IWT: an innovative solution for AGS e-learning model

Albano, Giovannina; Gaeta, Matteo; Ritrovato, Pierluigi

doi:10.1504/ijkl.2007.015552

Cited by 39 publications

(15 citation statements)

References 2 publications

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“…The Learning Needs sharing will produce two positive effects: (i) the possibility for the workers community to provide suitable learning resources and satisfy specific Learning Needs, and (ii) the opportunity for the organisation to adapt learning plans on the basis of real workers' learning needs. A first implementation of the proposed integrated approach has been provided extending the capabilities of a commercial e-Learning Platform, namely Intelligent Web Teacher (IWT) [12]. The sentence similarity computation feature has been realized using Lucene.NET [13] full-text search engine.…”

Section: Discussionmentioning

confidence: 99%

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

Capuano

Gaeta

Ritrovato

2009

2009 Ninth IEEE International Conference on Advanced Learning Technologies

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Section: Discussionmentioning

confidence: 99%

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

Capuano

Gaeta

Ritrovato

2009

2009 Ninth IEEE International Conference on Advanced Learning Technologies

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“…The affective profile will consists of one numerical array v=(v 1 , v 2 , v 3 ), as we will show in details in the following, and a matrix of the causes of the failure in mathematics individuated by the student, which will be used in order to choose suitable learning activities aimed to recover or prevent failure.…”

Section: From the Questionnaire Back To The Modelmentioning

confidence: 99%

“…Starting from what said above, the platform is able to create, manage and update in itinere a personalised Unit of Learning for each student [1].…”

mentioning

confidence: 99%

“…• the cognitive level, which involves the learning of the specific concepts and methods of the discipline, also related to the obstacles recognized by research and practice; • the meta-cognitive level, which involves learners' control of their own learning processes; • the peri-cognitive level 1 , which involves beliefs, emotions and attitudes, and all affective aspects, which are most often critical in shaping learners' decisions and performances.…”

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confidence: 99%

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E-Learning and Affective Student’s Profile in Mathematics

Albano¹,

Ascione²

2008

Int. J. Emerg. Technol. Learn.

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Abstract-This paper is concerned with the personalisation of teaching/learning paths in mathematics education. Such personalisation would exploit the research results on the connection between the affective experience of the student learning mathematics and his/her success or failure in mathematics, which produces the learner's attitude towards mathematics. We present a model for the learner's affective profile in mathematics, which would extend the current user profile in an e-learning platform taking into account the learner's attitude, to be used in order to offer and manage a Unit of Learning in mathematics better tailored on the global student' needs. Tools for the implementation of the model in an e-learning platform have been devised. Activities templates suitable to various attitudes towards mathematics have been designed and their experimentation is in progress.

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“…Similarly, "SmartContext" was an ontology based context model which included a standardized context template, a context reasoning ontology and a context middleware [30]. A flexible e-learning model would take into consideration the learner's knowledge state and learning preferences [31] to create personalized learning paths [32]. Furthermore, the users may prefer informal communication and learning [33].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware Mobile Learning

Economides

The Open Knowlege Society. A Computer Science and Information Systems Manifesto

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Recent developments on mobile devices and networks enable new opportunities for mobile learning anywhere, anytime. Furthermore, recent advances on adaptive learning establish the foundations for personalized learning adapted to the characteristics of each individual learner. A mobile learner would perform an educational activity using the infrastructure (e.g. handheld devices, networks) in an environment (e.g. outdoors). In order to provide personalization, an adaptation engine adapts the educational activity and the infrastructure according to the context. The context is described by the learner's state, the educational activity's state, the infrastructure's state, and the environment's state. Furthermore, each one of these states is described by its dimensions. Many examples illustrate the adaptation decisions.

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IWT: an innovative solution for AGS e-learning model

Cited by 39 publications

References 2 publications

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

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

E-Learning and Affective Student’s Profile in Mathematics

Context-Aware Mobile Learning

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