratory computational approach to investigate pre-service teachers' pPCK for a similar non-hierarchical structure using a large dataset of responses to a pPCK questionnaire (N=846). We drew on theoretical foundations and previous empirical findings to achieve interpretability by integrating this external knowledge into our analyses using the Computational Grounded Theory (CGT) framework. The results of a cluster analysis of the pPCK scores indicate the emergence of prototypical groups, which we refer to as competency profiles: (1) a group with low performance, (2) a group with relatively advanced competency in using pPCK to create instructional elements, (3) a group with relatively advanced competency in using pPCK to assess and analyze described instructional elements, and (4) a group with high performance. These groups show tendencies for certain language usage, which we analyze using a structural topic model in a CGT-inspired pattern refinement step. We verify these patterns by demonstrating the ability of a machine learning model to predict the competency profile assignments. Finally, we discuss some implications of the results for the further development of the RCM and their potential usability for an automated formative assessment."}],"status":"public","type":"journal_article","publication":"Journal of Research in Science Teaching"},{"year":"2015","citation":{"bibtex":"@inproceedings{Heymann_Haeb-Umbach_Golik_Schlueter_2015, title={Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">10.1109/ICASSP.2015.7178933</a>}, booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, author={Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P. and Schlueter, R.}, year={2015}, pages={5053–5057} }","mla":"Heymann, Jahn, et al. “Unsupervised Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>, 2015, pp. 5053–57, doi:<a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">10.1109/ICASSP.2015.7178933</a>.","short":"J. Heymann, R. Haeb-Umbach, P. Golik, R. Schlueter, in: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On, 2015, pp. 5053–5057.","apa":"Heymann, J., Haeb-Umbach, R., Golik, P., &#38; Schlueter, R. (2015). Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions. In <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on</i> (pp. 5053–5057). <a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">https://doi.org/10.1109/ICASSP.2015.7178933</a>","ama":"Heymann J, Haeb-Umbach R, Golik P, Schlueter R. Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions. In: <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>. ; 2015:5053-5057. doi:<a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">10.1109/ICASSP.2015.7178933</a>","ieee":"J. Heymann, R. Haeb-Umbach, P. Golik, and R. Schlueter, “Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions,” in <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on</i>, 2015, pp. 5053–5057.","chicago":"Heymann, Jahn, Reinhold Haeb-Umbach, P. Golik, and R. Schlueter. “Unsupervised Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” In <i>Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On</i>, 5053–57, 2015. <a href=\"https://doi.org/10.1109/ICASSP.2015.7178933\">https://doi.org/10.1109/ICASSP.2015.7178933</a>."},"page":"5053-5057","title":"Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2015/hey_icassp_2015.pdf","open_access":"1"}],"doi":"10.1109/ICASSP.2015.7178933","date_updated":"2022-01-06T06:51:09Z","oa":"1","date_created":"2019-07-12T05:28:45Z","author":[{"last_name":"Heymann","id":"9168","full_name":"Heymann, Jahn","first_name":"Jahn"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"},{"first_name":"P.","last_name":"Golik","full_name":"Golik, P."},{"first_name":"R.","last_name":"Schlueter","full_name":"Schlueter, R."}],"abstract":[{"lang":"eng","text":"The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data."}],"status":"public","type":"conference","publication":"Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on","keyword":["codecs","signal denoising","speech recognition","Bayesian feature enhancement","denoising autoencoder","reverberant ASR","single-channel speech recognition","speaker to microphone distances","unsupervised adaptation","Adaptation models","Noise reduction","Reverberation","Speech","Speech recognition","Training","deep neuronal networks","denoising autoencoder","feature enhancement","robust speech recognition"],"language":[{"iso":"eng"}],"_id":"11813","user_id":"44006","department":[{"_id":"54"}]},{"_id":"11833","department":[{"_id":"54"}],"user_id":"460","keyword":["Unsupervised","geometry calibration","microphone arrays","position self-calibration"],"language":[{"iso":"eng"}],"publication":"International Workshop on Acoustic Signal Enhancement (IWAENC 2012)","type":"conference","abstract":[{"lang":"eng","text":"In this paper we propose an approach to retrieve the geometry of an acoustic sensor network consisting of spatially distributed microphone arrays from unconstrained speech input. The calibration relies on Direction of Arrival (DoA) measurements which do not require a clock synchronization among the sensor nodes. The calibration problem is formulated as a cost function optimization task, which minimizes the squared differences between measured and predicted observations and additionally avoids the existence of minima that correspond to mirrored versions of the actual sensor orientations. Further, outlier measurements caused by reverberation are mitigated by a Random Sample Consensus (RANSAC) approach. The experimental results show a mean positioning error of at most 25 cm even in highly reverberant environments."