@article{35202, abstract = {{Purpose: This study aims at investigating how digitalisation (in the sense of industry 4.0) has changed the work of farmers and how they experience the changes from more traditional work to digitalised agriculture. It also investigates what knowledge farmers require on digitalised farms and how they acquire it. Dairy farming was used as domain of investigation since it, unlike other industries, has strongly been affected by digitalisation throughout the last years. Method: Exploratory interviews with 10 livestock farmers working on digitalised dairy farms were analysed using qualitative content analysis. A deductive and inductive coding strategy was used. Findings: Farming work has changed from more manual tasks towards symbol manipulation and data processing. Farmers must be able to use computers and other digital devices to retrieve and analyse sensor data that allow them to monitor and control the processes on their farm. For this new kind of work, farmers require elaborated mental models that link traditional farming knowledge with knowledge about digital systems, including a strong understanding of production processes underlying their farm. Learning is mostly based on instructions offered by manufacturers of the new technology as well as informal and non-formal learning modes. Even younger farmers report that digital technology was not sufficiently covered in their (vocational) degrees. In general, farmers emphasises the positive effects of digitalisation both on their working as well as private life. Conclusions: Farmers should be aware of the opportunities as well as the potential drawbacks of the digitalisation of work processes in agriculture. Providers of agricultural education (like vocational schools or training institutes) need to incorporate the knowledge and skills required to work in digitalised environments (e.g., data literacy) in their syllabi. Further studies are required to assess how digitalisation changes farming practices and what knowledge as well as skills linked to these developments are required in the future.}}, author = {{Goller, Michael and Caruso, Carina and Harteis, Christian}}, issn = {{2197-8646}}, journal = {{International Journal for Research in Vocational Education and Training}}, keywords = {{Work-Based Learning, Organisational Change, Digital Competences, Qualitative Research, Digitalisation, Farming, Dairy, VET, Vocational Education and Training}}, number = {{2}}, pages = {{208–223}}, title = {{{Digitalisation in Agriculture: Knowledge and Learning Requirements of German Dairy Farmers}}}, doi = {{10.13152/IJRVET.8.2.4.}}, volume = {{8}}, year = {{2021}}, } @inbook{35464, abstract = {{The digital transformation of organizations in the industrial sector is primarily driven by the opportunity to increase productivity while simultaneously reducing costs through integration into a cyber-physical system. One way to fully tap the potential of a cyber-physical system is the concept of the digital twin, i.e., the real-time digital representation of machines and resources involved – including human resources. The vision of representing humans by digital twins primarily aims at increasing economic benefits. The digital twin of a human, however, cannot be designed in a similar way to that of a machine. The human digital twin shall rather enable humans to act within the cyber-physical system. It therefore offers humans a power of control and the opportunity to provide feedback. The concept of the digital twin is still in its infancy and raises many questions in particular from an educational perspective. The contribution aims at answering the following questions and refers to the example of team learning: Which and how much data should and may the digital twin contain in order to support humans in their learning? To what extent will humans be able to control and design their own learning? How may skills, experiences, and social interactions of humans be represented in the digital twin; their growth and further development, respectively? With cyber-physical systems transcending corporate, national, and legal boundaries, what learning culture will be the frame of reference for the involved organizations?}}, author = {{Berisha-Gawlowski, Angelina and Caruso, Carina and Harteis, Christian}}, booktitle = {{Digital Transformation of Learning Organizations }}, editor = {{Ifenthaler, Dirk and Hofhues, Sandra and Egloffstein, Marc and Helbig, Christian}}, isbn = {{978-3-030-55877-2}}, keywords = {{Digital twin, Learning organization, Change, Team learning, Professional development}}, pages = {{ 95–114}}, publisher = {{Springer}}, title = {{{The Concept of a Digital Twin and Its Potential for Learning Organizations}}}, doi = {{10.1007/978-3-030-55878-9_6}}, year = {{2021}}, } @techreport{35889, abstract = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}}, author = {{Schneider, Stefan Balthasar}}, keywords = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}}, title = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}}, year = {{2021}}, } @inproceedings{24547, abstract = {{Over the last years, several approaches for the data-driven estimation of expected possession value (EPV) in basketball and association football (soccer) have been proposed. In this paper, we develop and evaluate PIVOT: the first such framework for team handball. Accounting for the fast-paced, dynamic nature and relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep learning architecture that relies solely on tracking data. This efficient approach is capable of predicting the probability that a team will score within the near future given the fine-grained spatio-temporal distribution of all players and the ball over the last seconds of the game. Our experiments indicate that PIVOT is able to produce accurate and calibrated probability estimates, even when trained on a relatively small dataset. We also showcase two interactive applications of PIVOT for valuing actual and counterfactual player decisions and actions in real-time.