@article{61463, abstract = {{Vernetztes Wissen ist ein zentrales Lernziel des Hochschulstudiums, insbesondere im interdisziplinär angelegten Studienfach Komparatistik. Um den Aufbau vernetzten Wissens bei Bachelorstudierenden der Komparatistik zu unterstützen, ist in diesem Projekt Portfolioarbeit eingesetzt worden, die als Methode selbsttätigen und selbstreflexiven Lernens geeignet erscheint, zur Auseinandersetzung mit Lerninhalten zu motivieren und zur Kompetenzentwicklung der Studierenden beizutragen. Mittels unstrukturierter Beobachtungen der Portfolioarbeit im Seminar sind inhaltliche Effekte und methodische Entwicklungen erfasst worden. Anhand anteilig quantitativer, überwiegend qualitativer Inhaltsanalysen der Portfolios sind konkrete Vernetzungen zwischen Lerninhalten ermittelt worden. Die Explorationsstudie zeigt veränderte Perspektiven und geweckte Interessen bei den Studierenden durch die Portfolioarbeit sowie vielfältige Kontextualisierungen, Vergleiche und Verknüpfungen in den Portfolios auf und bietet hierdurch einen möglichen Ansatzpunkt für strukturelle Empfehlungen für das Studienfach Komparatistik.}}, author = {{Hannebohm, Ronja}}, issn = {{2199–8825}}, journal = {{die hochschullehre: Interdisziplinäre Zeitschrift für Studium und Lehre}}, keywords = {{Portfolioarbeit, portfolio work, vernetztes Wissen, knowledge networks, Beobachtung, naturalistic observation, Inhaltsanalyse, content analysis}}, pages = {{65--80}}, publisher = {{wbv}}, title = {{{Potenziale der Portfolioarbeit für den Aufbau vernetzten Wissens im Bachelorstudium: Eine Explorationsstudie im Studienfach Komparatistik/Vergleichende Literatur- und Kulturwissenschaft}}}, doi = {{10.3278/HSL2606W}}, volume = {{12}}, year = {{2026}}, } @article{58885, abstract = {{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.}}, author = {{Zeller, Jannis and Riese, Josef}}, issn = {{1098-2736}}, journal = {{Journal of Research in Science Teaching}}, keywords = {{computational grounded theory, language analysis, machine learning, pedagogical content knowledge, unsupervised learning}}, title = {{{Competency Profiles of PCK Using Unsupervised Learning: What Implications for the Structures of pPCK Emerge From Non-Hierarchical Analyses?}}}, doi = {{10.1002/tea.70001}}, year = {{2025}}, } @inproceedings{60958, abstract = {{Large Language Models (LLMs) excel in understanding, generating, and processing human language, with growing adoption in process mining. Process mining relies on event logs that capture explicit process knowledge; however, knowledge-intensive processes (KIPs) in domains such as healthcare and product development depend on tacit knowledge, which is often absent from event logs. To bridge this gap, this study proposes a LLM-based framework for mobilizing tacit process knowledge and enriching event logs. A proof-of-concept is demonstrated using a KIP-specific LLM-driven conversational agent built on GPT-4o. The results indicate that LLMs can capture tacit process knowledge through targeted queries and systematically integrate it into event logs. This study presents a novel approach combining LLMs, knowledge management, and process mining, advancing the understanding and management of KIPs by enhancing knowledge accessibility and documentation.}}, author = {{Brennig, Katharina}}, booktitle = {{AMCIS 2025 Proceedings. 11.}}, keywords = {{Process Mining, Large Language Model, Knowledge Management, Knowledge-Intensive Process, Tacit Knowledge}}, location = {{Montréal}}, title = {{{Revealing the Unspoken: Using LLMs to Mobilize and Enrich Tacit Knowledge in Event Logs of Knowledge-Intensive Processes}}}, year = {{2025}}, } @article{61123, abstract = {{Knowledge graphs are used by a growing number of applications to represent structured data. Hence, evaluating the veracity of assertions in knowledge graphs—dubbed fact checking—is currently a challenge of growing importance. However, manual fact checking is commonly impractical due to the sheer size of knowledge graphs. This paper is a systematic survey of recent works on automatic fact checking with a focus on knowledge graphs. We present recent fact-checking approaches, the varied sources they use as background knowledge, and the features they rely upon. Finally, we draw conclusions pertaining to possible future research directions in fact checking knowledge graphs.