Adaptive E-Learning

Shute, Valerie J.; Towle, Brendon

doi:10.4324/9780203764664-4

Cited by 12 publications

(11 citation statements)

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“…Personalized learning empowers students by giving them control throughout their learning, personalizing it, and providing real-time feedback [119]. Shute & Towle [120] argue that adaptive e-learning services and study materials promote individual learning in higher education.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

A review of the Development Trend of Personalized learning Technologies and its Applications

Ambele¹,

Kaijage²,

Dida³

et al. 2022

IJASRE

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Personalized learning tailors material and strategy to student requirements, interests, and goals in e-learning. These developments help educational institutions and other organizations to keep up with the fast pace of information technology, communications, and computing power. Studies show that self-adaptive learning and relevant learning information improve study efficiency.Compared to traditional teaching methods, the practice of online education is well in its infancy. On the other hand, the pedagogy and evaluation of students in online courses have a large gap that has to be filled, necessitating significant improvements in e-learning. We call this approach to education "personalized learning," which is a central focus of today's leading online education platforms. Several studies have been conducted on e-learning and personalized learning, but few investigated the development trend of personalized learning technologies and applications. Therefore this study examines the literature to close the gap and promote the development trend for personalized learning technologies and applications in higher education from 2010 to 2021 by analyzing related journal articles. The pivotal studies used inclusion criteria after a searchgenerated 372 complete research articles and reduced them to 146 publications based on their proposed learning domains and research themes. Through carefully reviewing current trends and successes in numerous aspects of personalized learning, this discussion analyzes prospective future research directions in the field of personalized learning.

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Section: Discussion Of Findingsmentioning

confidence: 99%

A review of the Development Trend of Personalized learning Technologies and its Applications

Ambele¹,

Kaijage²,

Dida³

et al. 2022

IJASRE

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“…Feedback) bez potrebe za intervencijama čoveka odnosno tradicionalnog učitelja (engl. Human Teacher) [4]. Cilj inteligentnog tutoring sistema je da pruži personalizovano podučavanje učeniku i da zameni generalizovanje u nastavi kako bi svakom učeniku pružio visokokvalitetno obrazovanje oblikovano na osnovu njegovih performansi iz određene oblasti [5].…”

Section: Inteligentni Tutoring Sistemiunclassified

Inteligentni Tutoring Sistem Za Obuku Inženjera Softvera

Dorić¹

2022

Zbornik radova FTN

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U sklopu ovog rada je dizajniran i implementiran inteligentni tutoring sistem. Opisane su teorijske osnove na kojima se sistem zasniva, arhitektura sistema, empirijska evaluacija sistema kao i unapređenja koja nas čekaju u narednom periodu. Rezultat rada jeste platforma za učenje koja nosi naziv Clean CaDET Tutor koji se primenjuje na nekoliko predmeta na Fakultetu Tehničkih Nauka u Novom Sadu.

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“…The proposed architecture of system is presented in Figure . The architecture is designed to align with the AES framework as recommended by Shute and Towle .…”

Section: Proposed System Architecturementioning

confidence: 99%

KLSAS—An adaptive dynamic learning environment based on knowledge level and learning style

Dhakshinamoorthy¹,

Dhakshinamoorthy²

2018

Comp Applic In Engineering

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Adaptive learning environment, the extension of E‐learning, has gained its importance in the present education scenario with the growth in information and communication technology. To develop such an environment, suitable learner characteristics have to be selected and learning materials are to be adapted to the selected characteristics with intelligent mechanisms. The effectiveness of the adaptive systems improves with increased number of learner characteristics considered to build the systems. However, the observations from the existing adaptive systems reveal that the development of systems that adapts to more than one learner characteristic is more challenging than systems that adapt to single learner characteristic. This paper proposes an adaptive learning system, KLSAS: Knowledge and Learning Style based Adaptive System, which is based on two significant learner characteristics, knowledge level and learning style. The adaptation model of KLSAS is proposed as a dynamic system which updates itself with learner performance and preferences. The evaluation of the system is done through the test performance of two different set of learners. The results are encouraging and ensuring that the adaptive strategy based on the selected learner characteristics leads to improvement in learning.

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Adaptive E-Learning

Cited by 12 publications

References 1 publication

A review of the Development Trend of Personalized learning Technologies and its Applications

A review of the Development Trend of Personalized learning Technologies and its Applications

Inteligentni Tutoring Sistem Za Obuku Inženjera Softvera

KLSAS—An adaptive dynamic learning environment based on knowledge level and learning style

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