Adaptation in current e-learning systems

Ruiz, María del Puerto Paule; Díaz, Ma Jesús Fernández; Soler, Francisco Ortín; Pérez, José Manuel Errasti

doi:10.1016/j.csi.2007.07.006

Cited by 57 publications

(24 citation statements)

References 7 publications

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“…The first version of SCORM was published in 2000, and since that time it has become widely used for the development, packaging, and delivery of digital learning materials and courses (Ruiz, et al, 2008, Aroyo, Pokraev & Brussee, 2003.…”

Section: Standardizationmentioning

confidence: 99%

“…In an adaptive system, in order to discover and publish the most appropriate content for different learners (bearing in mind that the content must be compatible with the students' characteristics), metadata that defines each learning object and that describes each learner's aspects are needed. Using metadata, the body of learning content, the learners' profiles, and different learning contexts can be easily integrated into many adaptive systems (Ruiz, et al, 2008;Tseng, et al, 2008;Cristea, 2004).…”

Section: Standardizationmentioning

confidence: 99%

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The new trends in adaptive educational hypermedia systems

Somyürek

2015

IRRODL

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This paper aims to give a general review of existing literature on adaptive educational hypermedia systems and to reveal technological trends and approaches within these studies. Fifty-six studies conducted between 2002 and 2012 were examined, to identify prominent themes and approaches.According to the content analysis, the new technological trends and approaches were grouped into seven categories: standardization, semantic web, modular frameworks, data mining, machine learning techniques, social web, and device adaptation. Furthermore, four challenges are suggested as explanation why adaptive systems are still not used on a large scale: inter-operability, open corpus knowledge, usage across a variety of delivery devices, and the design of metaadaptive systems.

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Section: Standardizationmentioning

confidence: 99%

Section: Standardizationmentioning

confidence: 99%

The new trends in adaptive educational hypermedia systems

Somyürek

2015

IRRODL

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“…To this end, adaptive educational hypermedia (AEH) include the variables and a user model to adapt system navigation and content, goals, knowledge level, background, interests, preferences, stereotypes and cognitive preferences [12]. The user model can contain behavioural, cognitive and affective data [13].…”

Section: Adaptive Educational Systemsmentioning

confidence: 99%

Adaptation in E-Learning Content Specifications with Dynamic Sharable Objects

Gutiérrez

Álvarez

Paule

et al. 2016

Systems

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Dynamic sophisticated real-time adaptation is not possible with current e-learning technologies. Our proposal is based on changing the approach for the development of e-learning systems using dynamic languages and including them in both platforms and learning content specifications thereby making them adaptive. We propose a Sharable Auto-Adaptive Learning Object (SALO), defined as an object that includes learning content and describes its own behaviour supported by dynamic languages. We describe an example implementation of SALO for the delivery and assessment of a web development course using Moodle rubrics. As a result, the learning objects can dynamically adapt their characteristics and behaviour in e-learning platforms.

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“…Several studies have been carried out in the field of elearning education to improve the quality of services offered to learners and ensure the development of their skills and thus, their success. This work emphasizes individualized training paths (Idris, Yusof, & Saad, 2009;Vazquez, Ramirez, Gonzalez-Abril, & Morente, 2011), pedagogical content adaptation (Ruiz, Diaz, Soler, & Pérez, 2008;Wong & Looi, 2009), and learner guidance (Dahbi, El Kamoun, Aqqal, & El Hannani, 2009;Kamsa, Elouahbi, El Khoukhi, Karite, & Zouiten, 2016). Yet despite all of these valuable aspects, we may note the high failure and abandonment rates in elearning, regardless of the quality of the services provided (Linard, 2000).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Agents for Dynamic Optimization of Learner Performances in an Online System

2017

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Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners’ failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these vulnerabilities due to the lack of direct contact with the teacher, who would be able to point out the learner’s difficulties and help to rectify them. Methodology: The research sample was 49 learners enrolled in an online course. We focused on three cognitive factors: language, memory, and reasoning. We propose an approach to optimize learners’ performances based on two intelligent agents that model the role of a teacher: the “detector agent” and the “rectifier agent”. Contribution: The intelligent agents beneficially contribute to e-learning enrichment and the development of cognitive skills and solidification of knowledge acquisition. This is achieved by strengthening the memory, the assimilation of lessons by improving language skills, and the reinforcement of problem solving by developing reasoning and analysis capacity. Findings: The results show that the proposed approach efficiently detects the weaknesses of learners and resolves them intelligently. Future Research: The approach toward e-learning performance can be improved by focusing on other factors and intelligent agents that can improve the yield for learners and more effectively optimize system operation for their perceived needs.

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Adaptation in current e-learning systems

Cited by 57 publications

References 7 publications

The new trends in adaptive educational hypermedia systems

The new trends in adaptive educational hypermedia systems

Adaptation in E-Learning Content Specifications with Dynamic Sharable Objects

Intelligent Agents for Dynamic Optimization of Learner Performances in an Online System

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