The digital home learning environment and its relation to children’s ICT self-efficacy - Bonanati, Sabrina; Buhl, Heike M. (Learning Environments Research)
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.
Keywords: Digital media use; Gender; Home learning environment; ICT self-efcacy; Motivation; Parental involvement

Digitalisation in Agriculture: Knowledge and Learning Requirements of German Dairy Farmers - Goller, Michael; Caruso, Carina; Harteis, Christian (International Journal for Research in Vocational Education and Training)
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.
Keywords: Work-Based Learning; Organisational Change; Digital Competences; Qualitative Research; Digitalisation; Farming; Dairy; VET; Vocational Education and Training

The Concept of a Digital Twin and Its Potential for Learning Organizations - Berisha-Gawlowski, Angelina; Caruso, Carina; Harteis, Christian (Digital Transformation of Learning Organizations)
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?
Keywords: Digital twin; Learning organization; Change; Team learning; Professional development

Conventional and Machine Learning Approaches for Network and Service Coordination - Schneider, Stefan Balthasar
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.
Keywords: nfv; coordination; machine learning; reinforcement learning; phd; digest

PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking Data - Müller, Oliver; Caron, Matthew; Döring, Michael; Heuwinkel, Tim; Baumeister, Jochen (8th Workshop on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021))
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.
Keywords: expected possession value; handball; tracking data; time series classification; deep learning

Conceptualising and measuring domain-specific content knowledge of early childhood educators: A systematic review - Bruns, Julia; Gasteiger, Hedwig; Strahl, Carolin (Review of Education)
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.
Keywords: content knowledge; domain-specific learning; early childhood education; teacher knowledge

Self-Driving Network and Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Manzoor, Adnan; Qarawlus, Haydar; Schellenberg, Rafael; Karl, Holger; Khalili, Ramin; Hecker, Artur (IEEE International Conference on Network and Service Management (CNSM))
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.
Keywords: self-driving networks; self-learning; network coordination; service coordination; reinforcement learning; deep learning; nfv

SEMANTIC TAGGING OF REQUIREMENT DESCRIPTIONS: A TRANSFORMER-BASED APPROACH - Kersting, Joschka; Bäumer, Frederik Simon (PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON APPLIED COMPUTING 2020)
Keywords: Software Requirements; Natural Language Processing; Transfer Learning; On-The-Fly Computing

Aspect Phrase Extraction in Sentiment Analysis with Deep Learning - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020))
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.
Keywords: Deep Learning; Natural Language Processing; Aspect-based Sentiment Analysis

Conceptual Change in the Face of Digitalization - Harteis, Christian; Goller, Michael; Caruso, Carina (Frontiers in Education)
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.
Keywords: conceptual change; digitalization; workplace learning; professional development; agriculture

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

ML-Plan for Unlimited-Length Machine Learning Pipelines - Wever, Marcel Dominik; Mohr, Felix; Hüllermeier, Eyke (ICML 2018 AutoML Workshop)
In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated. Various AutoML tools have already been introduced to provide out-of-the-box machine learning functionality. More specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand. Except for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline. However, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets. In this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length. We evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.
Keywords: automated machine learning; complex pipelines; hierarchical planning

Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure - Kim, Yeongsu; Lee, Seungwoo; Dollmann, Markus; Geierhos, Michaela (International Journal of Advanced Science and Technology)
A user generally writes software requirements in ambiguous and incomplete form by using natural language; therefore, a software developer may have difficulty in clearly understanding what the meanings are. To solve this problem with automation, we propose a classifier for semantic annotation with manually pre-defined semantic categories. To improve our classifier, we carefully designed syntactic features extracted by constituency and dependency parsers. Even with a small dataset and a large number of classes, our proposed classifier records an accuracy of 0.75, which outperforms the previous model, REaCT.
Keywords: Software Engineering; Natural Language Processing; Semantic Annotation; Machine Learning; Feature Engineering; Syntactic Structure

