TY - JOUR A1 - Emhardt, Selina N. A1 - Jarodzka, Halszka A1 - Brand-Gruwel, Saskia A1 - Drumm, Christian A1 - Niehorster, Diederick C. A1 - van Gog, Tamara T1 - What is my teacher talking about? Effects of displaying the teacher’s gaze and mouse cursor cues in video lectures on students’ learning JF - Journal of Cognitive Psychology N2 - Eye movement modelling examples (EMME) are instructional videos that display a teacher’s eye movements as “gaze cursor” (e.g. a moving dot) superimposed on the learning task. This study investigated if previous findings on the beneficial effects of EMME would extend to online lecture videos and compared the effects of displaying the teacher’s gaze cursor with displaying the more traditional mouse cursor as a tool to guide learners’ attention. Novices (N = 124) studied a pre-recorded video lecture on how to model business processes in a 2 (mouse cursor absent/present) × 2 (gaze cursor absent/present) between-subjects design. Unexpectedly, we did not find significant effects of the presence of gaze or mouse cursors on mental effort and learning. However, participants who watched videos with the gaze cursor found it easier to follow the teacher. Overall, participants responded positively to the gaze cursor, especially when the mouse cursor was not displayed in the video. KW - Instructional design KW - eye movement modelling examples KW - video learning Y1 - 2022 U6 - https://doi.org/10.1080/20445911.2022.2080831 SN - 2044-5911 SP - 1 EP - 19 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Kempt, Hendrik A1 - Freyer, Nils A1 - Nagel, Saskia K. T1 - Justice and the normative standards of explainability in healthcare JF - Philosophy & Technology N2 - Providing healthcare services frequently involves cognitively demanding tasks, including diagnoses and analyses as well as complex decisions about treatments and therapy. From a global perspective, ethically significant inequalities exist between regions where the expert knowledge required for these tasks is scarce or abundant. One possible strategy to diminish such inequalities and increase healthcare opportunities in expert-scarce settings is to provide healthcare solutions involving digital technologies that do not necessarily require the presence of a human expert, e.g., in the form of artificial intelligent decision-support systems (AI-DSS). Such algorithmic decision-making, however, is mostly developed in resource- and expert-abundant settings to support healthcare experts in their work. As a practical consequence, the normative standards and requirements for such algorithmic decision-making in healthcare require the technology to be at least as explainable as the decisions made by the experts themselves. The goal of providing healthcare in settings where resources and expertise are scarce might come with a normative pull to lower the normative standards of using digital technologies in order to provide at least some healthcare in the first place. We scrutinize this tendency to lower standards in particular settings from a normative perspective, distinguish between different types of absolute and relative, local and global standards of explainability, and conclude by defending an ambitious and practicable standard of local relative explainability. KW - Clinical decision support systems KW - Justice KW - Medical AI KW - Explainability KW - Normative standards Y1 - 2022 U6 - https://doi.org/10.1007/s13347-022-00598-0 VL - 35 IS - Article number: 100 SP - 1 EP - 19 PB - Springer Nature CY - Berlin ER - TY - CHAP A1 - Eggert, Mathias A1 - Kriska, Melina T1 - Gamification for software development processes – relevant affordances and design principles T2 - Proceedings of the 55th Hawaii International Conference on System Sciences N2 - A Gamified Information System (GIS) implements game concepts and elements, such as affordances and game design principles to motivate people. Based on the idea to develop a GIS to increase the motivation of software developers to perform software quality tasks, the research work at hand aims at investigating relevant requirements from that target group. Therefore, 14 interviews with software development experts are conducted and analyzed. According to the results, software developers prefer the affordances points, narrative storytelling in a multiplayer and a round-based setting. Furthermore, six design principles