Competence-based Knowledge Structures for Personalised Learning: Distributed Resources and Virtual Experiments

Heller, Jürgen; Mayer, Birgit; Hockemeyer, Cord; Albert, Dietrich

doi:10.14236/ewic/el2005.2

Cited by 34 publications

(48 citation statements)

References 8 publications

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“…Moreover, the graph ) , (  K is a Hasse digram for K . The important assumption for future discussion is that due to cognitive nature of problem and mutual relation between them, not all potential knowledge states have to be observed [90]. :…”

Section: Definition: Organization Membersmentioning

confidence: 99%

Model of Multilayer Knowledge Diffusion for Competence Development in an Organization

Różewski

Jankowski

2015

Mathematical Problems in Engineering

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Growing role of intellectual capital within organizations is affecting new strategies related to knowledge management and competence development. Among different aspects related to this field, knowledge diffusion has become one of interesting areas from both practitioner and researcher's perspective. Several models were proposed with main goal to simulate diffusion and to explain the nature of these processes. Existing models are focused on knowledge diffusion and they assume diffusion within a single layer using knowledge representation. From the organizational perspective connecting several types of knowledge and modelling changes of competence can bring additional value. In the article we extended existing approaches by using multilayer diffusion model and focused on analysis of competence development process. The proposed model describes competence development process in a new way through horizontal and vertical knowledge diffusion in multilayer network. In the network, agents collaborate and interchange various kind of knowledge through different layers and this mutual activities affect the competences in a positive or negative way. Taking under consideration worker's cognitive and social abilities and the previous level of competence the ARTICLE in MATHEMATICAL PROBLEMS IN ENGINEERING · NOVEMBER 2014 2 new competence level can be estimated. The model is developed to support competence management in different organizations.

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Section: Definition: Organization Membersmentioning

confidence: 99%

Model of Multilayer Knowledge Diffusion for Competence Development in an Organization

Różewski

Jankowski

2015

Mathematical Problems in Engineering

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“…Personalized e-learning environment is becoming more demanding in today's academic world [1]- [3]. According to Heller and his colleagues [4], the aim of personalized learning is to "tailor teaching to individual needs, interests, and aptitude." This form of online learning has the potential to serve the learners by providing a learning-teaching process according to the learner's needs as a medium of adaptation techniques to ensure the most effective knowledge transfer for each learner [5].…”

Section: Introductionmentioning

confidence: 99%

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

Al-Abri¹,

Jamoussi²,

Al-Khanjari³

et al. 2019

IJACSA

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With the emergence of Web 2.0 technologies, interaction and collaboration support in the educational field have been augmented. These types of support embrace researchers to enrich the e-learning environment with personalized characteristics with the utilization of the collaboration support outputs. Achieving this requires understanding the existing environments and highlights their eminence. As a result, there are many attempts to state the current status of personalized e-learning environment from different perspectives. However, these attempts targeted a specific view and direction which failed to provide us with the general view of the adoption of personalized e-learning environment with the support of social collaboration tools. This paper provides a classified view of the current status of personalized e-learning environments which incorporate social collaboration tools for providing the personalization feature. The classification adopts four different views to carry out the classification; these views are subject, purpose, method, and tool. The findings show that the utilization of the user-generated contents and social interaction functionalities for personalization is tight and not fully consumed. In short, the potential of providing personalized learning with social interaction and collaboration features remains not fully explored.

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“…A number of theoretical extensions have been worked out by various authors (Doignon, ; Düntsch & Gediga, ; Falmagne et al ., ; Gediga & Düntsch, ; Heller, Stefanutti, Anselmi, & Robusto, ; Heller, Ünlü, et al ., ; Korossy, , , ) to incorporate the cognitive level of the skills into the theory. The basic KST equipped with these extensions is known as competence‐based knowledge space theory CbKST; (CbKST; Heller, Steiner, Hockemeyer, & Albert, ; Heller, Augustin et al ., ; Heller, Ünlü, et al ., ; Stefanutti & de Chiusole, ) and is divided into two parallel and interdependent levels: the performance level and the competence level . The knowledge domain Q and the knowledge state K ⊆ Q of an individual are located at the performance level.…”

Section: Introductionmentioning

confidence: 99%

On the assessment of procedural knowledge: From problem spaces to knowledge spaces

Stefanutti

2018

Brit J Math & Statis

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By generalizing and completing the work initiated by Stefanutti and Albert (2003, Journal of Universal Computer Science, 9, 1455), this article provides the mathematical foundations of a theoretical approach whose primary goal is to construct a bridge between problem solving, as initially conceived by Newell and Simon (1972, Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.), and knowledge assessment (Doignon and Falmagne, 1985, International Journal of Man-Machine Studies, 23, 175; Doignon and Falmagne, 1999, Knowledge spaces. Berlin, Germany: Springer-Verlag.; Falmagne et al., 2013, Knowledge spaces: Applications in education. New York, NY: Springer-Verlag; Falmagne and Doignon, 2011, Learning spaces: Interdisciplinary applied mathematics. Berlin, Germany: Springer-Verlag.). It is shown that the collection of all possible knowledge states for a given problem space is a learning space. An algorithm for deriving a learning space from a problem space is illustrated. As an example, the algorithm is used to derive the learning space of a neuropsychological test whose problem space is well known: the Tower of London (TOL; Shallice, 1982, Philosophical Transactions of the Royal Society of London B: Biological Sciences, 298, 199). The derived learning space could then be used for adaptively assessing individual planning skills with the TOL.

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Competence-based Knowledge Structures for Personalised Learning: Distributed Resources and Virtual Experiments

Cited by 34 publications

References 8 publications

Model of Multilayer Knowledge Diffusion for Competence Development in an Organization

Model of Multilayer Knowledge Diffusion for Competence Development in an Organization

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

On the assessment of procedural knowledge: From problem spaces to knowledge spaces

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