}}, author = {{Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, issn = {{0360-0300}}, journal = {{ACM Computing Surveys}}, keywords = {{fact checking, knowledge graphs, fact-checkers, check worthiness, evidence retrieval, trust, veracity.}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Fact Checking Knowledge Graphs -- A Survey}}}, doi = {{10.1145/3749838}}, volume = {{58}}, year = {{2025}}, } @inbook{62701, abstract = {{Learning continuous vector representations for knowledge graphs has significantly improved state-of-the-art performances in many challenging tasks. Yet, deep-learning-based models are only post-hoc and locally explainable. In contrast, learning Web Ontology Language (OWL) class expressions in Description Logics (DLs) is ante-hoc and globally explainable. However, state-of-the-art learners have two well-known lim-itations: scaling to large knowledge graphs and handling missing infor-mation. Here, we present a decision-tree-based learner (tDL) to learn Web Ontology Languages (OWLs) class expressions over large knowl-edge graphs, while imputing missing triples. Given positive and negative example individuals, tDL firstly constructs unique OWL expressions in .SHOIN from concise bounded descriptions of individuals. Each OWL class expression is used as a feature in a binary classification problem to represent input individuals. Thereafter, tDL fits a CART decision tree to learn Boolean decision rules distinguishing positive examples from nega-tive examples. A final OWL expression in.SHOIN is built by traversing the built CART decision tree from the root node to leaf nodes for each positive example. By this, tDL can learn OWL class expressions without exploration, i.e., the number of queries to a knowledge graph is bounded by the number of input individuals. Our empirical results show that tDL outperforms the current state-of-the-art models across datasets. Impor-tantly, our experiments over a large knowledge graph (DBpedia with 1.1 billion triples) show that tDL can effectively learn accurate OWL class expressions, while the state-of-the-art models fail to return any results. Finally, expressions learned by tDL can be seamlessly translated into natural language explanations using a pre-trained large language model and a DL verbalizer.}}, author = {{Demir, Caglar and Yekini, Moshood and Röder, Michael and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783032060655}}, issn = {{0302-9743}}, keywords = {{Decision Tree, OWL Class Expression Learning, Description Logic, Knowledge Graph, Large Language Model, Verbalizer}}, location = {{Porto, Portugal}}, publisher = {{Springer Nature Switzerland}}, title = {{{Tree-Based OWL Class Expression Learner over Large Graphs}}}, doi = {{10.1007/978-3-032-06066-2_29}}, year = {{2025}}, } @inproceedings{62007, abstract = {{Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE) models in performing link prediction. Typical approaches to this end train multiple models as part of the ensemble, and the diverse predictions are then averaged. However, this approach has some significant drawbacks. For instance, the computational overhead of training multiple models increases latency and memory overhead. In contrast, model merging approaches offer a promising alternative that does not require training multiple models. In this work, we introduce model merging, specifically weighted averaging, in KGE models. Herein, a running average of model parameters from a training epoch onward is maintained and used for predictions. To address this, we additionally propose an approach that selectively updates the running average of the ensemble model parameters only when the generalization performance improves on a validation dataset. We evaluate these two different weighted averaging approaches on link prediction tasks, comparing the state-of-the-art benchmark ensemble approach. Additionally, we evaluate the weighted averaging approach considering literal-augmented KGE models and multi-hop query answering tasks as well. The results demonstrate that the proposed weighted averaging approach consistently improves performance across diverse evaluation settings.