Information Security Investments: An Exploratory Multiple Case Study on Decision-Making, Evaluation and Learning - Weishäupl, Eva; Yasasin, Emrah; Schryen, Guido (Computers & Security)
The need to protect resources against attackers is reflected by huge information security investments of firms worldwide. In the presence of budget constraints and a diverse set of assets to protect, organizations have to decide in which IT security measures to invest, how to evaluate those investment decisions, and how to learn from past decisions to optimize future security investment actions. While the academic literature has provided valuable insights into these issues, there is a lack of empirical contributions. To address this lack, we conduct a theory-based exploratory multiple case study. Our case study reveals that (1) firms? investments in information security are largely driven by external environmental and industry-related factors, (2) firms do not implement standardized decision processes, (3) the security process is perceived to impact the business process in a disturbing way, (4) both the implementation of evaluation processes and the application of metrics are hardly existent and (5) learning activities mainly occur at an ad-hoc basis.
Keywords: Information Security Investments; Multiple Case Study; Organizations; Single Loop Learning; Double Loop Learning

Reciprocal effects between self-concept of ability and performance: A longitudinal study of children with learning disabilities in inclusive versus exclusive elementary education - Gorges, Julia; Neumann, Phillip; Wild, Elke; Stranghöner, Daniela; Lütje-Klose, Birgit (Learning and Individual Differences)
Ample empirical research from regular school settings documents reciprocal effects between academic performance and academic self-concept of ability (ASC), supporting what is known as a reciprocal effects model (REM). The present article investigates a REM in the domain of reading performance in a sample of elementary students with special educational needs in learning (SEN-L) who received special educational support in exclusive versus inclusive settings (N = 446). In exclusive settings, SEN-L students attend special schools and are completely separated from regular students. By contrast, SEN-L students in inclusive settings attend regular schools and are educated in classes with regular students. In both settings, SEN-L students are not graded and taught based on individual learning goals, which may affect reciprocal effects between ASC and reading performance. In addition, given that special education for SEN-L students relies heavily on individual reference standards to evaluate performance, we tested individual performance growth of SEN-L students as a predictor of ASC. Analyses of a longitudinal dataset across 3rd and 4th grade revealed some cross-lagged effects and an effect of performance growth on ASC in exclusive settings in particular. The discussion focuses on the role of individualized instruction, grades, peer groups, and individual versus social reference standards for reciprocal effects between ASC and performance as well as practical implications.
Keywords: BiLieF; Special educational needs; Learning disability; Academic selfconcept of ability; Reciprocal effects model; Inclusive education

Leveraging TSP Solver Complementarity through Machine Learning - Kerschke, Pascal; Kotthoff, Lars; Bossek, Jakob; Hoos, Holger H.; Trautmann, Heike (Evolutionary Computation)
The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard problems. Over the years, many different solution approaches and solvers have been developed. For the first time, we directly compare five state-of-the-art inexact solvers— namely, LKH, EAX, restart variants of those, and MAOS— on a large set of well-known benchmark instances and demonstrate complementary performance, in that different instances may be solved most effectively by different algorithms. We leverage this complementarity to build an algorithm selector, which selects the best TSP solver on a per-instance basis and thus achieves significantly improved performance compared to the single best solver, representing an advance in the state of the art in solving the Euclidean TSP. Our in-depth analysis of the selectors provides insight into what drives this performance improvement.
Keywords: automated algorithm selection; machine learning.; performance modeling; Travelling Salesperson Problem

Experiential or Self-Regulated Learning: A Critical Reflection of Entrepreneurial Learning Processes - Fust, Alexander Paul; Jenert, Tobias; Winkler, Christoph (Entrepreneurship Research Journal)
Research on entrepreneurial learning highlights the importance of experience and prior knowledge to entrepreneurial success. However, a conundrum remains and we are still seeking answers as to why some novice entrepreneurs learn successfully from their experiences and succeed, while some experienced entrepreneurs fail with their ventures. In order to advance the discussion about the role of experience during entrepreneurial learning, our critical reflection aims to (1) highlight some of the shortcomings of experiential learning theory (ELT) and (2) illustrate how alternative theoretical perspectives have the potential to advance our conceptual understanding of entrepreneurial learning processes. We argue for an explanation of entrepreneurial learning as a dynamic and self-regulated process that relies on planning, monitoring, and self-reflection.
Keywords: entrepreneurial learning; experiential learning; self-regulated learning

