@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}}, } @article{23891, abstract = {{Within a pre-post-design, we scrutinized the effects of normative augmented feedback with positive and negative valence on learning motor accuracy, consistency as well as automaticity by means of a dual-task paradigm. Forty-two healthy physical education students were instructed to produce an arm-movement sequence as precisely as possible with regard to three spatial reversal points within a time limit of 1200 ms. Twenty-eight practiced an elbow-extension-flexion-sequence (690 trials) and 14 participants were tested as a control group without feedback practice. Valence of normative feedback was systematically manipulated by means of reference lines in a visual feedback display. The reference lines indicated performance of a putative peer-group either to be superior (negative valence, Normative-Negative-Group) or inferior (positive valence, Normative-Positive-Group) to participants’ actual performance. As a result, dual-task costs (n-back error) significantly decreased solely in the Normative-Positive-Group, p = .003, η2p = .51, but in no other group. Surprisingly, the mean absolute error for the motor task significantly decreased (i.e., precision increased) only in the Normative-Negative-Group with a large effect size, but in none of the other groups. Motor consistency was not significantly affected by the valence of normative feedback. According to the hypotheses of error-provoked attentional control, positive feedback-valence appears to enhance skill automatization, while – unexpectedly – only negative feedback-valence seems to enhance movement precision, which may be explained by effects of feedback valence on the learners aspiration level.}}, author = {{Zobe, Christina and Krause, Daniel and Blischke, Klaus}}, journal = {{Human Movement Science}}, keywords = {{Augmented feedback Automaticity Dual task Motor learning}}, pages = {{529--540}}, publisher = {{Elsevier}}, title = {{{Dissociative effects of normative feedback on motor automaticity and motor accuracy in learning an arm movement sequence}}}, doi = {{https://doi.org/10.1016/j.humov.2019.06.004}}, volume = {{66}}, year = {{2019}}, } @inbook{57886, abstract = {{The research and development project Postdigital Popular Music Pedagogy (PPP) aims at the development of a music pedagogical program oriented towards informal learning in bands. Using the actor network theory, and thus investigating songwriting as a sociomaterial process, we present, exemplify, and discuss the results of the exploration of informal practices. The song as an actor network transforms through several spaces and phases. The starting point is the socio-technical constellation in which the events and the maturation of ideas as organisms are made probable. From there, an iteration of adaptation to musical-aesthetic standards and physical ability begins: The recording, internal publishing, and rehearsing phases, translate the idea from the workpiece to the object of dispatch into technical requirements. This is completed by the publication phase, in which the song idea is presented as a standardized product in several online and offline contexts. (DIPF/Orig.)}}, author = {{Godau, Marc and Haenisch, Matthias}}, booktitle = {{Praxen und Diskurse aus Sicht musikpädagogischer Forschung}}, editor = {{Weidner, Verena and Rolle, Christian}}, keywords = {{Praxeologie, Informal learning, Informelles Lernen, Komponieren, Learning, Lernen, Musical Composition, Musical education, Musician, Musiker, Musikpädagogik, Pop music, Popmusik, Popular Music, Prozess, Studie}}, pages = {{51–67}}, publisher = {{Waxmann}}, title = {{{How popular musicians learn in the postdigital age. Ergebnisse einer Studie zur Soziomaterialität des Songwritings von Bands in informellen Kontexten}}}, year = {{2019}}, } @inbook{3593, author = {{Harteis, Christian and Fischer, Christoph}}, booktitle = {{Handbuch Gestaltung digitaler und vernetzter Arbeitswelten}}, editor = {{Maier, Günter W. and Engels, Gregor and Steffen, Eckhard}}, isbn = {{978-3-662-52903-4}}, keywords = {{i40, learning culture}}, pages = {{1----18}}, publisher = {{Springer}}, title = {{{Wissensmanagement unter Bedingungen von Arbeit 4.0}}}, year = {{2018}}, } @inbook{3594, author = {{Fischer, Christoph and Goller, Michael and Brinkmann, Lorraine and Harteis, Christian}}, booktitle = {{Digital Workplace Learning}}, editor = {{Ifenthaler, Dirk}}, isbn = {{978-3-319-46214-1 978-3-319-46215-8}}, keywords = {{i40, learning culture}}, pages = {{227----249}}, publisher = {{Springer}}, title = {{{Digitalisation of Work: Between Affordances and Constraints for Learning at Work}}}, year = {{2018}}, } @inproceedings{3852, abstract = {{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.