}}, author = {{Sapkota, Rupesh and Demir, Caglar and Sharma, Arnab and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Proceedings of the Thirteenth International Conference on Knowledge Capture(K-CAP 2025)}}, keywords = {{Knowledge Graphs, Embeddings, Ensemble Learning}}, location = {{Dayton, OH, USA}}, publisher = {{ACM}}, title = {{{Parameter Averaging in Link Prediction}}}, doi = {{https://doi.org/10.1145/3731443.3771365}}, year = {{2025}}, } @inproceedings{57445, abstract = {{Knowledge management is essential for successful disaster management. This paper conducts a Systematic Literature Review at the intersection of the knowledge management field and disaster management and examines the available body of literature. Fire departments are chosen as the focus group as they are the most prevalent emergency services. There are many publications that deal with knowledge management during the response phase of an emergency. Often, the literature focuses on the application of knowledge management in large-scale disasters to link the various organizations on-scene. What is missing in most approaches is a prior step of implementing and training the knowledge management system. Therefore, this literature review seeks to provide an overview of approaches for daily routines and small-to-medium incidents that serve as a training ground. However, literature on non-incident phases and smaller incidents is scarce. As information technologies are developing rapidly, there is no modern and recent description of the current use of knowledge management solutions in this area.}}, author = {{Schultz, Andreas Maximilian and Dotzki, Fabian and Mozgova, Iryna}}, booktitle = {{Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management}}, keywords = {{Knowledge Management, Civil Protection, Systematic Literature Review, Fire Brigade}}, location = {{Porto, Portugal}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Knowledge Management in Civil Protection at the Example of Fire Brigades}}}, doi = {{10.5220/0012947700003838}}, year = {{2024}}, } @inproceedings{57240, abstract = {{Validating assertions before adding them to a knowledge graph is an essential part of its creation and maintenance. Due to the sheer size of knowledge graphs, automatic fact-checking approaches have been developed. These approaches rely on reference knowledge to decide whether a given assertion is correct. Recent hybrid approaches achieve good results by including several knowledge sources. However, it is often impractical to provide a sheer quantity of textual knowledge or generate embedding models to leverage these hybrid approaches. We present FaVEL, an approach that uses algorithm selection and ensemble learning to amalgamate several existing fact-checking approaches that rely solely on a reference knowledge graph and, hence, use fewer resources than current hybrid approaches. For our evaluation, we create updated versions of two existing datasets and a new dataset dubbed FaVEL-DS. Our evaluation compares our approach to 15 fact-checking approaches—including the state-of-the-art approach HybridFC—on 3 datasets. Our results demonstrate that FaVEL outperforms all other approaches significantly by at least 0.04 in terms of the area under the ROC curve. Our source code, datasets, and evaluation results are open-source and can be found at https://github.com/dice-group/favel.}}, author = {{Qudus, Umair and Röder, Michael and Tatkeu Pekarou, Franck Lionel and Morim da Silva, Ana Alexandra and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{EKAW 2024}}, editor = {{Rospocher, Marco}}, keywords = {{fact checking, ensemble learning, transfer learning, knowledge management.}}, location = {{Amsterdam, Netherlands}}, title = {{{FaVEL: Fact Validation Ensemble Learning}}}, year = {{2024}}, } @inproceedings{57160, abstract = {{Large audio tagging models are usually trained or pre-trained on AudioSet, a dataset that encompasses a large amount of different sound classes and acoustic environments. Knowledge distillation has emerged as a method to compress such models without compromising their effectiveness. There are many different applications for audio tagging, some of which require a specialization to a narrow domain of sounds to be classified. For these scenarios, it is beneficial to distill the large audio tagger with respect to a specific subset of sounds of interest. A method to prune a general dataset with respect to a target dataset is presented. By distilling with such a specialized pruned dataset, we obtain a compressed model with better classification accuracy in the specific target domain than with target-agnostic distillation.