@inproceedings{55337,
  abstract     = {{As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.}},
  author       = {{Wachsmuth, Henning and Alshomary, Milad}},
  booktitle    = {{Proceedings of the 29th International Conference on Computational Linguistics}},
  editor       = {{Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}},
  pages        = {{344–354}},
  publisher    = {{International Committee on Computational Linguistics}},
  title        = {{{“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations}}},
  year         = {{2022}},
}

@inproceedings{34067,
  author       = {{Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 2022 Workshop on Figurative Language Processing}},
  title        = {{{Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning}}},
  year         = {{2022}},
}

@inproceedings{35674,
  abstract     = {{<jats:p>We report on our work with students in our data science courses, focusing on the analysis of students’ results. This study represents an in-depth analysis of students’ creation and documentation of machine learning models. The students were supported by educationally designed Jupyter Notebooks, which are used as worked examples. Using the worked example, students document their results in a so-called computational essay. We examine which aspects of creating computational essays are difficult for students to find out how worked examples should be designed to support students without being too prescriptive. We analyze the computational essays produced by students and draw consequences for redesigning our worked example.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Hüsing, Sven and Biehler, Rolf and Podworny, Susanne and Schulte, Carsten}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  editor       = {{Peters, S. A. and Zapata-Cardona, L. and Bonafini, F. and Fan, A.}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Jupyter Notebooks for Teaching, Learning, and Doing Data Science}}},
  doi          = {{10.52041/iase.icots11.t10e3}},
  year         = {{2022}},
}

@article{35672,
  abstract     = {{<jats:p>This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students’ work is based on a teaching module about decision trees in machine learning and a worked example of such a modelling process. The study outlines the students’ performance in carrying out the machine learning technically and reasoning about bias in the data, different data preparation steps, the application context, and the resulting decision model. Furthermore, the context of the study and the theoretical backgrounds are presented.</jats:p>}},
  author       = {{Fleischer, Franz Yannik and Biehler, Rolf and Schulte, Carsten}},
  issn         = {{1570-1824}},
  journal      = {{Statistics Education Research Journal}},
  keywords     = {{Education, Statistics and Probability}},
  number       = {{2}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Teaching and Learning Data-Driven Machine Learning with Educationally Designed Jupyter Notebooks}}},
  doi          = {{10.52041/serj.v21i2.61}},
  volume       = {{21}},
  year         = {{2022}},
}

@article{34716,
  author       = {{Terhörst, Philipp and Kolf, Jan Niklas and Huber, Marco and Kirchbuchner, Florian and Damer, Naser and Moreno, Aythami Morales and Fierrez, Julian and Kuijper, Arjan}},
  journal      = {{IEEE Transactions on Technology and Society}},
  number       = {{1}},
  pages        = {{16--30}},
  title        = {{{A Comprehensive Study on Face Recognition Biases Beyond Demographics}}},
  doi          = {{10.1109/TTS.2021.3111823}},
  volume       = {{3}},
  year         = {{2022}},
}

@inproceedings{34710,
  author       = {{Huber, Marco and Terhörst, Philipp and Luu, Anh Thi and Kirchbuchner, Florian and Damer, Naser}},
  booktitle    = {{26th International Conference on Pattern Recognition, ICPR 2022, Montreal, QC, Canada, August 21-25, 2022}},
  pages        = {{938–944}},
  publisher    = {{IEEE}},
  title        = {{{Verification of Sitter Identity Across Historical Portrait Paintings by Confidence-aware Face Recognition}}},
  doi          = {{10.1109/ICPR56361.2022.9956452}},
  year         = {{2022}},
}

@article{34709,
  author       = {{Roig, Dailé Osorio and Rathgeb, Christian and Drozdowski, Pawel and Terhörst, Philipp and Struc, Vitomir and Busch, Christoph}},
  journal      = {{IEEE Trans. Biom. Behav. Identity Sci.}},
  number       = {{2}},
  pages        = {{263–275}},
  title        = {{{An Attack on Facial Soft-Biometric Privacy Enhancement}}},
  doi          = {{10.1109/TBIOM.2022.3172724}},
  volume       = {{4}},
  year         = {{2022}},
}

