An Adaptive E-Learning Platform with VARK Learning Styles to Support the Learning of Object Orientation

Diaz, Felipe; Rubilar, Tania Pizarro; Figueroa, Claudio Cubillos; Mellado-Silva, Rafael

doi:10.1109/edunine.2018.8450990

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Numerous systems have been developed that incorporate penalisation options in e-learning systems using many learning styles models, namely, Felder & Silverman model [92,99,137,144,161,202], Kolb method [64,239] or VAK model [78]. Those systems classify learners in several dimensions or categories and tailor the course accordingly.…”

Section: Adaptation To Learning Modalitiesmentioning

confidence: 99%

“…Resource sequencing Genetic Algorithms PLS-ML [135], QUESTOURnament [5,10,230] Learning path, Course structure 4 Memetic Algorithms TPG [206] Learning path, Search process, Learner preferences 1 Ant Colony Optimisation ACSEL [41], AACS [286], LPR [207] Learning CS388 [34], TANGOW/WOTAN [214], PLORS [137], POLCA [92], Protus [99,161], ULEARN [202], AeLS [144] Learning style, Content, Learner preferences, Assignments 8 VARK [78] Learning style, Content, Learner Classification…”

Section: Recommender Systemsmentioning

confidence: 99%

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Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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Section: Adaptation To Learning Modalitiesmentioning

confidence: 99%

Section: Recommender Systemsmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

View full text Add to dashboard Cite

show abstract

“…Various adaptivity parameters are considered when developing adaptive e-learning systems including learner knowledge, learning styles, cognitive abilities, and learning behavior and motivation. Learning resources and adaptive learning are significant in the subject of the learning process of every learner (Diaz et al, 2018) Adaptive e-learning systems are the new trend in e-learning whose goal involves the personalization of learning material and their sequences in matching the needs of the individual student as close as possible. These systems combine the student features like learning styles, affective state, and knowledge level to provide personalized services and recommend the appropriate learning materials to the student.…”

Section: Adaptive E-learningmentioning

confidence: 99%

Untitled

2021

OJIT

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Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current elearning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

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“…The structure of user models constantly changes over time. Some of them consider learning styles (Diaz, Rubilar, Figueroa, & Silva, 2018;Eltigani, Mustafa, & Sharif, 2011;García, Amandi, Schiaffino, & Campo, 2007;Nafea, Siewe, & He, 2017;Rasheed & Wahid, 2019;Truong, 2016) and some uses hybrid models (Al-Shamri & Bharadwaj, 2008) to present user characteristics. Even the same authors over the years recommend different sets of user model attributes; as an example (Brusilovsky & Millán, 2007) and (Gerhard Weber, 2001).…”

Section: User Model and User Characteristicsmentioning

confidence: 99%