}}, author = {{Werning, Alexander and Haeb-Umbach, Reinhold}}, booktitle = {{32nd European Signal Processing Conference (EUSIPCO 2024)}}, keywords = {{data pruning, knowledge distillation, audio tagging}}, location = {{Lyon}}, title = {{{Target-Specific Dataset Pruning for Compression of Audio Tagging Models}}}, year = {{2024}}, } @inproceedings{50479, abstract = {{Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. However, most fact-checking approaches are binary classifiers that fail to consider the volatility of some assertions, i.e., the fact that such assertions are only valid at certain times or for specific time intervals. Moreover, the few approaches able to predict when an assertion was valid (i.e., time-point prediction approaches) rely on manual feature engineering. This paper presents TEMPORALFC, a temporal fact-checking approach that uses multiple sources of background knowledge to assess the veracity and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets and compare it to the state of the art in fact-checking and time-point prediction. Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.}}, author = {{Qudus, Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga}}, booktitle = {{The Semantic Web – ISWC 2023}}, editor = {{R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón, María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong and Li, Juanzi}}, isbn = {{9783031472398}}, issn = {{0302-9743}}, keywords = {{temporal fact checking · ensemble learning · transfer learning · time-point prediction · temporal knowledge graphs}}, location = {{Athens, Greece}}, pages = {{465–483}}, publisher = {{Springer, Cham}}, title = {{{TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs}}}, doi = {{10.1007/978-3-031-47240-4_25}}, volume = {{14265}}, year = {{2023}}, } @inproceedings{35942, abstract = {{Partial coverage of the traditional grid is one of the factors that con-tribute to the low electrical energy access levels in developing countries. This often results in long distances between the grid and unconnected communities. Microgrids, due to their distributed energy resources, have the potential to increase energy access levels. However, there is limited access to microgrids-related knowledge. The knowledge is essential for the effective and efficient use of energy, operation, and hence sustainability of microgrids. To contribute to the sustainability of microgrids, a Virtual and Interactive Microgrids Learning Environment (VIMLE) for microgrids knowledge transfer is developed. VIMLE development is guided by design-based research. With knowledge transfer and skills acquisition through the use of VIMLE, local capacity for designing, installing, operating and maintenance of microgrids is built. Skilled local capacity will contribute to microgrids sustainability. Hence, improve electrical energy access levels and contribute to the achievement of SDG 7.}}, author = {{Bogere, Paul and Bode, Henrik and Temmen, Katrin}}, booktitle = {{Learning in the Age of Digital and Green Transition. ICL 2022. Lecture Notes in Networks and Systems}}, editor = {{E. Auer, Michael and Pachatz, Wolfgang and Rüütmann, Tiia}}, keywords = {{Knowledge Transfer, Microgrids, Sustainability}}, location = {{Wien}}, pages = {{671 -- 679}}, publisher = {{Springer Nature}}, title = {{{Work in Progress: Development of a Virtual and Interactive Microgrids Learning Environment for Microgrids Sustainability – The case of East Africa}}}, doi = {{https://doi.org/10.1007/978-3-031-26876-2_63}}, volume = {{633}}, year = {{2023}}, } @article{34132, abstract = {{How can Knowledge In/Equity be addressed in qualitative research by taking the idea of Open Science into account? Two projects from the Open Science Fellows Programme by Wikimedia Deutschland will be used to illustrate how Open Science practices can succeed in qualitative research, thereby reducing In/Equity. In this context, In/Equity is considered as a fair and equal representation of people, their knowledge and insights and comprehends questions about how epistemic, structural, institutional and personal biases generate and shape knowledge as guidance. Three questions guide this approach: firstly, what do we understand by In/Equity in the context of knowledge production in these projects? Secondly, who will be involved in knowledge generation and to what extent will they be valued or unvalued? Thirdly, how can data be made accessible for re-use to enable true participation and sharing?