On-demand Construction of Personalized Learning Experiences Using Semantic Web and Web 2.0 Techniques

Capuano, Nicola; Gaeta, Matteo; Ritrovato, Pierluigi

doi:10.1109/icalt.2009.53

Cited by 22 publications

(12 citation statements)

References 8 publications

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“…To simplify user interactions with the system, IWT also implements an alternative method for the expression of a learning need through Upper Level Learning Goals (ULLGs) [16]. An ULLG is a meaningful set of target concepts on a given domain model with a connected textual description.…”

Section: The Starting Pointmentioning

confidence: 99%

Management of Latent Learning Needs in Adaptive e-Learning Systems

Capuano

Salerno

Mangione

et al. 2013

2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems

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The construction of personalized courses taking into account needs and preferences of each learner is one of the most studied topics within the field of adaptive learning systems. Such systems assume that learning needs are well know by the teacher or, in self directed learning settings, that learners can easily determine and express own needs. Nevertheless, in real situations, learning needs often remain latent and, for this reason, unsatisfied. To overcome this limitation we propose in this paper a methodology and a prototype system enabling the elicitation of latent learning needs, as well as the automatic generation of learning experiences capable of satisfying such needs. The proposed methodology applies principles of recommending systems and also relies on a semantic representation of topics to be taught. The encouraging experimental results obtained with University students are also discussed.

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Section: The Starting Pointmentioning

confidence: 99%

Management of Latent Learning Needs in Adaptive e-Learning Systems

Capuano

Salerno

Mangione

et al. 2013

2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems

Self Cite

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“…The presentation of all possible cases describe the full set of the emotional state if we use the emotive parameters (see parameters in figure 1) and the full set of affective state if we use the parameters describing the affectivity (see figure 2). More details about the questionnaire and the results coming from the assessments by using the e-learning platform IWT [55] [56] are presented in [57].…”

Section: Step Level 3: Estimation Of Dominancementioning

confidence: 99%

A Computational Model for Managing Emotions and Affections in Emotional Learning Platforms and Learning Experience in Emotional Computing Context

Iovane

Salerno

Giordano

et al. 2012

2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems

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In this paper we propose a model to represent and manage affective / emotional feedback. This model represents a set of emotions which is assumed to play a major role for learning processes. The emotions are to be identified during the learning phase. To achieve these objectives, we have considered primarily: i) Methods and techniques to assess and identify affectivity/ emotivity in ITS (Intelligent Tutoring System), ii) Feedback management derived by the detection of user's affective states in ITS. Our objective is to establish a methodology through the steps that lead us to 'identification and quantification of the emotional state of a learner. © 2012 Crown Copyright

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“…Following the larger-scale publication of STASIS [82], there has been a flurry of work in the STSS field. The majority of subsequent work in the field is either derivative from or influenced by STASIS [84][85][86][87][88][89][90][91][92][93][94]. Accordingly, the following description of STASIS should prove useful to Systems Engineers wishing to develop STSS algorithms.…”

Section: The Stasis Algorithmmentioning

confidence: 99%

Systems Engineering and Conversational Agents

O’Shea

Bandar

Crockett

2011

Intelligent Systems Reference Library

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Abstract. This chapter describes Conversational Agents (CAs) in the context of Systems Engineering. A CA is a computer program which interacts with a user through natural language dialogue and provides some form of service. CA technology has two points of interest to systems engineers: the use of systems engineering techniques in CA research and the application of CAs in projects development. CAs offer the opportunity to automate more complex applications than are feasible with conventional web interfaces. Currently such applications require a human expert in the domain to mediate between the user and the application. The CA effectively replaces the human expert. This chapter reviews the current capabilities of various CA technologies, outlines a development methodology for systems engineering practitioners interested in developing real world applications and suggests a number of directions for systems engineers who wish to participate in CA research.Keywords: Conversational agent, systems engineering, dialogue, evaluation, methodology, semantic similarity, short text Section 1 IntroductionIn the early decades of computing ordinary people did not have any interaction with computers at all, any information that was needed for an application was entered by specialised clerks. This was followed by a period (largely driven by the internet) in which users could drive simple applications by entering simple facts and figures directly. Recently an explosion of internet use has demanded that ordinary people interact with complex applications, and the trend is for these to be of increasing complexity. Such applications would normally require a domain expert to interview the user to obtain the necessary information. Automating such processes represents a serious challenge to computing practitioners. This chapter describes a possible way forward, Conversational Agents (CAs). CA technology has two points of interest to systems engineers: the use of systems engineering techniques in CA research and the application of CAs in projects development. A CA is a computer program which interacts with a user through natural language dialogue and provides some form of service.

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On-demand Construction of Personalized Learning Experiences Using Semantic Web and Web 2.0 Techniques

Cited by 22 publications

References 8 publications

Management of Latent Learning Needs in Adaptive e-Learning Systems

Management of Latent Learning Needs in Adaptive e-Learning Systems

A Computational Model for Managing Emotions and Affections in Emotional Learning Platforms and Learning Experience in Emotional Computing Context

Systems Engineering and Conversational Agents

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