Reconfiguration of the learning style in the computer learning platform based on cloud paradigm

Yanmelly, Delgado; Hernández, Domingo; Aguilar, José; Altamiranda, Junior

doi:10.1109/sccc.2016.7836041

Cited by 2 publications

(3 citation statements)

References 3 publications

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Section: Behaviour Of Our Recommendation Systemmentioning

confidence: 99%

“…The extraction of the learning styles of the students who are going to take the programming logic course can be from a quiz defined by the Felder-Silverman model, or using information about its activities in the VLE or social networks. Previous works have proposed approaches to that based on these ideas [11,10,35]. Also, its information in the academic system is used, in order to have an accurate information about it.Contextual information extraction (phase 3): This layer extracts the information about the educational context, in order to fill-out the general context metadata model, which is composed of three parts: internal context, external context and Edge Context.Sending descriptors: The descriptors are sent to the modeling layer, where they are stored in the knowledge database.…”

Section: Analysis Of the Behaviour Of The Arsmentioning

confidence: 99%

“…A LMS is a web-based software to manage academic courses, where tutors manage the teachinglearning process through an integration of students, digital resources and contents, according to an instructional design [6]. The recommendation systems can improve the distribution of academic material to the students, through content adapted to their needs according to their learning styles [10,11]. Recommendation systems in the educational domain are not directly transferable to conventional systems, specifically due to the student's cognitive state and learning contents, because they are very specific to this domain [12].…”

Section: Introductionmentioning

confidence: 99%

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Autonomous recommender system architecture for virtual learning environments

Monsalve-Pulido

Aguilar

Montoya

et al. 2020

ACI

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This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

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Section: Behaviour Of Our Recommendation Systemmentioning

confidence: 99%