}],"status":"public","date_updated":"2023-10-26T08:10:52Z","oa":"1","author":[{"first_name":"Florian","last_name":"Jacob","full_name":"Jacob, Florian"},{"full_name":"Schmalenstroeer, Joerg","id":"460","last_name":"Schmalenstroeer","first_name":"Joerg"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_created":"2019-07-12T05:29:08Z","title":"Microphone Array Position Self-Calibration from Reverberant Speech Input","main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12.pdf","open_access":"1"}],"quality_controlled":"1","related_material":{"link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/Microphine_Array_Position_Self-Calibration_from_Reverberant_Speech_Input.mp4","relation":"supplementary_material","description":"Video"},{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12_Poster.pdf","description":"Poster","relation":"supplementary_material"},{"url":"https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12_Demonstrator.pdf","relation":"supplementary_material","description":"Demonstrator"}]},"year":"2012","citation":{"bibtex":"@inproceedings{Jacob_Schmalenstroeer_Haeb-Umbach_2012, title={Microphone Array Position Self-Calibration from Reverberant Speech Input}, booktitle={International Workshop on Acoustic Signal Enhancement (IWAENC 2012)}, author={Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2012} }","mla":"Jacob, Florian, et al. “Microphone Array Position Self-Calibration from Reverberant Speech Input.” <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>, 2012.","short":"F. Jacob, J. Schmalenstroeer, R. Haeb-Umbach, in: International Workshop on Acoustic Signal Enhancement (IWAENC 2012), 2012.","apa":"Jacob, F., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2012). Microphone Array Position Self-Calibration from Reverberant Speech Input. <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>.","ieee":"F. Jacob, J. Schmalenstroeer, and R. Haeb-Umbach, “Microphone Array Position Self-Calibration from Reverberant Speech Input,” 2012.","chicago":"Jacob, Florian, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Microphone Array Position Self-Calibration from Reverberant Speech Input.” In <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>, 2012.","ama":"Jacob F, Schmalenstroeer J, Haeb-Umbach R. Microphone Array Position Self-Calibration from Reverberant Speech Input. In: <i>International Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>. ; 2012."}},{"main_file_link":[{"url":"https://groups.uni-paderborn.de/nt/pubs/2009/HePlFiScHa09.pdf","open_access":"1"}],"doi":"10.1109/SSP.2009.5278589","title":"A hierarchical approach to unsupervised shape calibration of microphone array networks","date_created":"2019-07-12T05:28:37Z","author":[{"first_name":"Marius","full_name":"Hennecke, Marius","last_name":"Hennecke"},{"full_name":"Ploetz, Thomas","last_name":"Ploetz","first_name":"Thomas"},{"first_name":"Gernot A.","last_name":"Fink","full_name":"Fink, Gernot A."},{"first_name":"Joerg","last_name":"Schmalenstroeer","id":"460","full_name":"Schmalenstroeer, Joerg"},{"first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242","full_name":"Haeb-Umbach, Reinhold"}],"date_updated":"2023-10-26T08:09:22Z","oa":"1","citation":{"mla":"Hennecke, Marius, et al. “A Hierarchical Approach to Unsupervised Shape Calibration of Microphone Array Networks.” <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 2009, pp. 257–60, doi:<a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>.","short":"M. Hennecke, T. Ploetz, G.A. Fink, J. Schmalenstroeer, R. Haeb-Umbach, in: IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009), 2009, pp. 257–260.","bibtex":"@inproceedings{Hennecke_Ploetz_Fink_Schmalenstroeer_Haeb-Umbach_2009, title={A hierarchical approach to unsupervised shape calibration of microphone array networks}, DOI={<a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>}, booktitle={IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)}, author={Hennecke, Marius and Ploetz, Thomas and Fink, Gernot A. and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2009}, pages={257–260} }","apa":"Hennecke, M., Ploetz, T., Fink, G. A., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2009). A hierarchical approach to unsupervised shape calibration of microphone array networks. <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 257–260. <a href=\"https://doi.org/10.1109/SSP.2009.5278589\">https://doi.org/10.1109/SSP.2009.5278589</a>","ama":"Hennecke M, Ploetz T, Fink GA, Schmalenstroeer J, Haeb-Umbach R. A hierarchical approach to unsupervised shape calibration of microphone array networks. In: <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>. ; 2009:257-260. doi:<a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>","ieee":"M. Hennecke, T. Ploetz, G. A. Fink, J. Schmalenstroeer, and R. Haeb-Umbach, “A hierarchical approach to unsupervised shape calibration of microphone array networks,” in <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 2009, pp. 257–260, doi: <a href=\"https://doi.org/10.1109/SSP.2009.5278589\">10.1109/SSP.2009.5278589</a>.","chicago":"Hennecke, Marius, Thomas Ploetz, Gernot A. Fink, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “A Hierarchical Approach to Unsupervised Shape Calibration of Microphone Array Networks.” In <i>IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)</i>, 257–60, 2009. <a href=\"https://doi.org/10.1109/SSP.2009.5278589\">https://doi.org/10.1109/SSP.2009.5278589</a>."},"page":"257-260","year":"2009","quality_controlled":"1","language":[{"iso":"eng"}],"keyword":["acoustic sensing tasks","array geometry","calibration","coherence analysis","hierarchical procedure","local shape calibration","microphone array networks","microphone arrays","network calibration method","sensor arrays","SRP-PHAT","unsupervised shape calibration"],"user_id":"460","department":[{"_id":"54"}],"_id":"11806","status":"public","abstract":[{"lang":"eng","text":"Microphone arrays represent the basis for many challenging acoustic sensing tasks. The accuracy of techniques like beamforming directly depends on a precise knowledge of the relative positions of the sensors used. Unfortunately, for certain use cases manually measuring the geometry of an array is not feasible due to practical constraints. In this paper we present an approach to unsupervised shape calibration of microphone array networks. We developed a hierarchical procedure that first performs local shape calibration based on coherence analysis and then employs SRP-PHAT in a network calibration method. Practical experiments demonstrate the effectiveness of our approach especially for highly reverberant acoustic environments."}],"type":"conference","publication":"IEEE/SP 15th Workshop on Statistical Signal Processing (SSP 2009)"}]