@phdthesis{34041,
  author       = {{Witschen, Linus Matthias}},
  title        = {{{Frameworks and Methodologies for Search-based Approximate Logic Synthesis}}},
  doi          = {{10.17619/UNIPB/1-1649}},
  year         = {{2022}},
}

@inproceedings{37554,
  author       = {{Asmar, Laban and Grigoryan, Khoren and Kuhn, Arno and Dumitrescu, Roman}},
  booktitle    = {{2022 IEEE 20th International Conference on Industrial Informatics (INDIN)}},
  publisher    = {{IEEE}},
  title        = {{{Survey on methods for early prototyping and validation of technical product ideas}}},
  doi          = {{10.1109/indin51773.2022.9976130}},
  year         = {{2022}},
}

@inproceedings{37550,
  author       = {{Kaiser, Lydia and Schräder, Elena and Bernijazov, Ruslan and Foullois, Marc and Dumitrescu, Roman}},
  booktitle    = {{Tag des Systems Engineering}},
  editor       = {{Koch, Walter and Wilke, Daria and Dreiseitel, Stefan and Kaffenberger, Rüdiger}},
  title        = {{{Ein Ansatz zur Strukturierung von KI-Assistenzen im Model-Based Systems Engineering}}},
  year         = {{2022}},
}

@inproceedings{38158,
  author       = {{Winkelnkemper, Felix and Huhmann, Tobias and Bechinie, Dominik and Eilerts, Katja and Lenke, Michael and Schulte, Carsten}},
  booktitle    = {{Society for Information Technology & Teacher Education International Conference}},
  keywords     = {{⛔ No DOI found}},
  pages        = {{1407–1413}},
  title        = {{{Supporting Geometry Learning Digitally-an Interdisciplinary Project to Foster Spatial Competences and Individual Learning Paths by Using Adaptable Algorithmic Feedback Capabilities}}},
  year         = {{2022}},
}

@inbook{39080,
  author       = {{Schulte, Carsten and Winkelnkemper, Felix}},
  booktitle    = {{Theologie im Übergang - Identität - Digitalisierung - Dialog}},
  pages        = {{117–135}},
  publisher    = {{Herder}},
  title        = {{{Digitalisierung als Chance und Herausforderung - Bemerkungen aus der Didaktik der Informatik}}},
  year         = {{2022}},
}

@inproceedings{38160,
  author       = {{Huhmann, Tobias and Winkelnkemper, Felix}},
  booktitle    = {{EDULEARN22 Proceedings}},
  pages        = {{10017–10026}},
  title        = {{{SUPPORTING GEOMETRY LEARNING DIGITALLY THROUGH ADAPTABLE ALGORITHMIC FEEDBACK-CHALLENGES AND SOLUTIONS}}},
  doi          = {{10.21125/edulearn.2022.2416}},
  year         = {{2022}},
}

@article{38162,
  author       = {{Huhmann, Tobias and Eilerts, Katja and Winkelnkemper, Felix}},
  journal      = {{Mathematik differenziert}},
  keywords     = {{⛔ No DOI found}},
  number       = {{4-2022}},
  pages        = {{42–45}},
  title        = {{{Pentomino Digital - Mit Einer App Geometrie Lernen}}},
  year         = {{2022}},
}

@article{34617,
  author       = {{Huber, Marco and Terhörst, Philipp and Kirchbuchner, Florian and Damer, Naser and Kuijper, Arjan}},
  journal      = {{33nd British Machine Vision Conference 2022}},
  keywords     = {{Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}},
  publisher    = {{arXiv}},
  title        = {{{Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition}}},
  doi          = {{10.48550/ARXIV.2210.10354}},
  year         = {{2022}},
}