Human agency at work: An active approach towards expertise development - Goller, Michael
Michael Goller gives a structured overview of the current discourses of human agency in relation to professional learning and development. Based on this discussion, the author develops a theoretical framework including human agency as an individual feature (i. e., a disposition) as well as a set of self-initiated and goal-directed behaviours that are assumed to affect employees’ learning and development (e. g., crafting of new work experiences). He then further specifies this theoretical framework and investigates it empirically in the domain of geriatric care nursing. Based on the findings of the three empirical studies conducted, the author discusses the relevance of human agency for the development of professional expertise of geriatric care nurses. The work received the American Educational Research Association (AERA) Workplace Learning SIG 2017 Dissertation of the Year Award.
Keywords: Workplace learning; Agency; Expertise

A configuration-based recommender system for supporting e-commerce decisions - Scholz, Michael; Dorner, Verena; Schryen, Guido; Benlian, Alexander (European Journal of Operational Research)
Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.
Keywords: E-Commerce; Recommender System; Attribute Weights; Configuration System; Decision Support

Semantic Annotation of Software Requirements with Language Frame - Kim, Yeong-Su; Lee, Seung-Woo; Dollmann, Markus; Geierhos, Michaela (International Journal of Software Engineering for Smart Device)
An end user generally writes down software requirements in ambiguous expressions using natural language; hence, a software developer attuned to programming language finds it difficult to understand th meaning of the requirements. To solve this problem we define semantic categories for disambiguation and classify/annotate the requirement into the categories by using machine-learning models. We extensively use a language frame closely related to such categories for designing features to overcome the problem of insufficient training data compare to the large number of classes. Our proposed model obtained a micro-average F1-score of 0.75, outperforming the previous model, REaCT.
Keywords: Natural Language Processing; Semantic Annotation; Machine Learning

The Multifaceted Nature of Agency and Professional Learning - Paloniemi, Susanna; Goller, Michael (Agency at work: An agentic perspective on professional learning and development)
The present volume has aimed to cover a broad range of approaches to agency at work, exploring its relationship with professional learning and development. Thus, the chapters included in this book have discussed the role of agency in learning and development, considering a variety of working life contexts and applying both conceptual and empirical perspectives. This final chapter provides an overview of both the conceptual approaches and the empirical implementations. We see the perspectives as complementary. From the content of the book, we discern the phenomena as falling on two main dimensions, clustering at opposite ends of these dimensions. Thus, the following contrasts are evidenced: (a) agency understood as a personal capacity, vs. agency as behaviour, and (b) agency as an individual phenomenon, vs. agency as a collective phenomenon. All the chapters emphasise that agency is needed for learning and development. However, they differ in how they view the relationships between the concepts. They also exhibit differences in the empirical decisions taken and the research strategies chosen. In this concluding chapter, we discuss the main similarities and differences emerging from the chapters. We also highlight avenues for future research on agency and its relationship with professional learning.
Keywords: Agency; Workplace learning; Professional development

Listening to learners’ voices: Qualitative aspects of pronunciation learning during study abroad - Müller, Mareike (Journal of Second Language Pronunciation)
This article investigates learners’ perceptions on pronunciation learning in study-abroad contexts from a qualitative perspective. While previous research focused mainly on quantitative measurements of pronunciation gains with mixed results, this study takes a more learner-centered approach and examines the impact of socio-psychological factors on learning foreign pronunciation, which appears to be a highly individual and at times conflict-prone process with which sojourners are confronted. The study draws on the cases of five Canadian students who studied abroad at German universities for one or two semesters. The data collection involved a learning history questionnaire; semi-structured interviews pre-, mid-, and post-sojourn; and bi-weekly e-journals. The data was analyzed and interpreted within the framework of narrative analysis. The results show how sojourners’ beliefs about the importance of pronunciation, community participation, identity-related challenges, and obstacles to pronunciation learning influence and help explain individually different learning behaviors and results.
Keywords: pronunciation learning; study abroad; qualitative approach; narrative analysis; learner beliefs; socio-psychological learning factors

