Tracking e-learning through published papers: A systematic review

Rodrigues, Helena; Almeida, Filomena; Figueiredo, Vanessa; Lopes, Sara L.

doi:10.1016/j.compedu.2019.03.007

Cited by 308 publications

(270 citation statements)

References 102 publications

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“…Adaptivity is a critical component of modern e-learning systems (Rodrigues et al, 2019). Adaptivity facilitates student learning by recommending suitable learning strategies, offering relevant instruc-tional materials, and guiding navigation through the material (Brusilovsky, 1996).…”

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

“…There have been many attempts to build and evaluate AESs. However, there is a shortage of studies that take into account detailed, carefully designed, and controlled experimental evaluations that assess learning effectiveness (Akbulut & Cardak, 2012;Normadhi et al, 2019;Özyurt & Özyurt, 2015;Rodrigues et al, 2019;Truong, 2016;Xie et al, 2019). Further, while there are a number of research studies based on learning style, these studies typically are short-term in nature, focus on a specific model or theory, and utilize small samples of subjects (Akbulut & Cardak, 2012;E.…”

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

“…A driving force behind the development of modern e-learning systems is the provision of appropriate learning content mediation to enhance the effectiveness of the learning process. Current approaches to the evolution of e-learning systems are designed to overcome previous limitations of traditional e-learning systems, avoid information overload, aid students in selecting learning material, and maintain student interest (Rodrigues, Almeida, Figueiredo, & Lopes, 2019). A primary issue realized in the development of adaptive e-learning systems is the fulfillment of student requirements while delivering adaptive learning experiences and appropriate learning resources (Truong, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive learning has emerged as a fundamental concept and paradigm for modern e-learning systems (Rodrigues et al, 2019), and is rising in prevalence in educational technology research (Xie, Chu, Hwang, & Wang, 2019). However, the implementation of adaptation is not always apparent in elearning systems, especially adaptation that takes into consideration both learning style and knowledge level (Brusilovsky & Millán, 2007;Truong, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its value in learning enhancement. This study aims to bridge that gap by examining the effect of different modes of learning material adaptation and their sequences to the learning style and knowledge level of students in e-learning systems. Background: E-learning systems aim to provide acceptability and interactivity between students, instructors, and learning content anytime and anywhere. However, traditional systems are typically designed for generic students irrespective of individual requirements. Successful e-learning systems usually consider student characteristics such as learning style and knowledge level to provide more personalized and adaptive student-system interaction. Methodology: A controlled experiment was conducted in a learning context with 174 subjects to evaluate the learning effectiveness of adaptivity in e-learning systems. Contribution: The main contributions of the paper are threefold. First, a novel adaptive approach is proposed based on a specific learning style model and knowledge level. Second, the approach is implemented in an e-learning system to teach computer security, the application domain. Third, a rigorous experimental evaluation of the learning effect of the adaptive approach is offered. Findings: The results indicate that adaptation according to the combination of learning style and knowledge level produces significantly better learning gains, both in the short-term and medium-term, than adaptation according to either trait individually. Recommendations for Practitioners: Practitioners should consider the combination of learning style and knowledge level when delivering and presenting learning material to their students. Recommendation for Researchers: Researchers should consider sound educational models when designing adaptive e-learning systems. Also, rigorous and carful experimental design evaluations should be taken into account. Impact on Society: Universities and e-learning industries can benefit from the proposed adaptive approach and the findings in designing and developing more personalized and adaptive e-learning systems. The incorporation of student characteristics, especially learning style and knowledge level, may be used to enhance learning. Future Research: The experiment might be duplicated with a focus on longer-term learning gains by including more subjects and more learning resources. Also, the study might be expanded to application domains other than computer security. Moreover, other variables such as student satisfaction, motivation, and affective state might be explored to further the understanding of the effect of adaptivity on learning gains.

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Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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“…*Effective Virtual Learning Environments (VLEs) are designed to offer suitable and relevant learning resources to support the learning process (Gunathilaka et al, 2018). Traditional VLEs may not always be appropriate as they overlook the diverse requirements of students and are designed generally to support pre-defined learning activities (Rodrigues et al, 2019). The main challenge in designing VLEs is to find out the appropriate approach needed to meet the needs, motivation, and preferences of students, and to deliver a more personalized learning experience (Truong, 2016).…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of an adaptive framework for virtual learning environments

Alshammari¹

2020

Int. j. adv. appl. sci.

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Traditional virtual learning environments may not always be suitable as they overlook the diverse requirements of students and are designed generally to support certain learning activities. Adaptivity is often proposed as a promising solution to overcome that limitation. However, it is still challenging to find the proper way to design such systems in order to adapt learning material in accordance with the students' characteristics. This paper, therefore, provides an adaptive framework to design different instances of adaptive virtual learning environments. An implementation based on the proposed framework resulting in an adaptive virtual learning environment is also presented. The adaptive environment incorporates learning style and student performance. These two student characteristics are used to produce personalized learning paths as the main adaptive feature. An illustrative example is also offered to highlight how the framework can be used and implemented. The paper also presents an evaluation of the developed adaptive virtual learning environment in terms of perceived usefulness and learning engagement. A controlled experiment was managed with seventy-five participants in a learning environment. The results indicate that the adaptive virtual learning environment can be better to support students in terms of their perception and better in engaging them in the learning process than when they interact with a non-adaptive version. The framework can be valuable as a foundation in designing such systems and in enhancing future adaptive online-learning research. Future directions of research are also highlighted.

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Prioritizing the Critical Success Factors of E‐Learning Systems by Using DEMATEL

Mehta¹,

Sharma²

2023

Redefining Virtual Teaching Learning Pedagogy

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Tracking e-learning through published papers: A systematic review

Cited by 308 publications

References 102 publications

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Design and evaluation of an adaptive framework for virtual learning environments

Prioritizing the Critical Success Factors of E‐Learning Systems by Using DEMATEL

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