Integrating descriptions of knowledge management learning activities into large ontological structures: A case study

Ontologies have been recognized as a fundamental infrastructure for advanced approaches to Knowledge Management (KM) automation, and the conceptual foundations for them have been discussed in some previous reports. Nonetheless, such conceptual structures should be properly integrated into existing ontological bases, for the practical purpose of providing the required support for the development of intelligent applications. Such applications should ideally integrate KM concepts into a framework of commonsense knowledge with clear computational semantics. In this paper, such an integration work is illustrated through a concrete case study, using the large OpenCyc knowledge base. Concretely, the main elements of the Holsapple & Joshi KM ontology and some existing work on e-learning ontologies are explicitly linked to OpenCyc definitions, providing a framework for the development of functionalities that use the built-in reasoning services of OpenCyc in KM ctivities. The integration can be used as the point of departure for the engineering of KM-oriented systems that account for a shared understanding of the discipline and rely on public semantics provided by one of the largest open knowledge bases available

Workplace 'learning' and adult education: Messy objects, blurry maps and making difference

This article reviews diverse representations of learning evident among published accounts of workplace learning across fields such as adult education, human resource development, management and organisation studies. The discussion critically addresses the question of how to mediate a multiplicity of definitional, ideological and purposive orientations. The argument here is that the issue is not perspectival, but ontological. The critical problem lies in mistaking learning as a single object when in fact it is enacted as multiple objects, as very different things in different logics of study and practice. Particularly in the contested arena of work as a site of economic conflict and production, learning needs to be appreciated as a messy object, existing in different states, or perhaps a series of different objects that are patched together through some manufactured linkages

Understanding relations of individual-collective learning in work: A review of research

Abstract: A review was conducted of literature addressing learning in work, focusing on relations between individual and collective learning published in nine journals during the period 1999-2004. The journals represent three distinct fields of management/organization studies, adult education, and human resource development: all publish material about workplace learning regularly. A total of 209 articles were selected for content analysis, containing a range of material including reports of empirical research to theoretical discussion. Eight themes of individual-collective learning were identified through inductive content analysis of this literature: individual knowledge acquisition, sensemaking/reflective dialogue, levels of learning, network utility, individual human development, individuals in community, communities of practice, and a co-participation or co-emergence theme. The discussion notes apparent lack of dialogue across the fields despite similar concepts, the ontological and ideological differences among the themes of learning currently in circulation, and the low frequency of analysis of power relations in the articles reviewed

Apprentissage permanent par feedback endogène, application à un système robotique