Embodied Language Learning and Cognitive Bootstrapping: Methods and Design Principles - Lyon, Caroline; Nehaniv, Chrystopher L.; Saunders, Joe; Belpaeme, Tony; Bisio, Ambra; Fischer, Kerstin; Forster, Frank; Lehmann, Hagen; Metta, Giorgio; Mohan, Vishwanathan; Morse, Anthony; Nolfi, Stefano; et al. (International Journal of Advanced Robotic Systems)
Co-development of action, conceptualization and social interaction mutually scaffold and support each other within a virtuous feedback cycle in the development of human language in children. Within this framework, the purpose of this article is to bring together diverse but complementary accounts of research methods that jointly contribute to our understanding of cognitive development and in particular, language acquisition in robots. Thus, we include research pertaining to developmental robotics, cognitive science, psychology, linguistics and neuroscience, as well as practical computer science and engineering. The different studies are not at this stage all connected into a cohesive whole; rather, they are presented to illuminate the need for multiple different approaches that complement each other in the pursuit of understanding cognitive development in robots. Extensive experiments involving the humanoid robot iCub are reported, while human learning relevant to developmental robotics has also contributed useful results. Disparate approaches are brought together via common underlying design principles. Without claiming to model human language acquisition directly, we are nonetheless inspired by analogous development in humans and consequently, our investigations include the parallel co-development of action, conceptualization and social interaction. Though these different approaches need to ultimately be integrated into a coherent, unified body of knowledge, progress is currently also being made by pursuing individual methods.
Keywords: Robot Language; Human Robot Interaction; HRI; Developmental Robotics; Cognitive Bootstrapping; Statistical Learning

An Alternative to Mapping a Word onto a Concept in Language Acquisition: Pragmatic Frames - Rohlfing, Katharina; Wrede, Britta; Vollmer, Anna-Lisa; Oudeyer, Pierre-Yves (FRONTIERS IN PSYCHOLOGY)
The classic mapping metaphor posits that children learn a word by mapping it onto a concept of an object or event. However, we believe that a mapping metaphor cannot account for word learning, because even though children focus attention on objects, they do not necessarily remember the connection between the word and the referent unless it is framed pragmatically, that is, within a task. Our theoretical paper proposes an alternative mechanism for word learning. Our main premise is that word learning occurs as children accomplish a goal in cooperation with a partner. We follow Bruner's (1983) idea and further specify pragmatic frames as the learning units that drive language acquisition and cognitive development. These units consist of a sequence of actions and verbal behaviors that are co-constructed with a partner to achieve a joint goal. We elaborate on this alternative, offer some initial parametrizations of the concept, and embed it in current language learning approaches.
Keywords: language acquisition; pragmatics; infants' social learning; frames; learning and memory; developmental robotics

Spielend Lehrer/in werden: problembasiertes Lernen mit virtuellen Schülerinnen/Schülern. - Praetorius, Saskia; Al-Kabbani, Daniel; Bohndick, Carla; Hilkenmeier, Johanna; König, Sebastian T.; Müsche, Hannah S.; Sommer, Sabrina; Klingsieck, Katrin B. (Zeitschrift für Hochschulentwicklung)
Über das Lehramtsstudium sollen Studierende unter anderem dazu befähigt werden, die Leistung ihrer Schüler/innenzu bewerten. Dazu müssen sie Einflussfaktoren auf Schulleistung kennen und diese richtig diagnostizieren und fördern können. Mit diesem Beitrag wird eine game-und E-Learning-gestützte Lernumgebung vorgestellt, in der Studierende –anders als in vielen inputorientierten Seminarkonzepten –in einem virtuellen Klassenzimmer an realitätsnahen Fällen lernen,problembasiert zu diagnostizieren und zu fördern. Über denEinsatz der Lernumgebung in der Lehre wird berichtet, erste Rückmeldungen von Lehrenden und Studierenden werden erläutert und weitere Planungsschritte dargestellt.
Keywords: E-Learning; digitale Medien; problembasiertes Lernen; Lehrerbildung; Diagnostik

