Semantic Composition of Lecture Subparts for a Personalized e-Learning

Karam, Naouel; Linckels, Serge; Meinel, Christoph

doi:10.1007/978-3-540-72667-8_50

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…For example, Karam, et al (2007) assemble learning objects by inferring the best "composition flow" using the current user knowledge state and the domain ontology. Description logic is used to solve the "concept-covering problem", which corresponds to the knowledge need of the current user.…”

Section: Metadata-based Techniquesmentioning

confidence: 99%

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

Steichen

Ashman

Wade

2012

Information Processing & Management

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Abstract.A key driver for next generation web information retrieval systems is becoming the degree to which a user's search and presentation experience is adapted to individual user properties and contexts of use. Over the past decades, two parallel threads of personalisation research have emerged, one originating in the document space in the area of Personalised Information Retrieval (PIR) and the other arising from the hypertext space in the field of Adaptive Hypermedia (AH).PIR typically aims to bias search results towards more personally relevant information by modifying traditional document ranking algorithms. Such techniques tend to represent users with simplified personas (often based on historic interests), enabling the efficient calculation of personalised ranked lists. On the other hand, the field of Adaptive Hypermedia (AH) has addressed the challenge of biasing content retrieval and presentation by adapting towards multiple characteristics. These characteristics, more typically called personalisation "dimensions", include user goals or prior knowledge, enabling adaptive and personalised result compositions and navigations.The question arises as to whether it is possible to provide a comparison of PIR and AH, where the respective strengths and limitations can be exposed, but also where potential complementary affordances can be identified. This survey investigates the key techniques and impacts in the use of PIR and AH technology in order to identify such affordances and limitations. In particular, the techniques are analysed by examining key activities in the retrieval process, namely (i) query adaptation, (ii) adaptive retrieval and (iii) adaptive result composition and presentation. In each of these areas, the survey identifies individual strengths and limitations. Following this comparison of techniques, the paper also illustrates an example of a potential synergy in a hybridised approach, where adaptation can be tailored in different aspects of PIR and AH systems. Moreover, the concerns resulting from interdependencies and the respective tradeoffs of techniques are discussed, along with potential future directions and remaining challenges.

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Section: Metadata-based Techniquesmentioning

confidence: 99%

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

Steichen

Ashman

Wade

2012

Information Processing & Management

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“…The semantic search engine that we used is described in detail in [11]. It infers over the OWL-DL metadata and computes how much the description matches the query.…”

Section: Search Enginesmentioning

confidence: 99%

Towards to an automatic semantic annotation for multimedia learning objects

Repp

Linckels

Meinel

2007

Proceedings of the International Workshop on Educational Multimedia and Multimedia Education

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The number of digital video recordings has increased dramatically. The idea of recording lectures, speeches, and other academic events is not new. But, the accessibility and traceability of its content for further use is rather limited. Searching multimedia data, in particular audiovisual data, is still a challenging task to overcome. We describe and evaluate a new approach to generate a semantic annotation for multimedia resources, i.e., recorded university lectures. Speech recognition is applied to create a tentative and deficient transliteration of the video recordings. We show that the imperfect transliteration is sufficient to generate semantic metadata serialized in an OWL file. The semantic annotation process based on textual material and deficient transliterations of lecture recordings are discussed and evaluated.

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“…Nevertheless, relying on standard services and on RDQL, such systems cannot deal with approximation nor provide explanation services. An alternative proposal of integration may be found in [70], in which the composition of learning objects is based on the solution of a Concept Covering problem defined in terms of LCS computation. Such an approach, although dealing with approximation, does not provide explanation facilities.…”

Section: Related Workmentioning

confidence: 99%

Automating competence management through non-standard reasoning

Colucci

Tinelli

Sciascio

et al. 2011

Engineering Applications of Artificial Intelligence

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Competence Management calls for automated knowledge-based services in order to take full advantage from the know-how of a company. This paper presents an integrated semantic-based knowledge management system providing decision support services for several activities typical of competence management, including Core Competence evaluation, human resources allocation, training programs planning. Knowledge resources are represented according to the formalism of Description Logics, which also allows for inference services crucial for the implemented solutions. We present the various features of our approach, which exploit advanced non-standard reasoning services from Description Logics, specifically developed to support knowledge management.

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Semantic Composition of Lecture Subparts for a Personalized e-Learning

Cited by 8 publications

References 8 publications

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

Towards to an automatic semantic annotation for multimedia learning objects

Automating competence management through non-standard reasoning

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