}}, author = {{Steinhardt, Isabel and Kruschick, Felicitas}}, issn = {{2367-7163}}, journal = {{Research Ideas and Outcomes}}, keywords = {{Open Science, Knowledge Equity, Qualitative Methods}}, publisher = {{Pensoft Publishers}}, title = {{{Knowledge Equity and Open Science in qualitative research – Practical research considerations}}}, doi = {{10.3897/rio.8.e86387}}, volume = {{8}}, year = {{2022}}, } @inbook{32179, abstract = {{This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.}}, author = {{Kersting, Joschka and Ahmed, Mobeen and Geierhos, Michaela}}, booktitle = {{HCI International 2022 Posters}}, editor = {{Stephanidis, Constantine and Antona, Margherita and Ntoa, Stavroula}}, isbn = {{9783031064166}}, issn = {{1865-0929}}, keywords = {{On-The-Fly Computing, Chatbot, Knowledge Base}}, location = {{Virtual}}, pages = {{419----426}}, publisher = {{Springer International Publishing}}, title = {{{Chatbot-Enhanced Requirements Resolution for Automated Service Compositions}}}, doi = {{10.1007/978-3-031-06417-3_56}}, volume = {{1580}}, year = {{2022}}, } @article{35136, abstract = {{Im Zentrum dieses Beitrags stehen Ergebnisse der Messung pädagogischer Kompetenzen Studierender der Theologie, die das Praxissemester in Deutschland absolviert haben. Das bildungswissenschaftliche Wissen, Kompetenzselbsteinschätzungen und ihre Entwicklung sowie die Einschätzung der im Praxissemester erreichten Ziele Studierender werden dabei unter Berücksichtigung der Ausrichtung des Lehramtsstudiums auf eine Schulform betrachtet. Um die Ergebnisse der Messung bildungswissenschaftlichen Wissens und die der Messung von Kompetenzselbsteinschätzungen zu kontextualisieren (N = 304), wird zuerst die Relevanz des (bildungswissenschaftlichen) Wissens als Ausgangspunkt des Könnens herausgearbeitet. Daran anschließend werden Befunde zur schulformspezifischen Professionalisierung resümiert. Anschließend werden Hypothesen hergeleitet, die Anlage der Studie sowie die Testinstrumente vorge- stellt, die Ergebnisse präsentiert und diskutiert. Die Ergebnisse zeigen wider Erwarten, dass sich weder das bildungswissenschaftliche Wissen, die Kompetenzselbsteinschätzungen und ihre Entwicklung noch die Einschätzung der im Praxissemester erreichten Ziele angehender Lehrkräfte in Abhängigkeit der Schulformen unterscheiden. Die Diskussion bezieht sich u.a. auf die Struktur der Lehramtsstudiengänge, die Denkfiguren zur Entwicklung von Können und die Konzeption der Messinstrumente. }}, author = {{Caruso, Carina and Seifert, Andreas}}, issn = {{1018-1539}}, journal = {{Österreichische Religionspädagogische Forum}}, keywords = {{Bildungswissenschaftliches Wissen, Kompetenzmessung, Kompetenzselbsteinschätzung, Praxissemester, Professionalisierung / competence measurement, competence self-assessment, educational knowledge, internship, professionalization}}, number = {{1}}, pages = {{239--260}}, publisher = {{Universitätsbibliothek Graz}}, title = {{{ Inwiefern ist die Professionalisierung in Praxisphasen schulformspezifisch?}}}, doi = {{10.30:2022.1.14}}, volume = {{30}}, year = {{2022}}, } @article{35137, abstract = {{Im Zentrum dieses Beitrags stehen Ergebnisse der Messung pädagogischer Kompetenzen Studierender. Dabei werden sowohl das bildungswissenschaftliche Wissen als auch die Entwicklung der Kompe­tenzselbsteinschätzungen in den Bereichen Unterrichten, Erziehen, Beurteilen und Innovieren unter Berücksichtigung individueller Voraussetzungen (Alter, Geschlecht, Abiturnote, Bachelornote, Konfession) betrachtet. Um die Ergeb­nisse hinsichtlich ihrer Bedeutung für die Professionalisierung angehender Lehrkräfte diskutieren zu können, wird, den empirischen Erkenntnissen voranstehend, die Bedeutung von Wissen für berufliches Können herausgearbeitet. Daran anschließend werden Hypothesen hergeleitet, die Anlage der Studie sowie die Testinstrumente vorgestellt, die Ergebnisse präsentiert und diskutiert. Die Ergebnisse zeigen, dass die Abitur- und Bachelornote die Varianz hinsichtlich des pädagogischen Wissens aufklären, sich eine signifikante Entwicklung der Kompetenzselbsteinschätzungen angehender Lehrkräfte feststellen lässt, aber sich angehende Religionslehrkräfte kaum von anderen Studierenden unterscheiden. Die Diskussion nimmt u. a. Rückbezug auf die Denkfiguren zur Entwicklung berufli­chen Könnens und benennt Limitationen, die mit der Studie und Kompetenzmessungen verbunden sind. Daran schließt die Formulierung eines Ausblicks an. Der Beitrag zielt insbesondere darauf, repräsentative Ergebnisse der Kompetenzmessung zu präsentieren und dabei potenzielle Einflussfaktoren auf die studentische Kompetenzent­wicklung zu beleuchten. Ein dadurch angereichertes Konglomerat belastbarer Erkenntnisse zielt darauf, langfristig zur Ableitung lehrerbildungsdidaktischer Überlegungen herangezogen werden zu können, die die studentische Professionalisierung unterstützen.