Learning Boolean Specifications - Bubeck, Uwe; Kleine Büning, Hans (Artificial Intelligence)
In this paper we consider an extended variant of query learning where the hidden concept is embedded in some Boolean circuit. This additional processing layer modifies query arguments and answers by fixed transformation functions which are known to the learner. For this scenario, we provide a characterization of the solution space and an ordering on it. We give a compact representation of the minimal and maximal solutions as quantified Boolean formulas and we adapt the original algorithms for exact learning of specific classes of propositional formulas.
Keywords: Query learning; Propositional logic

Die Bedeutung subjektiver Sichtweisen für die Implementierung betrieblicher Lernkultur - Fischer, Christoph; Harteis, Christian (Informelles Lernen, Annäherungen - Problemlagen - Forschungsbefunde)
Keywords: Epistemische Überzeugungen; learning culture

The Value of Self-Service: Long-Term Effects of Technology-Based Self-Service Usage on Customer Retention. - Scherer, Anne; Wünderlich, Nancy; Von Wangenheim, Florian (MIS Quarterly)
Advancements in information technology have changed the way customers experience a service encounter and their relationship with service providers. Especially technology-based self-service channels have found their way into the 21st century service economy. While research embraces these channels for their cost-efficiency, it has not examined whether a shift from personal to self-service affects customer–firm relationships. Drawing from the service-dominant logic and its central concept of value-in-context, we discuss customers’ value creation in self-service and personal service channels and examine the long-term impact of these channels on customer retention. Using longitudinal customer data, we investigate how the ratio of self-service versus personal service use influences customer defection over time. Our findings suggest that the ratio of self-service to personal service used affects customer defection in a U-shaped manner, with intermediate levels of both self-service and personal service use being associated with the lowest likelihood of defection. We also find that this effect mitigates over time. We conclude that firms should not shift customers toward self-service channels completely, especially not at the beginning of a relationship. Our study underlines the importance of understanding when and how self-service technologies create valuable customer experiences and stresses the notion of actively managing customers’ cocreation of value.
Keywords: customer defection; customer retention; e-service; longitudinal; Self-service; value-in-context

The self-fulfilling prophecy of fear of academic failure - Brahm, Taiga; Jenert, Tobias; Wagner, Dietrich
Academic success in Higher Education is influenced by a number of different factors. This paper tackles the question if the individual levels of motivation, anxiety, enjoyment and self-efficacy, measured immediately before entering university, influence the probability of academic success. Former studies have shown an influence of the high school grade, the learning environment and motivational variables. They do not investigate, however, the individual levels of the mentioned constructs before the beginning of the studies. This research was conducted at the University of St. Gallen/Switzerland. The sample includes 695 first-year students who provided information about the individual level of the mentioned constructs. Descriptive statistics show that on average the students are highly motivated, have a high level of self-efficacy and are looking forward to their studies before their beginning. Yet, there are students who have a high level of fear of failure in the study in spite of their high motivation and self-efficacy. A logistic regression shows that there is a significant effect of fear of failure on the probability of study success. This paper shows that fear of failure can increase the probability of academic failure and thus become a self-fulfilling prophecy. It confirms fear as an important factor for academic success. Furthermore, other important factors for academic success, for example the high school grade, could be confirmed in this study
Keywords: Quantitative methods; Student learning; Emotion and Cognition; Social sciences; Higher education; Motivation and Emotion; Fear of Failure

A Multi-Theoretical Literature Review on Information Security Investments using the Resource-Based View and the Organizational Learning Theory - Weishäupl, Eva; Yasasin, Emrah; Schryen, Guido (International Conference on Information Systems)
The protection of information technology (IT) has become and is predicted to remain a key economic challenge for organizations. While research on IT security investment is fast growing, it lacks a theoretical basis for structuring research, explaining economic-technological phenomena and guide future research. We address this shortcoming by suggesting a new theoretical model emerging from a multi-theoretical perspective adopt-ing the Resource-Based View and the Organizational Learning Theory. The joint appli-cation of these theories allows to conceptualize in one theoretical model the organiza-tional learning effects that occur when the protection of organizational resources through IT security countermeasures develops over time. We use this model of IT security invest-ments to synthesize findings of a large body of literature and to derive research gaps. We also discuss managerial implications of (closing) these gaps by providing practical ex-amples.
Keywords: Information Security; Investment; Literature review; Resource-based View; Organi-zational Learning Theory; Multi-theoretical Perspective

