Motivierende Lernzugänge als Ausgangspunkt der Professionalisierung angehender Lehrer_innen - Caruso, Carina; Adammek, Christine; Bonanati, Sabrina; Wiescholek, Sybille (Herausforderung Lehrer*innenbildung - Zeitschrift Zur Konzeption, Gestaltung Und Diskussion)
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.
Keywords: ästhetische Forschung; Biographiearbeit; Praxissemester; Professionalisierung; selbstreguliertes Lernen; Motivation / aesthetic research; biographical work; long-term internship; profes-sionalization; self-regulated learning; motivation

Informationskompetenz durch E-Learning? Durch Lernvideos nach Literatur suchen - Hahn, Charlotte Anna (die hochschullehre)
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.
Keywords: E-Learning; information competence; literature; library; research

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem - Seiler, Moritz; Pohl, Janina; Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Parallel Problem Solving from Nature (PPSN XVI))
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.
Keywords: Automated algorithm selection; Deep learning; Feature-based approaches; Traveling Salesperson Problem

Squaring the circle: Business model teaching in large classroom settings - Szopinski, Daniel (Journal of Business Models)
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.
Keywords: Business model teaching; peer assessment; experiential learning

A Hypothetical Learning Trajectory for the Learning of the Rules for Manipulating Integers - Schumacher, Jan; Rezat, Sebastian (Proceedings of the Eleventh Congress of the European Society for Research in Mathematics Education (CERME11, February 6 – 10, 2019))
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.
Keywords: diagrammatic reasoning; hypothetical learning trajectory; induction extrapolatory method; integers; negative numbers; permanence principle; semiotics

Deep reinforcement learning for scheduling in large-scale networked control systems - Redder, Adrian; Ramaswamy, Arunselvan; Quevedo, Daniel (Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems)
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.
Keywords: Networked control systems; deep reinforcement learning; large-scale systems; resource scheduling; stochastic control

Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material - Schlichtig, Michael; Opel, Simone Anna; Budde, Lea; Schulte, Carsten (ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings)
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.
Keywords: Artificial Intelligence; Machine Learning; Teaching Material; Societal Aspects; Ethics. Social Aspects; Science Year; Simulation Game

OpenML: An R Package to Connect to the Machine Learning Platform OpenML - Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd (Computational Statistics)
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.
Keywords: Databases; Machine learning; R; Reproducible research

Wissensmanagement unter Bedingungen von Arbeit 4.0 - Harteis, Christian; Fischer, Christoph (Handbuch Gestaltung digitaler und vernetzter Arbeitswelten)
Keywords: i40; learning culture

Digitalisation of Work: Between Affordances and Constraints for Learning at Work - Fischer, Christoph; Goller, Michael; Brinkmann, Lorraine; Harteis, Christian (Digital Workplace Learning)
Keywords: i40; learning culture