}}, author = {{Caruso, Carina and Seifert, Andreas}}, issn = {{2750 - 3941}}, journal = {{Religionspädagogische Beiträge. Journal for Religion in Education }}, keywords = {{Bildungswissenschaftliches Wissen, Kompetenzmessung, Kompetenzselbsteinschätzung, Praxissemester, Professionalisierung / competence measurement, competence self-assessment, educational knowledge, internship, professionalization}}, number = {{1}}, pages = {{3--15}}, publisher = {{University of Bamberg Press}}, title = {{{Pädagogische Kompetenz als Ausgangspunkt beruflichen Könnens!? Ergebnisse der Kompetenzmessung angehender Lehrkräfte unter Berücksichtigung individueller Voraussetzungen}}}, doi = {{10.20377/rpb-101}}, volume = {{45}}, year = {{2022}}, } @article{36524, abstract = {{How can Knowledge In/Equity be addressed in qualitative research by taking the idea of Open Science into account? Two projects from the Open Science Fellows Programme by Wikimedia Deutschland will be used to illustrate how Open Science practices can succeed in qualitative research, thereby reducing In/Equity. In this context, In/Equity is considered as a fair and equal representation of people, their knowledge and insights and comprehends questions about how epistemic, structural, institutional and personal biases generate and shape knowledge as guidance. Three questions guide this approach: firstly, what do we understand by In/Equity in the context of knowledge production in these projects? Secondly, who will be involved in knowledge generation and to what extent will they be valued or unvalued? Thirdly, how can data be made accessible for re-use to enable true participation and sharing?}}, author = {{Steinhardt, Isabel and Kruschick, Felicitas}}, issn = {{2367-7163}}, journal = {{Research Ideas and Outcomes}}, keywords = {{Open Science, Knowledge Equity, Qualitative Methods}}, pages = {{e86387}}, publisher = {{Pensoft Publishers}}, title = {{{Knowledge Equity and Open Science in qualitative research – Practical research considerations}}}, doi = {{10.3897/rio.8.e86387}}, volume = {{8}}, year = {{2022}}, } @inproceedings{33957, abstract = {{Manufacturing companies are challenged to make the increasingly complex work processes equally manageable for all employees to prevent an impending loss of competence. In this contribution, an intelligent assistance system is proposed enabling employees to help themselves in the workplace and provide them with competence-related support. This results in increasing the short- and long-term efficiency of problem solving in companies.}}, author = {{Deppe, Sahar and Brandt, Lukas and Brünninghaus, Marc and Papenkordt, Jörg and Heindorf, Stefan and Tschirner-Vinke, Gudrun}}, keywords = {{Assistance system, Knowledge graph, Information retrieval, Neural networks, AR}}, location = {{Stuttgart}}, title = {{{AI-Based Assistance System for Manufacturing}}}, doi = {{10.1109/ETFA52439.2022.9921520}}, year = {{2022}}, } @inproceedings{32509, abstract = {{ We consider fact-checking approaches that aim to predict the veracity of assertions in knowledge graphs. Five main categories of fact-checking approaches for knowledge graphs have been proposed in the recent literature, of which each is subject to partially overlapping limitations. In particular, current text-based approaches are limited by manual feature engineering. Path-based and rule-based approaches are limited by their exclusive use of knowledge graphs as background knowledge, and embedding-based approaches suffer from low accuracy scores on current fact-checking tasks. We propose a hybrid approach—dubbed HybridFC—that exploits the diversity of existing categories of fact-checking approaches within an ensemble learning setting to achieve a significantly better prediction performance. In particular, our approach outperforms the state of the art by 0.14 to 0.27 in terms of Area Under the Receiver Operating Characteristic curve on the FactBench dataset. Our code is open-source and can be found at https://github.com/dice-group/HybridFC.