Autonomous Learning of Representations - Walter, Oliver; Haeb-Umbach, Reinhold; Mokbel, Bassam; Paassen, Benjamin; Hammer, Barbara (KI - Kuenstliche Intelligenz)
Besides the core learning algorithm itself, one major question in machine learning is how to best encode given training data such that the learning technology can efficiently learn based thereon and generalize to novel data. While classical approaches often rely on a hand coded data representation, the topic of autonomous representation or feature learning plays a major role in modern learning architectures. The goal of this contribution is to give an overview about different principles of autonomous feature learning, and to exemplify two principles based on two recent examples: autonomous metric learning for sequences, and autonomous learning of a deep representation for spoken language, respectively.
Keywords: Representation learning; Metric learning; Deep representation; Spoken language

Zum Einfluss des didaktischen Designs auf das (motivationale) Erleben von Lehrveranstaltungen: Eine quantitative Fallstudie im Rahmen der Methodeneinführung in den Bildungswissenschaften - Goller, Michael (die hochschullehre)
Im Sinne eines Scholarship of Teaching and Learning wird in diesem Aufsatz die Neugestaltung einer eigenständig durchgeführten Lehrveranstaltung im Bereich der Forschungsmethoden empirisch untersucht. Die neugestaltete Lehrveranstaltung integriert sowohl direkt-instruktionale als auch kooperativ-offene Sitzungsformate und wird mit einer thematisch ähnlichen Lehrveranstaltung des Jahres 2012 verglichen, welche fast ausschließlich im Vorlesungsstil (direkt-instruktional) gehalten wurde. Im Mittelpunkt des Evaluationsvorhabens standen folgende Forschungsfragen: (1) Unterscheiden sich die summativen Evaluationen der Lehrveranstaltungen der Jahre 2012 und 2014 in den Variablen subjektiver Lernerfolg, Motivation zur selbstständigen Auseinandersetzung mit den Inhalten sowie Gesamteindruck des Seminars?, (2) Welchen Einfluss hat die Form der Sitzungsgestaltung (direkt-instruktional vs. kooperativ-offen) auf das Erleben von Kompetenz, Autonomie und sozialer Eingebundenheit?, (3) Welche Form der Sitzungsgestaltung wird von den Studierenden stärker akzeptiert? und (4) Welchen Einfluss hat die Form der Sitzungsgestaltung auf den subjektiven Lernerfolg der Studierenden? Empirisch zeigte sich, dass die neugestaltete Lehrveranstaltung des Jahres 2014 von den Studierenden in fast allen Variablen als lernwirksamer eingeschätzt wurde. Des Weiteren ergaben sich Hinweise, dass sich kooperativ-offene Sitzungsformate positiv auf motivationale Ausgangsbedingungen auswirken.
Keywords: Direkte Instruktion; Evaluation; Kooperatives Lernen; Methodenausbildung; Paderborn; Scholarship of Teaching and Learning

Alignment to the Actions of a Robot - Vollmer, Anna-Lisa; Rohlfing, Katharina; Wrede, Britta; Cangelosi, Angelo (International Journal of Social Robotics)
Alignment is a phenomenon observed in human conversation: Dialog partners' behavior converges in many respects. Such alignment has been proposed to be automatic and the basis for communicating successfully. Recent research on human-computer dialog promotes a mediated communicative design account of alignment according to which the extent of alignment is influenced by interlocutors' beliefs about each other. Our work aims at adding to these findings in two ways. (a) Our work investigates alignment of manual actions, instead of lexical choice. (b) Participants interact with the iCub humanoid robot, instead of an artificial computer dialog system. Our results confirm that alignment also takes place in the domain of actions. We were not able to replicate the results of the original study in general in this setting, but in accordance with its findings, participants with a high questionnaire score for emotional stability and participants who are familiar with robots align their actions more to a robot they believe to be basic than to one they believe to be advanced. Regarding alignment over the course of an interaction, the extent of alignment seems to remain constant, when participants believe the robot to be advanced, but it increases over time, when participants believe the robot to be a basic version.
Keywords: learning; Human-robot interaction; Alignment; Robot social; Action understanding

