Evaluation of APeLS – An Adaptive eLearning Service Based on the Multi-model, Metadata-Driven Approach

Conlan, Owen; Wade, Vincent

doi:10.1007/978-3-540-27780-4_35

Cited by 51 publications

(42 citation statements)

References 4 publications

(2 reference statements)

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“…A drawback of the system is the fact that the particular retrieval strategy is encoded into the system rather than separated from the adaptive engine. In Conlan, et al (2002) and Conlan and Wade (2004), a multi-modal, metadata-driven approach is proposed in order to provide this separation of concerns. Most importantly, the approach introduces an additional model called the narrative model, which encodes a set of generic strategies for presenting concepts.…”

Section: Metadata-based Approachesmentioning

confidence: 99%

“…The implemented APeLS system executes the narrative by consolidating models in an Adaptive Engine (see Figure 5). (Conlan, et al, 2002) and Conlan and Wade (2004) In terms of retrieval adaptation, the narrative contains the rules for which concepts should be selected, how they should be sequenced and which candidate content group should be considered for content retrieval. All adaptation hence occurs on the concept-level, enabling the narrative to be independent from the actual content.…”

Section: Metadata-based Approachesmentioning

confidence: 99%

“…The generation of personalised learning sequences is taken further in the APeLS system (Conlan et al, 2002) (Conlan and Wade, 2004), where the separation between the core adaptive engine and the sequencing service is proposed in the form of a narrative model. This narrative reflects a didactical ordering that can be specifically adapted to the current user task, context and preferences.…”

Section: Metadata-based Techniquesmentioning

confidence: 99%

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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 Approachesmentioning

confidence: 99%

Section: Metadata-based Approachesmentioning

confidence: 99%

Section: Metadata-based Techniquesmentioning

confidence: 99%

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A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

Steichen

Ashman

Wade

2012

Information Processing & Management

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“…The motivation behind personalising content was the need to provide a learning environment to suit each person's learning capabilities. Adaptive eLearning systems are currently being designed and several existing elearning tools are being incorporated with this feature [4,13,5]. But most of them use predefined or adaptive tests [14] to decide the learning path, making them static adaptation models.…”

Section: Application Of the Proposed Research Workmentioning

confidence: 99%

CAFSM: A Communicating Adaptive Finite State Machine for Personalised Multimedia Streaming

Elias¹,

Jasmin²,

Mathew³

et al. 2010

IJCA

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In this paper we present the design of a multimedia presentation system which permits the dynamic adaptation of the content. The Communicating Adaptive Finite State Machine (CAFSM) presented in this paper, has been used to describe the multimedia streaming and presentation system proposed here. This system is driven by a set of messages that are used for communication and co-ordination among the various component machines which form the system

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“…The user-model is used to represent attributes such as Prior Knowledge, User Preferences, Cognitive and Learning Style, Aims and Goals etc. Successful second generation AHS include APeLS [6], KnowledgeTree [7], various prototype systems based on the AHA! architecture and KBS-Hyperbook [8].…”

Section: State Of the Artmentioning

confidence: 99%

Dynamic Content Discovery, Harvesting and Delivery, from Open Corpus Sources, for Adaptive Systems

Lawless

Wade

2006

Lecture Notes in Computer Science

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Abstract. Personalised elearning is being heralded as one of the grand challenges of next generation learning systems, in particular, its ability to support greater effectiveness, efficiency and student empowerment. However, a key problem with such systems is their reliance on bespoke content developed for, and only used by, these systems. The challenge for adaptive systems in scalably supporting personalised elearning is its ability to source, harvest and deliver open corpus content to adaptive content services and personalised elearning systems. This paper examines the issues involved in implementing such an adaptive content service. The paper seeks to explore the accurate extraction of content requirements from the adaptive system, the sourcing and identification of suitable learning content, the harvesting and customisation of the content for delivery to adaptive elearning systems. MotivationeLearning environments are attempting to respond to the demand for personalised ondemand distance/on-line learning by providing increasing support for such functionality as personalisation, adaptivity and on-demand learning object generation [1]. Adaptive Hypermedia is seen as one of the key areas of delivering personalised "just-for-you" eLearning. The benefit of such learning is that it can be dynamically tailored to the individual's experience, goals, preferences etc. This empowers the learner as the learning experience and activities are more suited to that individual.One of the most significant problems with personalised eLearning systems (PeLS) is that they are traditionally restricted to using bespoke proprietary content. This is particularly the case with Intelligent Tutoring Systems (ITS) where, in fact, the personalisation is embedded in the content itself [2]. However in the second generation of adaptive systems the sequencing for adaptivity has been separated from the physical content. This provides the opportunity whereby content can be selected, to create a learning offering, in a sequence that suits each individual learner. This would allow the developers of online learning offerings to concentrate on the pedagogical design of such offerings, rather than on content development. However, in such systems (Knowledge Tree, Aha!, APeLS) the content is, in most cases, still sourced from a private repository of learning resources. This paper discusses ongoing 446 S. Lawless and V. Wade research into the provision of dynamic adaptivity using open corpus content rather than content uniquely created for each system involved.The paper begins with a brief introduction into the state of the art in Adaptive Hypermedia Systems (AHS), how they currently source and utilize learning content, and how this has evolved over the generations of AHS. The paper proceeds by identifying the key issues and challenges in the discovery, harvesting, and delivery of open corpus content for AHS. The paper concludes by presenting a proposed system architecture of an open corpus content service, in its initial stages. The areas where, and m...

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Evaluation of APeLS – An Adaptive eLearning Service Based on the Multi-model, Metadata-Driven Approach

Cited by 51 publications

References 4 publications

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

A comparative survey of Personalised Information Retrieval and Adaptive Hypermedia techniques

CAFSM: A Communicating Adaptive Finite State Machine for Personalised Multimedia Streaming

Dynamic Content Discovery, Harvesting and Delivery, from Open Corpus Sources, for Adaptive Systems

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