}}, author = {{Qudus, Umair and Röder, Michael and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web -- ISWC 2022}}, editor = {{Sattler, Ulrike and Hogan, Aidan and Keet, Maria and Presutti, Valentina}}, isbn = {{978-3-031-19433-7}}, keywords = {{fact checking · ensemble learning · knowledge graph veracit}}, location = {{Hanghzou, China}}, pages = {{462----480}}, publisher = {{Springer International Publishing}}, title = {{{HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs}}}, doi = {{10.1007/978-3-031-19433-7_27}}, year = {{2022}}, } @article{34822, abstract = {{The role of domain-specific content knowledge is discussed controversially for the early childhood context. Therefore, this review aims at untangling the research on domain-specific content knowledge for early childhood educators by systematically reviewing the conceptual and operational definition of and results on early childhood educators' content knowledge in different domains. Using the scientific databases ERIC, PsycInfo and Web of Sciences, we identified 36 studies on early childhood educators' domain-specific content knowledge. By comparing these studies, we found that conceptualizations of early childhood educators' content knowledge move on a continuum between a scientific related perspective and a practice related perspective. The scientific related perspective defines content knowledge as the knowledge of key concepts, facts and rules of the domain integrating knowledge taught in primary, secondary or upper secondary school. The practice related perspective includes knowledge of key concepts, facts and rules of the domain limited to the knowledge explicitly relevant for teaching in early childhood education as well as selected domain-specific knowledge of children and teaching. Our review shows that the results and implications drawn by the study authors depend on how these authors conceptualize early childhood educators' content knowledge on this continuum. Further research, therefore, needs to consider carefully how early childhood educators' content knowledge is conceptualized. The paper further discusses gaps in this research field, such as validating methods for measuring early childhood educators' content knowledge or implementing more rigorous experimental designs to examine effects of early childhood educators' content knowledge.}}, author = {{Bruns, Julia and Gasteiger, Hedwig and Strahl, Carolin}}, issn = {{2049-6613}}, journal = {{Review of Education}}, keywords = {{content knowledge, domain-specific learning, early childhood education, teacher knowledge}}, number = {{2}}, pages = {{500--538}}, publisher = {{Wiley}}, title = {{{Conceptualising and measuring domain-specific content knowledge of early childhood educators: A systematic review}}}, doi = {{10.1002/rev3.3255}}, volume = {{9}}, year = {{2021}}, } @techreport{17019, abstract = {{The scientific impact of research papers is multi-dimensional and can be determined quantitatively by means of citation analysis and qualitatively by means of content analysis. Accounting for the widely acknowledged limitations of pure citation analysis, we adopt a knowledge-based perspective on scientific impact to develop a methodology for content-based citation analysis which allows determining how papers have enabled knowledge development in subsequent research (knowledge impact). As knowledge development differs between research genres, we develop a new knowledgebased citation analysis methodology for the genre of standalone literature reviews (LRs). We apply the suggested methodology to the IS business value domain by manually coding 22 LRs and 1,228 citing papers (CPs) and show that the results challenge the assumption that citations indicate knowledge impact. We derive implications for distinguishing knowledge impact from citation impact in the LR genre. Finally, we develop recommendations for authors of LRs, scientific evaluation committees and editorial boards of journals how to apply and benefit from the suggested methodology, and we discuss its efficiency and automatization.}}, author = {{Schryen, Guido and Wagner, Gerit and Benlian, Alexander}}, keywords = {{Scientific impact, knowledge impact, content-based citation analysis, methodology}}, title = {{{Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre}}}, year = {{2020}}, }