}}, author = {{Müller, Oliver and Caron, Matthew and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen}}, booktitle = {{8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)}}, keywords = {{expected possession value, handball, tracking data, time series classification, deep learning}}, location = {{Online}}, title = {{{PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data}}}, year = {{2021}}, } @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}}, } @inproceedings{27491, abstract = {{ Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.}}, author = {{Höper, Lukas}}, booktitle = {{21st Koli Calling International Conference on Computing Education Research}}, isbn = {{9781450384889}}, keywords = {{data awareness, machine learning, data science education, data-driven digital artifacts, artificial intelligence}}, publisher = {{Association for Computing Machinery}}, title = {{{Developing and Evaluating the Concept Data Awareness for K12 Computing Education}}}, doi = {{10.1145/3488042.3490509}}, year = {{2021}}, } @inbook{57884, abstract = {{Although music apps are becoming increasingly popular, there has been little research on informal music practices with apps. This article presents findings of an ongoing study on learning processes and aesthetic experiences with informal appmusic practices. In particular, it discusses the aesthetic practices (Reckwitz, 2008b) of using specific places for making music. In our grounded theory study (Charmaz, 2014) we collected data using interviews, participant observation and videography. As exemplary cases, this article presents two analyses of the use of ‘inspiring places’ and ‘safe places’. The results suggest that perceiving the atmosphere is a fundamental prerequisite for both places. Additionally, the results shed light on aesthetic aspects of mobile music making. (DIPF/Orig.)}}, author = {{Eusterbrock, Linus and Godau, Marc and Haenisch, Matthias and Krebs, Matthias and Rolle, Christian}}, booktitle = {{Musikpädagogik im Spannungsfeld von Reflexion und Intervention}}, editor = {{Hasselhorn, Johannes and Kautny, Oliver and Platz, Friedrich}}, keywords = {{Education, Ästhetik, Schul- und Bildungswesen, Informal learning, Informelles Lernen, Musical education, Musikpädagogik, Anwendung, Ästhetische Erfahrung, Grounded Theory, Längsschnittuntersuchung, Learning process, Lernprozess, Longitudinal analysis, Longitudinal study, Mobiles Gerät, Music reading, Musizieren, Erziehung}}, pages = {{155–172}}, publisher = {{Waxmann}}, title = {{{Von ’inspirierenden Orten’ und ’Safe Places’. Die ästhetische Nutzung von Orten in der Appmusikpraxis}}}, volume = {{41}}, year = {{2021}}, } @article{32558, abstract = {{With the rapid progress of technological development, self-efficacy in reference to digital devices (i.e., information and computer technology [ICT] self-efficacy) is an important driver that helps students to deal with technological problems and support their lifelong learning processes. Schools, peers, and home learning environments are important sources for the development of positive self-efficacy. Expanding on previous research, we investigated the associations between different aspects of the digital home learning environment and students’ ICT self-efficacy. The moderation effects of gender were also tested. A total of 651 children answered a questionnaire about different digital home learning environment dimensions and estimated their ICT self-efficacy using an adapted scale—Schwarzer and Jerusalem’s (1999) general self-efficacy scale. Using the structural equation modeling technique, a digital home learning environment containing six different qualities of parental support was investigated. Families’ cultural capital, parents’ attitudes toward the Internet, and shared Internet activities at home contributed positively to ICT self-efficacy. We observed small gender differences, with the moderation effect being nonsignificant. The results help researchers and practitioners to understand how different dimensions of the digital home learning environment support ICT self-efficacy. We will discuss how parents can enhance the home learning environment and how teachers can integrate this knowledge into formal education.}}, author = {{Bonanati, Sabrina and Buhl, Heike M.}}, issn = {{1387-1579}}, journal = {{Learning Environments Research}}, keywords = {{Digital media use, Gender, Home learning environment, ICT self-efcacy, Motivation, Parental involvement}}, number = {{2}}, pages = {{485--505}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{The digital home learning environment and its relation to children’s ICT self-efficacy}}}, doi = {{10.1007/s10984-021-09377-8}}, volume = {{25}}, year = {{2021}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{18686, author = {{Kersting, Joschka and Bäumer, Frederik Simon}}, booktitle = {{PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020}}, keywords = {{Software Requirements, Natural Language Processing, Transfer Learning, On-The-Fly Computing}}, location = {{Lisbon, Portugal}}, pages = {{119----123}}, publisher = {{IADIS}}, title = {{{SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH}}}, year = {{2020}}, } @inproceedings{15580, abstract = {{This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.