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{ICML 2018 AutoML Workshop}}, keywords = {{automated machine learning, complex pipelines, hierarchical planning}}, location = {{Stockholm, Sweden}}, title = {{{ML-Plan for Unlimited-Length Machine Learning Pipelines}}}, year = {{2018}}, } @article{2331, abstract = {{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.}}, author = {{Kim, Yeongsu and Lee, Seungwoo and Dollmann, Markus and Geierhos, Michaela}}, issn = {{2207-6360}}, journal = {{International Journal of Advanced Science and Technology}}, keywords = {{Software Engineering, Natural Language Processing, Semantic Annotation, Machine Learning, Feature Engineering, Syntactic Structure}}, pages = {{123--136}}, publisher = {{SERSC Australia}}, title = {{{Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure}}}, doi = {{10.14257/ijast.2018.112.12}}, volume = {{112}}, year = {{2018}}, } @article{5586, abstract = {{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.}}, author = {{Weishäupl, Eva and Yasasin, Emrah and Schryen, Guido}}, journal = {{Computers & Security}}, keywords = {{Information Security Investments, Multiple Case Study, Organizations, Single Loop Learning, Double Loop Learning}}, pages = {{807 -- 823}}, publisher = {{Elsevier}}, title = {{{Information Security Investments: An Exploratory Multiple Case Study on Decision-Making, Evaluation and Learning}}}, volume = {{77}}, year = {{2018}}, } @article{34498, abstract = {{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. }}, author = {{Gorges, Julia and Neumann, Phillip and Wild, Elke and Stranghöner, Daniela and Lütje-Klose, Birgit}}, issn = {{1041-6080}}, journal = {{Learning and Individual Differences}}, keywords = {{BiLieF, Special educational needs, Learning disability, Academic selfconcept of ability, Reciprocal effects model, Inclusive education}}, pages = {{11--20}}, publisher = {{Elsevier BV}}, title = {{{Reciprocal effects between self-concept of ability and performance: A longitudinal study of children with learning disabilities in inclusive versus exclusive elementary education}}}, doi = {{10.1016/j.lindif.2017.11.005}}, volume = {{61}}, year = {{2018}}, } @article{48884, abstract = {{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\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash 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.}}, author = {{Kerschke, Pascal and Kotthoff, Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}}, issn = {{1063-6560}}, journal = {{Evolutionary Computation}}, keywords = {{automated algorithm selection, machine learning., performance modeling, Travelling Salesperson Problem}}, number = {{4}}, pages = {{597–620}}, title = {{{Leveraging TSP Solver Complementarity through Machine Learning}}}, doi = {{10.1162/evco_a_00215}}, volume = {{26}}, year = {{2018}}, } @article{4419, abstract = {{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.}}, author = {{Fust, Alexander Paul and Jenert, Tobias and Winkler, Christoph}}, journal = {{Entrepreneurship Research Journal}}, keywords = {{entrepreneurial learning, experiential learning, self-regulated learning}}, number = {{2}}, pages = {{1--11}}, publisher = {{de @Gruyter}}, title = {{{Experiential or Self-Regulated Learning: A Critical Reflection of Entrepreneurial Learning Processes}}}, volume = {{8}}, year = {{2018}}, } @inbook{57890, abstract = {{Within recent years, research on music learning in groups has increased. But the distinction between collaboration and cooperation is mostly unclear. This article aims to distinguish both concepts by presenting a study on popular music learning in groups (Godau, 2017) based on elements of the learning approach in Musical Futures (Green, 2008). As a result, the two concepts are seen as complementary. They form the poles of a continuum of collective learning: Collaboration characterizes the collective action toward the common goal. By contrast, cooperation occurs when group members act separately toward achieving the common goal. (DIPF/Orig.)