On the assessment of attitudes towards studying—development and validation of a questionnaire - Brahm, Taiga; Jenert, Tobias (Learning and individual differences : journal of psychology and education)
This research aims to develop and validate an instrument for the assessment of attitudes towards two particular objects: the Higher Education Institution (HEI) and the process of studying. Investigating attitudes towards studying at a HEI in a comprehensive way addresses an important research gap. It connects students' perception of the institution with teaching and learning. To validate the Assessment of Students' Attitudes towards Studying (ASAtS) Questionnaire, data was gathered at three different universities in Switzerland (820 students), Sweden (167) and Germany (133). Overall, the results show the internal consistency of the ASAtS. Its nomological validity is also supported by correlations with other constructs, such as intrinsic motivation and study performance. The ASAtS contributes to the theory on students learning by broadening the scope of research beyond learning in a narrow sense. From a practical point of view, it provides a tool for HE management to monitor students' perception of their HEI.
Keywords: Attitude; Student learning; Higher Education Institution; university; student experience

Augmented reality in informal learning environments: A field experiment in a mathematics exhibition - Sommerauer, Peter; Müller, Oliver (Computers and Education)
Keywords: Augmented reality; Cognitive theory of multimedia learning; Field experiment; Informal learning; Museum

Tutoring in adult-child-interaction: On the loop of the tutor's action modification and the recipient's gaze - Pitsch, Karola; Vollmer, Anna-Lisa; Rohlfing, Katharina; Fritsch, Jannik; Wrede, Britta (Interaction Studies)
Research of tutoring in parent-infant interaction has shown that tutors - when presenting some action - modify both their verbal and manual performance for the learner (‘motherese’, ‘motionese’). Investigating the sources and effects of the tutors’ action modifications, we suggest an interactional account of ‘motionese’. Using video-data from a semi-experimental study in which parents taught their 8 to 11 month old infants how to nest a set of differently sized cups, we found that the tutors’ action modifications (in particular: high arches) functioned as an orienting device to guide the infant’s visual attention (gaze). Action modification and the recipient’s gaze can be seen to have a reciprocal sequential relationship and to constitute a constant loop of mutual adjustments. Implications are discussed for developmental research and for robotic ‘Social Learning’. We argue that a robot system could use on-line feedback strategies (e.g. gaze) to pro-actively shape a tutor’s action presentation as it emerges.
Keywords: conversation analysis; interactional coordination; adult-child-interaction; feedback; gaze; quantification; social learning; motionese; tutoring

Herausforderungen der Kompetenzorientierung in der Studienprogrammentwicklung - Brahm, Taiga; Jenert, Tobias (Zeitschrift für Hochschulentwicklung)
Kompetenzorientierung ist ein wichtiges aktuelles Ziel bei der Studienprogrammentwicklung. Allerdings ist die Umsetzung in der Praxis mit zwei wesentlichen Herausforderungen verbunden: So ist zum einen der Wechsel von disziplinären Inhalten hin zu Kompetenzen wenig intuitiv; zum anderen wird die Formulierung so genannter Learning Outcomes als administrative Übung betrachtet. An der Universität St. Gallen wurde ein systematischer Prozess für die Qualitätsentwicklung der Studienprogramme eingeführt. Im Beitrag wird dieser Prozess im Hinblick auf die Kompetenzorientierung reflektiert. Es erscheint wesentlich, den Programmen möglichst viel Freiraum auf ihrem Weg zur Kompetenzorientierung zu überlassen und sie auf diese Weise zu unterstützen.
Keywords: Kompetenzorientierung; Studienprogrammentwicklung; Hochschulentwicklung; Hochschule; Learning Outcomes