Les applications robotiques sont liées à l'environnement sociotechnique dynamique dans lequel elles sont intégrées. Dans ce contexte, l'auto-adaptation est une préoccupation centrale et la conception d'applications intelligentes dans de tels environnements nécessite de les considérer comme des systèmes complexes. Le domaine de la robotique est très vaste. L'accent est mis sur les systèmes qui s'adaptent aux contraintes de leur environnement et non sur la mécanique ou le traitement du signal. À la lumière de ce contexte, l'objectif de cette thèse est la conception d'un mécanisme d'apprentissage capable d'apprendre de manière continue en utilisant des feedbacks endogènes (i.e. des interactions internes) dans des environnements sociotechniques dynamiques. Ce mécanisme d'apprentissage doit aussi vérifier plusieurs propriétés qui sont essentielles dans ce contexte comme : l'agnosticité, l'apprentissage tout au long de la vie, l'apprentissage en ligne, l'auto-observation, la généralisation des connaissances, le passage à l'échelle, la tolérance au volume de données et l'explicabilité. Les principales contributions consistent en la construction de l'apprentissage endogène par contextes et la conception du mécanisme d'apprentissage ELLSA pour Endogenous Lifelong Learner by Self-Adaptation. Le mécanisme d'apprentissage proposé est basé sur les systèmes multi-agents adaptatifs combinés à l'apprentissage endogène par contextes. La création de l'apprentissage endogène par contextes est motivée par la caractérisation d'imprécisions d'apprentissage qui sont détectées par des négociations locales entre agents. L'apprentissage endogène par contextes comprends aussi un mécanisme de génération de données artificielles pour améliorer les modèles d'apprentissage tout en réduisant la quantité nécessaire de données d'apprentissage. Dans un contexte d'apprentissage tout au long de la vie, ELLSA permet une mise à jour dynamique des modèles d'apprentissage. Il introduit des stratégies d'apprentissage actif et d'auto-apprentissage pour résoudre les imprécisions d'apprentissage. L'utilisation de ces stratégies dépend de la disponibilité des données d'apprentissage. Afin d'évaluer ses contributions, ce mécanisme est appliqué à l'apprentissage de fonctions mathématiques et à un problème réel dans le domaine de la robotique : le problème de la cinématique inverse. Le scénario d'application est l'apprentissage du contrôle de bras robotiques multi-articulés. Les expériences menées montrent que l'apprentissage endogène par contextes permet d'améliorer les performances d'apprentissage grâce à des mécanismes internes. Elles mettent aussi en évidence des propriétés du système selon les objectifs de la thèse : feedback endogènes, agnosticité, apprentissage tout au long de la vie, apprentissage en ligne, auto-observation, généralisation, passage à l'échelle, tolérance au volume de données et explicabilité.Robotic applications are linked to the dynamic sociotechnical environment in which they are embedded. In this scope, self-adaptation is a central concern and the design of intelligent applications in such environments requires to consider them as complex systems. The field of robotics is very broad. The focus is made on systems that adapt to the constraints of their environment and not on mechanics or signal processing. In light of this context, the objective of this thesis is the design of a learning mechanism capable of continuous learning using endogenous feedback (i.e. internal interactions) in dynamic sociotechnical environments. This learning mechanism must also verify several properties that are essential in this context such as: agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability. The main contributions consist of the construction of Endogenous Context Learning and the design of the learning mechanism ELLSA for Endogenous Lifelong Learner by Self-Adaptation. The proposed learning mechanism is based on Adaptive Multi-Agent Systems combined with Context Learning. The creation of Endogenous Context Learning is motivated by the characterization of learning inaccuracies that are detected by local negotiations between agents. Endogenous Context Learning also includes an artificial data generation mechanism to improve learning models while reducing the amount of the required learning data. In a Lifelong Learning setting, ELLSA enables dynamic updating of learning models. It introduces Active Learning and Self-Learning strategies to resolve learning inaccuracies. The use of these strategies depends on the availability of learning data. In order to evaluate its contributions, this mechanism is applied to the learning of mathematical functions and to a real problem in the field of robotics: the Inverse Kinematics problem. The application scenario is the learning of the control of multi-jointed robotic arms. The conducted experiments show that Endogenous Context Learning enables to improve the learning performances thanks to internal mechanisms. They also highlight the properties of the system according to the objectives of the thesis: endogenous feedback, agnosticity, lifelong learning, online learning, self-observation, knowledge generalization, scalability, data volume tolerance and explainability

Social support system in learning network for lifelong learners:a conceptual framework

Nadeem, D., Stoyanov, S., & Koper, R. (2009). Social support system in learning network for lifelong learners: A Conceptual framework [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning, 19(4/5/6), 337-351.Learning Networks are favorable model for supporting self-directed learning for lifelong learners. Learners can themselves decide about their learning plans to learn at their own pace irrespective of place and time. However, such learners remain hidden from others in the Learning Network., which makes their learning detrimental and less effective. Bringing learners together would benefit them in sharing each others expertise and learn effectively by collaboration. We propose to tackle the problem of finding people in learning networks by developing a Social Support System (SoSuSy) prototype. This position paper presents a conceptual framework for designing SoSuSy in a Learning Network. Such a system connects the learner with other learners who are dealing with similar problem by using their combined skills and to increase their social interaction. We propose by using people’s profile on social network and the public text content they create (blogs and book-marking) supported by web 2.0 applications, to enhance the search for finding suitable people who match in their interests, competence and tasks. We present an informal learning scenario to justify the need for such a system in online distributed Learning Network.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

A Design Model for Lifelong Learning Networks

The provision of lifelong learning facilities is considered to be a major new direction for higher and distance teaching educational institutes catering for the demands of industry and society. ICT networks will in future support seamless, ubiquitous access to lifelong learning facilities at home, at work, in schools and universities. This implies the development of new ways of organizing learning delivery that goes beyond course and programme-centric models. It envisions a learner-centred, learner-controlled model of distributed lifelong learning. We present a conceptual model for the support of lifelong learning which is based on notions from self-organization theory, learning communities, agent technologies and learning technology specifications such as IMS Learning Design. An exploratory implementation has been developed and used in practice. We reflect on the findings and future directions