for the development of a GIS are derived. Y1 - 2022 SN - 978-0-9981331-5-7 U6 - https://doi.org/10.24251/HICSS.2022.200 N1 - Hawaii International Conference on System Sciences (HICSS) 2022, 04.01.2022 – 07.01.2022 SP - 1614 EP - 1623 PB - HICSS Publishing CY - Honolulu ER - TY - CHAP A1 - Zähl, Philipp M. A1 - Biewendt, Marcel A1 - Wolf, Martin R. A1 - Eggert, Mathias T1 - Requirements for competence developing games in the environment of SE Competence Development T2 - AKWI-Tagungsband zur 35. AKWI-Jahrestagung N2 - Many of today’s factors make software development more and more complex, such as time pressure, new technologies, IT security risks, et cetera. Thus, a good preparation of current as well as future software developers in terms of a good software engineering education becomes progressively important. As current research shows, Competence Developing Games (CDGs) and Serious Games can offer a potential solution. This paper identifies the necessary requirements for CDGs to be conducive in principle, but especially in software engineering (SE) education. For this purpose, the current state of research was summarized in the context of a literature review. Afterwards, some of the identified requirements as well as some additional requirements were evaluated by a survey in terms of subjective relevance. KW - software engineering KW - requirements KW - competence developing games KW - systematic literature review Y1 - 2022 SN - 978-3-95545-409-8 U6 - https://doi.org/10.30844/AKWI_2022_05 N1 - Tagungsband zur 35. Jahrestagung des Arbeitskreises Wirtschaftsinformatik an Hochschulen für Angewandte Wissenschaften im deutschsprachigen Raum (AKWI), 11.09. bis 13.09.2022, Hochschule für Technik und Wirtschaft Berlin (HTW Berlin) und Hochschule für Wirtschaft und Recht Berlin (HWR Berlin) SP - 73 EP - 88 PB - GITO CY - Berlin ER - TY - CHAP A1 - Eggert, Mathias A1 - Dyong, Julian T1 - Applying process mining in small and medium sized IT enterprises – challenges and guidelines T2 - Business Process Management, 20th International Conference, BPM 2022, Proceedings N2 - Process mining gets more and more attention even outside large enterprises and can be a major benefit for small and medium sized enterprises (SMEs) to gain competitive advantages. Applying process mining is challenging, particularly for SMEs because they have less resources and process maturity. So far, IS researchers analyzed process mining challenges with a focus on larger companies. This paper investigates the application of process mining by means of a case study and sheds light into the particular challenges of an IT SME. The results reveal 13 SME process mining challenges and seven guidelines to address them. In this way, the paper contributes to the understanding of process mining application in SME and shows similarities and differences to larger companies. KW - Process mining KW - Challenges KW - Guidelines KW - SME KW - Case Study Y1 - 2022 SN - 978-3-031-16103-2 U6 - https://doi.org/10.1007/978-3-031-16103-2_11 N1 - International Conference on Business Process Management (BPM 2022), Münster, Germany, Sept. 11-16, 2022 SP - 125 EP - 142 PB - Springer CY - Cham ER - TY - JOUR A1 - Mueller, Tobias A1 - Segin, Alexander A1 - Weigand, Christoph A1 - Schmitt, Robert H. T1 - Feature selection for measurement models JF - International journal of quality & reliability management N2 - Purpose In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset. Design/methodology/approach In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments. Findings Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model. Originality/value For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future. KW - Feature selection KW - Modelling KW - Measurement models KW - Measurement uncertainty Y1 - 2022 U6 - https://doi.org/10.1108/IJQRM-07-2021-0245 SN - 0265-671X IS - Vol. ahead-of-print, No. ahead-of-print. PB - Emerald Group Publishing Limited CY - Bingley ER - TY - CHAP A1 - Freyer, Nils A1 - Kempt, Hendrik ED - Bhakuni, Himani ED - Miotto, Lucas T1 - AI-DSS in healthcare and their power over health-insecure collectives T2 - Justice in global health N2 - AI-based systems are nearing ubiquity not only in everyday low-stakes activities but also in medical procedures. To protect patients and physicians alike, explainability requirements have been proposed for the operation of