@inproceedings{40510,
  abstract     = {{<jats:p>Decision-making processes are often based on data and data-driven machine learning methods in different areas such as recommender systems, medicine, criminalistics, etc. Well-informed citizens need at least a minimal understanding and critical reflection of corresponding data-driven machine learning methods. Decision trees are a method that can foster a preformal understanding of machine learning. We developed an exploratory teaching unit introducing decision trees in grade 6 along the question “How can Artificial Intelligence help us decide whether food is rather recommendable or not?” Students’ performances in an assessment task and self-assessment show that young learners can use a decision tree to classify new items and that they found the corresponding teaching unit informative.</jats:p>}},
  author       = {{Podworny, Susanne and Fleischer, Yannik and Hüsing, Sven}},
  booktitle    = {{Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference on Teaching Statistics}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Grade 6 Students’ Perception and Use of Data-Based Decision Trees}}},
  doi          = {{10.52041/iase.icots11.t2h3}},
  year         = {{2022}},
}

@inproceedings{30937,
  abstract     = {{<jats:p>Data Science has become an increasingly important aspect of our everyday lives as we gain a lot of different insights from data analyses, for example in the context of environmental issues. In order to make the process of data analyses comprehensible for lower secondary school students, we developed a data analysis project for computer science classes, focusing on gaining insights from environmental data by using the concept of epistemic programming. In this article, we report on the second implementation of this project, which was conducted in a ninth-grade computer science class. Concretely, we want to examine, how far the students were able to create computational essays to conduct reproducible data analyses on their own. In this regard, the computational essays created with the help of the professional tool Jupyter Notebooks will be examined in terms of aspects of reproducibility.</jats:p>}},
  author       = {{Hüsing, Sven and Podworny, Susanne}},
  booktitle    = {{Proceedings of the IASE 2021 Satellite Conference}},
  publisher    = {{International Association for Statistical Education}},
  title        = {{{Computational Essays as an Approach for Reproducible Data Analysis in lower Secondary School}}},
  doi          = {{10.52041/iase.zwwoh}},
  year         = {{2022}},
}

@inproceedings{41164,
  abstract     = {{Companies show an increasing interest in low-code development platforms to facilitate application development by domain experts without sophisticated software development knowledge. Thus, companies aim for a more efficient development of more effective applications since domain experts as so-called citizen developers are no longer limited by the availability and domain knowledge of trained software developers. Nevertheless, efficiency and effectiveness of application development is traditionally also largely influenced by the use of a suitable software development method. Domain experts are, however, not trained in software development methods. This introduces a risk of domain experts creating unusable applications or exceeding the designated time frame of a project (or both). In this paper, we therefore propose an initial version of a situational software development method which supports domain experts in manufacturing companies during the low-code development of applications. The method can be tailored based on situational factors, considering application requirements, features of the used low-code development platform, and characteristics of the development team. We also present feedback corroborating the usefulness of our method and future extension points based on expert interviews.}},
  author       = {{Kirchhoff, Jonas and Weidmann, Nils and Sauer, Stefan and Engels, Gregor}},
  booktitle    = {{Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings}},
  publisher    = {{ACM}},
  title        = {{{Situational Development of Low-Code Applications in Manufacturing Companies}}},
  doi          = {{10.1145/3550356.3561560}},
  year         = {{2022}},
}

@inproceedings{41134,
  author       = {{Gottschalk, Sebastian and Bhat, Rakshit and Weidmann, Nils and Kirchhoff, Jonas and Engels, Gregor}},
  booktitle    = {{Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings}},
  publisher    = {{ACM}},
  title        = {{{Low-code experimentation on software products}}},
  doi          = {{10.1145/3550356.3561572}},
  year         = {{2022}},
}

@phdthesis{35188,
  author       = {{Eidens, Fabian}},
  title        = {{{Privacy-Preserving Cryptography: Attribute-Based Signatures and Updatable Credentials}}},
  doi          = {{10.17619/UNIPB/1-1653}},
  year         = {{2022}},
}