On the assessment of attitudes towards the study process and the university: Attitudes towards the Study Environment Questionnaire (ASEQ) - Brahm, Taiga; Jenert, Tobias; Euler, Dieter
The purpose of the research was to develop and validate an instrument for the assessment of attitudes towards the study environment in higher education contexts. The questionnaire is designed to measure students' attitudes towards two particular objects: the university (or Higher Education Institution) and the process of studying. Five studies at two different universities were conducted to develop and validate the Attitudes towards the Study Environment Questionnaire (ASEQ). In total, 1640 students filled in the questionnaire. Reliability tests and exploratory factor analysis as well as confirmatory factor analysis showed satisfactory psychometric characteristics of the ASEQ. Each part of the questionnaire is internally consistent and construct validity of the scales is supported by correlations with other constructs as assumed by current theories: Positive attitudinal constructs (normative behavior, autonomy, joy, self-efficacy, and task value) are positively related to intrinsic motivation and the expected study performance while anxiety is negatively related to these constructs. Since attitudes towards the study situation are connected to student engagement and motivation, the study contributes to the theory of student learning by providing a validated instrument to assess attitudes. Thus, the ASEQ can support further research on student performance and development by providing an instrument for the hitherto neglected but nonetheless extremely relevant domain of attitudes. Also, the questionnaire can be used as a diagnostic instrument for higher education faculty and administration to trace students' attitudinal development over time - a factor of prime importance for student socialization during the introductory phase of studying.
Keywords: attitudes; teaching and learning; studying; higher education institution; theory of planned behavior; instrument development

Educating attention: recruiting, maintaining, and framing eye contact in early natural mother-infant interactions - Nomikou, Iris; Rohlfing, Katharina; Szufnarowska, Joanna (Interaction Studies)
In a longitudinal naturalistic study, we observed German mothers interacting with their infants when they were 3 and 6 months old. Pursuing the idea that infants’ attention is socialized in everyday interactions, we explored whether eye contact is reinforced selectively by behavioral modification in the input provided to infants. Applying a microanalytical approach focusing on the sequential organization of interaction, we explored how the mother draws the infant’s attention to herself and how she tries to maintain attention when the infant is looking at her. Results showed that eye contact is reinforced by specific infant-directed practices: interrogatives and conversational openings, multimodal stimulation, repetition, and imitation. In addition, these practices are contingent on the infant’s own behavior. By comparing the two data points (3 and 6 months), we showed how the education of attention evolves hand-in-hand with the developing capacities of the infant.
Keywords: interactional adaptation; multimodal input; social learning; ecology of attention; eye contact

Optimierung des einsatzbezogenen Lernens durch Wissensidentifikation - Pottebaum, Jens
Keywords: Arbeitsorganisation; E-Learning; Lernorganisation; Lerntechnik; Wissensmanagement

Didaktische Potenziale des Mobile Learning für die Berufsbildung: Erfahrungen aus einem Pilotprojekt am Center for Young Professionals in Banking in der Schweiz - Seufert, Sabine; Jenert, Tobias; Kuhn-Senn, Andrea (Berufsbildung in Wissenschaft und Praxis)
Mobile Learning liegt in der beruflichen Bildung seit dem Aufkommen von Smartphones, Tablet-PCs1 und speziell entwickelten Applikationen (Mobile Apps) im Trend. Ziel des Beitrags ist es, die didaktischen Potenziale des Mobile Learning durch den Einsatz von Tablet-PCs in der beruflichen Ausbildung zu untersuchen. Als Ausgangspunkt dient dabei das Fallbeispiel des Center for Young Professionals in Banking in der Schweiz, das im Jahr 2011 eine umfassende Pilotstudie durchgeführt hat. Ausgehend vom konkreten Fall werden anschliessend die Potenziale für medienbezogene Kompetenzen sowie eine "didaktische Landkarte" zur Verortung von Mobile-Learning-Szenarien eingeführt, um die verschiedenen pädagogischen Leitvorstellungen dieser Szenarien aufzuzeigen.
Keywords: mobile learning; tablets; tablet learning; mobile technologies; elearning; TEL