AI-based decision support systems (AI-DSS), which adds hurdles to the productive use of AI in clinical contexts. This raises two questions: Who decides these requirements? And how should access to AI-DSS be provided to communities that reject these standards (particularly when such communities are expert-scarce)? This chapter investigates a dilemma that emerges from the implementation of global AI governance. While rejecting global AI governance limits the ability to help communities in need, global AI governance risks undermining and subjecting health-insecure communities to the force of the neo-colonial world order. For this, this chapter first surveys the current landscape of AI governance and introduces the approach of relational egalitarianism as key to (global health) justice. To discuss the two horns of the referred dilemma, the core power imbalances faced by health-insecure collectives (HICs) are examined. The chapter argues that only strong demands of a dual strategy towards health-secure collectives can both remedy the immediate needs of HICs and enable them to become healthcare independent. Y1 - 2023 SN - 9781003399933 U6 - https://doi.org/10.4324/9781003399933-4 SP - 38 EP - 55 PB - Routledge CY - London ER - TY - CHAP A1 - Kohl, Philipp A1 - Freyer, Nils A1 - Krämer, Yoka A1 - Werth, Henri A1 - Wolf, Steffen A1 - Kraft, Bodo A1 - Meinecke, Matthias A1 - Zündorf, Albert ED - Conte, Donatello ED - Fred, Ana ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - ALE: a simulation-based active learning evaluation framework for the parameter-driven comparison of query strategies for NLP T2 - Deep Learning Theory and Applications N2 - Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework. KW - Active learning KW - Query learning KW - Natural language processing KW - Deep learning KW - Reproducible research Y1 - 2023 SN - 978-3-031-39058-6 (Print) SN - 978-3-031-39059-3 (Online) U6 - https://doi.org/10.1007/978-3-031-39059-3_16 N1 - 4th International Conference, DeLTA 2023, Rome, Italy, July 13–14, 2023. SP - 235 EP - 253 PB - Springer CY - Cham ER - TY - CHAP A1 - Freyer, Nils A1 - Thewes, Dustin A1 - Meinecke, Matthias ED - Gusikhin, Oleg ED - Hammoudi, Slimane ED - Cuzzocrea, Alfredo T1 - GUIDO: a hybrid approach to guideline discovery & ordering from natural language texts T2 - Proceedings of the 12th International Conference on Data Science, Technology and Applications DATA - Volume 1 N2 - Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low. KW - Natural Language Processing KW - Text Mining KW - Process Model Extraction KW - Business Process Intelligence Y1 - 2023 SN - 978-989-758-664-4 U6 - https://doi.org/10.5220/0012084400003541 SN - 2184-285X N1 - 12th International Conference on Data Science, Technology and Applications, July 11-13, 2023, in Rome, Italy. SP - 335 EP - 342 ER - TY - JOUR A1 - Bruksle, Ieva A1 - Chwallek, Constanze A1 - Krastina, Anzelika T1 - Strengthening sustainability in entrepreneurship education - implications for shifting entrepreneurial thinking towards sustainability at universities JF - ACTA PROSPERITATIS N2 - By developing innovative solutions to social and environmental problems, sustainable ventures carry greatpotential. Entrepreneurship which focuses especially on new venture creation can be developed through education anduniversities, in particular, are called upon to provide an impetus for social change. But social innovations are associatedwith certain hurdles, which are related to the multi-dimensionality, i.e. the tension between creating social,environmental and economic value and dealing with a multiplicity of stakeholders. The already complex field ofentrepreneurship education has to face these challenges. This paper, therefore, aims to identify starting points for theintegration of sustainability into entrepreneurship education. To pursue this goal experiences from three differentproject initiatives between the partner universities: Lapland University of Applied Sciences, FH Aachen University ofApplied Sciences and Turiba University are reflected and findings are systematically condensed into recommendationsfor education on sustainable entrepreneurship. KW - climate change KW - entrepreneurship education KW - Finland KW - Germany KW - Latvia Y1 - 2023 U6 - https://doi.org/10.37804/1691-6077-2023-14-37-48 SN - 1691-6077 VL - 14 IS - 1 SP - 37 EP - 48 PB - Sciendo ER -