}}, author = {{Kersting, Joschka and Geierhos, Michaela}}, booktitle = {{Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)}}, keywords = {{Deep Learning, Natural Language Processing, Aspect-based Sentiment Analysis}}, location = {{Valetta, Malta}}, pages = {{391----400}}, publisher = {{SCITEPRESS}}, title = {{{Aspect Phrase Extraction in Sentiment Analysis with Deep Learning}}}, year = {{2020}}, } @article{35313, abstract = {{The article discusses the explanatory power of conceptual change for research on workplace learning in digitalized workplaces. Interestingly, research on conceptual change is well-established within the area of science education but widely neglected within the broad area of workplace learning research. Digitalization of work establishes new quality of tasks and tools by integrating workers and machines into digital networks. Hence, conceptual change can be considered a core concept for identifying workers’ successful adaption to digital transformation. Therefore, conceptual change research in the area of workplace learning in digitalized workplaces is highly relevant. The article reflects upon reasons, explores the potential of conceptual change for understanding workplace learning in digitalized workplaces, and illustrates the argumentation by exemplarily referring to digitalized farming. Finally, the article provides suggestions for future research.}}, author = {{Harteis, Christian and Goller, Michael and Caruso, Carina}}, journal = {{Frontiers in Education}}, keywords = {{conceptual change, digitalization, workplace learning, professional development, agriculture}}, number = {{1}}, title = {{{Conceptual Change in the Face of Digitalization}}}, doi = {{10.3389/feduc.2020.00001}}, volume = {{5}}, year = {{2020}}, } @article{35298, abstract = {{Im Artikel werden drei verschiedene Lernzugänge (kom-petenzorientiertes, ästhetisches und biographisches Lernen) vorgestellt und aus theoretischer Perspektive deren motivierender Gehalt für selbstreguliertes Lernen in Praxisphasen des Lehramtsstudiumsherausgearbeitet. Als theoretische Grund-lage dient die Selbstbestimmungstheorie als zentrale motivationale Theorie zur Erklärung selbstbestimmten Handelns.}}, author = {{Caruso, Carina and Adammek, Christine and Bonanati, Sabrina and Wiescholek, Sybille}}, issn = {{2625-0675}}, journal = {{Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion}}, keywords = {{ästhetische Forschung, Biographiearbeit, Praxissemester, Professionalisierung, selbstreguliertes Lernen, Motivation / aesthetic research, biographical work, long-term internship, profes-sionalization, self-regulated learning, motivation}}, number = {{1}}, pages = {{18--33}}, title = {{{Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen}}}, doi = {{10.4119/hlz-2540}}, volume = {{3}}, year = {{2020}}, } @article{33299, abstract = {{The aim of this study was to find out whether teaching how to search for literature would be more beneficial to students and teachers if done online through short videos rather than in person during course time. To find out whether online videos are more beneficial, two courses were asked to fill in questionnaires, one at the beginning and one at the end of the semester. One of the courses received the input online via videos and were given an exercise to put the newly learned skills to use, the other course served as a control group and learned how to search for literature during the course. The results show that while the difference between the two groups is not significant, the videos can still be regarded as being more beneficial than teaching the necessary skills during course time.}}, author = {{Hahn, Charlotte Anna}}, issn = {{ISSN 2199-8825}}, journal = {{die hochschullehre}}, keywords = {{E-Learning, information competence, literature, library, research}}, number = {{6}}, title = {{{Informationskompetenz durch E-Learning? Durch Lernvideos nach Literatur suchen}}}, year = {{2020}}, } @inproceedings{48897, abstract = {{In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.}}, author = {{Seiler, Moritz and Pohl, Janina and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from {Nature} (PPSN XVI)}}, isbn = {{978-3-030-58111-4}}, keywords = {{Automated algorithm selection, Deep learning, Feature-based approaches, Traveling Salesperson Problem}}, pages = {{48–64}}, publisher = {{Springer-Verlag}}, title = {{{Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem}}}, doi = {{10.1007/978-3-030-58112-1_4}}, year = {{2020}}, } @article{9853, abstract = {{Business model innovation is typically taught in small seminars at universities. Teaching this intrinsically task-oriented subject to a large number of students is a challenge. In this paper we address this challenge by proposing an experiential and interactive approach to teaching business models in a large classroom setting.