Providing Smart Objects with Intelligent Tutoring Capabilities by Semantic Technologies

Fenza, Giuseppe; Loia, Vincenzo; Orciuoli, Francesco

doi:10.1109/incos.2016.110

Cited by 5 publications

(2 citation statements)

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“…Because of the amount of data that every day circulate in Twitter, the problem of analyzing the posts has been associated, by data mining communities, to a problem of stream processing. 34 In fact, large volumes of data provided by social networks and blogs can be seen as sensors about peoples needs, preferences, inclinations, and so on. Such aspects, together with temporal one, have inspired several data stream algorithms.…”

Section: Related Workmentioning

confidence: 99%

Social media marketing through time‐aware collaborative filtering

Maio

Fenza

Gallo

et al. 2017

Concurrency and Computation

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SummarySocial media is assuming a crucial role in purchasing decisions, and most companies are using social media for marketing. Making sense of the unstructured information content shared by users along the time is an emerging challenge. We advise that the dynamic nature with which trends and user's interests evolve along the timeline requires the revising of well-assessed methods to address, for instance, recommendation provisioning, information retrieval, and so on. ie, Twitter. 6 In particular, we advise that time awareness is crucial to more effectively estimate user's interests, and thus, to better address social media marketing improving "vanity metrics" of likes and shares that in social marketing remain the most commonly used measures for evaluating success on social media attempting to increase traction, for instance, posting the right message at the right time.Problem. Let us consider a timestamped tweet stream = {tw 1 , … , tw n } in the time window starting at time t s and ending at time t e , and let us suppose topics corresponding to the content of each

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Section: Related Workmentioning

confidence: 99%

Social media marketing through time‐aware collaborative filtering

Maio

Fenza

Gallo

et al. 2017

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

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“…Such approaches can be roughly divided into two macro-classes. The first class includes techniques that exploit formal languages to represent knowledge (e.g., ontology languages like Web Ontology Language-OWL); for instances, [16]- [18] and [19]. The second class is represented by techniques based on machine learning and data mining (e.g., artificial neural networks, reinforcement learning, rule mining); for instances, [20]- [24] and [25].…”

Section: Introductionmentioning

confidence: 99%

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

et al. 2020

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Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL). INDEX TERMS Classification rule mining, intelligent tutoring systems, ontologies, pedagogical rules, semantic web, web ontology language (OWL).

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The design and development of an intelligent tutoring system as a part of the architecture of internet of things (IoT)

Mokmin

Masood

2017

Proceedings of the 2017 International Conference on Telecommunications and Communication Engineering

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Providing Smart Objects with Intelligent Tutoring Capabilities by Semantic Technologies

Cited by 5 publications

References 12 publications

Social media marketing through time‐aware collaborative filtering

Social media marketing through time‐aware collaborative filtering

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

The design and development of an intelligent tutoring system as a part of the architecture of internet of things (IoT)

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