}}, author = {{Godau, Marc}}, booktitle = {{Soziale Aspekte des Musiklernens}}, editor = {{Clausen, Bernd and Dreßler, Susanne}}, keywords = {{Kollaboration, Musik, Learning, Lernen, Musical education, Musikpädagogik, Pop music, Popmusik, Popular Music, Studie, Musikunterricht, Music lessons, Qualitative Forschung, Qualitative research, Teaching of music, Constructivism, Cooperation, Cooperative learning, Gruppe, Klassenmusizieren, Konstruktivismus, Kooperation, Kooperatives Lernen, Learning psychology, Lernpsychologie, Psychology of learning}}, pages = {{131–144}}, publisher = {{Waxmann}}, title = {{{Kollaboration und Kooperation beim Klassenmusizieren mit Populärer Musik. Musikmachen in der Schule im Spannungsfeld von Lernen mit der Gruppe und für die Gruppe}}}, year = {{2018}}, } @book{3600, abstract = {{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.}}, author = {{Goller, Michael}}, isbn = {{978-3-658-18285-4}}, keywords = {{Workplace learning, Agency, Expertise}}, pages = {{373}}, publisher = {{Springer}}, title = {{{Human agency at work: An active approach towards expertise development}}}, doi = {{10.1007/978-3-658-18286-1}}, year = {{2017}}, } @article{5671, abstract = {{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.}}, author = {{Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}}, journal = {{European Journal of Operational Research}}, keywords = {{E-Commerce, Recommender System, Attribute Weights, Configuration System, Decision Support}}, number = {{1}}, pages = {{205 -- 215}}, publisher = {{Elsevier}}, title = {{{A configuration-based recommender system for supporting e-commerce decisions}}}, volume = {{259}}, year = {{2017}}, } @article{1098, abstract = {{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.}}, author = {{Kim, Yeong-Su and Lee, Seung-Woo and Dollmann, Markus and Geierhos, Michaela}}, issn = {{2205-8494}}, journal = {{International Journal of Software Engineering for Smart Device}}, keywords = {{Natural Language Processing, Semantic Annotation, Machine Learning}}, number = {{2}}, pages = {{1--6}}, publisher = {{Global Vision School Publication}}, title = {{{Semantic Annotation of Software Requirements with Language Frame}}}, volume = {{4}}, year = {{2017}}, } @inbook{29735, abstract = {{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.}}, author = {{Paloniemi, Susanna and Goller, Michael}}, booktitle = {{Agency at work: An agentic perspective on professional learning and development}}, editor = {{Goller, Michael and Paloniemi, Susanna}}, isbn = {{9783319609423}}, issn = {{2210-5549}}, keywords = {{Agency, Workplace learning, Professional development}}, pages = {{465--478}}, publisher = {{Springer International Publishing}}, title = {{{The Multifaceted Nature of Agency and Professional Learning}}}, doi = {{10.1007/978-3-319-60943-0_23}}, year = {{2017}}, } @article{28355, abstract = {{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.}}, author = {{Müller, Mareike}}, issn = {{2215-1931}}, journal = {{Journal of Second Language Pronunciation}}, keywords = {{pronunciation learning, study abroad, qualitative approach, narrative analysis, learner beliefs, socio-psychological learning factors}}, number = {{1}}, pages = {{108--142}}, title = {{{Listening to learners’ voices: Qualitative aspects of pronunciation learning during study abroad}}}, doi = {{10.1075/jslp.2.1.05mul}}, volume = {{2}}, year = {{2016}}, } @article{48462, abstract = {{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. }}, author = {{Praetorius, Saskia and Al-Kabbani, Daniel and Bohndick, Carla and Hilkenmeier, Johanna and König, Sebastian T. and Müsche, Hannah S. and Sommer, Sabrina and Klingsieck, Katrin B.}}, journal = {{Zeitschrift für Hochschulentwicklung}}, keywords = {{E-Learning, digitale Medien, problembasiertes Lernen, Lehrerbildung, Diagnostik}}, number = {{3}}, pages = {{213--245}}, title = {{{Spielend Lehrer/in werden: problembasiertes Lernen mit virtuellen Schülerinnen/Schülern. }}}, volume = {{11}}, year = {{2016}}, } @article{17182, abstract = {{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.}}, author = {{Lyon, Caroline and Nehaniv, Chrystopher L. and Saunders, Joe and Belpaeme, Tony and Bisio, Ambra and Fischer, Kerstin and Forster, Frank and Lehmann, Hagen and Metta, Giorgio and Mohan, Vishwanathan and Morse, Anthony and Nolfi, Stefano and Nori, Francesco and Rohlfing, Katharina and Sciutti, Alessandra and Tani, Jun and Tuci, Elio and Wrede, Britta and Zeschel, Arne and Cangelosi, Angelo}}, issn = {{1729-8814}}, journal = {{International Journal of Advanced Robotic Systems}}, keywords = {{Robot Language, Human Robot Interaction, HRI, Developmental Robotics, Cognitive Bootstrapping, Statistical Learning}}, number = {{3}}, publisher = {{Intech Europe}}, title = {{{Embodied Language Learning and Cognitive Bootstrapping: Methods and Design Principles}}}, doi = {{10.5772/63462}}, volume = {{13}}, year = {{2016}}, }