}}, author = {{Szopinski, Daniel}}, journal = {{Journal of Business Models}}, keywords = {{Business model teaching, peer assessment, experiential learning}}, number = {{3}}, pages = {{90--100}}, title = {{{Squaring the circle: Business model teaching in large classroom settings}}}, volume = {{7}}, year = {{2019}}, } @inproceedings{13107, abstract = {{In this paper, we first outline a Hypothetical Learning Trajectory (HLT), which aims at a formal understanding of the rules for manipulating integers. The HLT is based on task formats, which promote algebraic thinking in terms of generalizing rules from the analysis of patterns and should be familiar to students from their mathematics education experiences in elementary school. Second, we analyze two students' actual learning process based on Peircean semiotics. The analysis shows that the actual learning process diverges from the hypothesized learning process in that the students do not relate the different levels of the diagrams in a way that allows them to extrapolate the rule for the subtraction of negative numbers. Based on this finding, we point out consequences for the design of the tasks.}}, author = {{Schumacher, Jan and Rezat, Sebastian}}, booktitle = {{Proceedings of the Eleventh Congress of the European Society for Research in Mathematics Education (CERME11, February 6 – 10, 2019)}}, editor = {{Jankvist, Uffe Thomas and Van den Heuvel-Panhuizen, Marja and Veldhuis, Michiel}}, keywords = {{diagrammatic reasoning, hypothetical learning trajectory, induction extrapolatory method, integers, negative numbers, permanence principle, semiotics}}, location = {{Utrecht}}, publisher = {{Freudenthal Group & Freudenthal Institute, Utrecht University and ERME}}, title = {{{A Hypothetical Learning Trajectory for the Learning of the Rules for Manipulating Integers}}}, year = {{2019}}, } @inproceedings{13443, abstract = {{This work considers the problem of control and resource allocation in networked systems. To this end, we present DIRA a Deep reinforcement learning based Iterative Resource Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards large-scale problems where control and scheduling need to act jointly to optimize performance. DIRA can be used to schedule general time-domain optimization based controllers. In the present work, we focus on control designs based on suitably adapted linear quadratic regulators. We apply our algorithm to networked systems with correlated fading communication channels. Our simulations show that DIRA scales well to large scheduling problems.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Quevedo, Daniel}}, booktitle = {{Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems}}, keywords = {{Networked control systems, deep reinforcement learning, large-scale systems, resource scheduling, stochastic control}}, location = {{Chicago, USA}}, title = {{{Deep reinforcement learning for scheduling in large-scale networked control systems}}}, year = {{2019}}, } @inproceedings{15332, abstract = {{Artificial intelligence (AI) has the potential for far-reaching – in our opinion – irreversible changes. They range from effects on the individual and society to new societal and social issues. The question arises as to how students can learn the basic functioning of AI systems, what areas of life and society are affected by these and – most important – how their own lives are affected by these changes. Therefore, we are developing and evaluating school materials for the German ”Science Year AI”. It can be used for students of all school types from the seventh grade upwards and will be distributed to about 2000 schools in autumn with the support of the Federal Ministry of Education and Research. The material deals with the following aspects of AI: Discussing everyday experiences with AI, how does machine learning work, historical development of AI concepts, difference between man and machine, future distribution of roles between man and machine, in which AI world do we want to live and how much AI would we like to have in our lives. Through an accompanying evaluation, high quality of the technical content and didactic preparation is achieved in order to guarantee the long-term applicability in the teaching context in the different age groups and school types. In this paper, we describe the current state of the material development, the challenges arising, and the results of tests with different classes to date. We also present first ideas for evaluating the results.}}, author = {{Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte, Carsten}}, booktitle = {{ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings}}, editor = {{Jasutė, Eglė and Pozdniakov, Sergei}}, isbn = {{978-9925-553-27-3}}, keywords = {{Artificial Intelligence, Machine Learning, Teaching Material, Societal Aspects, Ethics. Social Aspects, Science Year, Simulation Game}}, location = {{Lanarca}}, pages = {{65 -- 73}}, title = {{{Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material}}}, volume = {{12}}, year = {{2019}}, } @article{48877, abstract = {{OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online.}}, author = {{Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}}, issn = {{0943-4062}}, journal = {{Computational Statistics}}, keywords = {{Databases, Machine learning, R, Reproducible research}}, number = {{3}}, pages = {{977–991}}, title = {{{OpenML: An R Package to Connect to the Machine Learning Platform OpenML}}}, doi = {{10.1007/s00180-017-0742-2}}, volume = {{34}}, year = {{2019}}, }