Adaptive Instructional Planning in Intelligent Learning Systems

Séridi, Hamid; Sari, Toufik; Khadir, Tarek; Sellami, Mokhtar

doi:10.1109/icalt.2006.1652386

Cited by 14 publications

(6 citation statements)

References 1 publication

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“…Adaptive learning technologies would give rise to completely personalized environments with content that not only changes but are created based on the individual needs of the learner. [19] Used Artificial Neural networks for generating adaptive lessons, their work showed the usefulness of the techniques based on some training, which is considered the main drawback of classical methods.…”

Section: Discussion On Future Trends Of Deep Learning In Elearningmentioning

confidence: 99%

Deep Learning: The Impact on Future eLearning

Muniasamy

Alasiry

2020

Int. J. Emerg. Technol. Learn.

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eLearning as technology becomes more affordable in higher education but having a big barrier in the cost of developing its resources. Deep learning using artificial intelligence continues to become more and more popular and having impacts on many areas of eLearning. It offers online learners of the future with intuitive algorithms and automated delivery of eLearning content through modern LMS platforms. This paper aims to survey various applications of deep learning approaches for developing the resources of the eLearning platform, in which predictions, algorithms, and analytics come together to create more personalized future eLearning experiences. In addition, deep learning models for developing the contents of the eLearning platform, deep learning framework that enable deep learn-ing systems into eLearning and its development, benefits & future trends of deep learning in eLearning, the relevant deep learning-based artificial intelligence tools and a platform enabling the developer and learners to quickly reuse resources are clearly summarized. Thus, deep learning has evolved into developing ways to re-purpose existing resources can mitigate the expense of content development of future eLearning.

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Section: Discussion On Future Trends Of Deep Learning In Elearningmentioning

confidence: 99%

Deep Learning: The Impact on Future eLearning

Muniasamy

Alasiry

2020

Int. J. Emerg. Technol. Learn.

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“…They used two critical parameters for fitness function; one is difficulty level and other associations between the course concepts. Another research work on e-learning systems makes use of the nature-inspired algorithm for determining optimal course coverage plans based on the incorrect response on the pretest [32,33].…”

Section: Background and Preliminariesmentioning

confidence: 99%

SeisTutor: A Custom-Tailored Intelligent Tutoring System and Sustainable Education

Singh¹,

Gunjan²,

Mishra

et al. 2022

Sustainability

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Education is the cornerstone of improving people’s lives and achieving global sustainability. Intelligent systems assist sustainable education with various benefits, including recommending a personalized learning environment to learners. The classroom learning environment facilitates human tutors to interact with every learner and obtain the opportunity to understand the learner’s psychology and then provide learning material (access learner previous knowledge and well-align the learning material as per learner requirement) to them accordingly. Implementing this cognitive intelligence in Intelligent Tutoring System is quite tricky. This research focused on mimicking human tutor cognitive intelligence in the computer-aided system of offering an exclusive curriculum or quality education for sustainable learners. The prime focus of this research article was to evaluate the proposed SeisTutor using Kirkpatrick four-phase evaluation model. The experimental results depict the enhanced learning gained through intelligence incorporated SeisTutor against the intelligence absence, as demonstrated.

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“…AntColonyOptimization(ACO),abio-inspiredartificialintelligencetechniquehasalsogained attention. The first ant system was proposed by Seridi, Sari, Khadir, & Sellami, (2006) and was successfullyappliedinsolvingtravelingsalesmanproblem.Theoptimizationoflearningpathin educationalhypermediasystemisdoneinsuchawaythatthestructureofthelearningmaterialis representedbyagraphwithvaluedarcswhoseweightsareoptimizedbyvirtualants(learners)that releasevirtualpheromonesalongtheirpath.InHong,Chen,Chang,&Chen,(2007)theadaptation ofthelearningpathisdonetowardsstudent'slearningstyleandoptimizedusingAnt'sAlgorithm. Bonabeau,Dorigo,&Theraulaz,(1999)introducedanapproachtorecommendingthesequenceof e-learningmodulesfordistancelearnersbasedonself-organizationtheory.Thearchitecturethey namedas'educationalwayfindingsupport'thatsupportstherecording,processingandpresentation ofcollectivebehaviordesignedtocreateafeedbackloop,guidinglearnersonsuccessfulpathtowards learninggoalsbasedonant-algorithm.Similartechniquescanbeappliedusingaprobabilisticgraph wherenodesarepedagogicalitems,arcsarehypertextlinkswiththeirassociatedprobabilitiesand studentsplaytheroleofantsthattraversethegraph (Dronetal.,2002,Weber,&Specht,1997, Brusilovsky,Ritter,&Schwarz,1997.Paraschool (Dahbi,Elkamoun,&Berraissoul,(2006),Vanden Berg,VanEs,Tattersall,Janssen,Manderveld,Brouns,&Koper,(2005)appliedtheACOheuristics topedagogicmaterialnavigationproblem,andValigiani,Biojout,Jamont,Lutton,&Collet,(2005 experimented with an "ant-hill" method which laid the pheromone depending on how students validatedanitem(successorfailure),soastooptimizelearningpathwithdifferentstudentsthathave differentviews.Style-basedantcolonysystem(SACS)bySemet, Lutton,&Collet,(2003), Wang, Wang,&Huang,(2008)isbasedongrouplearningpatternandemployedanextendedant-colony systemapproach.Itisproposedtoconstructausermodelforfindingsuitablelearningpathfrom graph-basedpathstructureforsolvingmostglobaloptimalproblem.…”

Section: Background and Preliminariesmentioning

confidence: 99%

A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

Singh

Ahuja

Kumar

2018

Journal of Cases on Information Technology

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An ideal face-to-face tutor learner interaction aims to offer learning to the learner in a manner that best suits an individual learner's learning level and learning style. This ability of differentiated instruction has been built in Seis-Tutor Intelligent Tutoring system, developed to offer subject matter knowledge of ‘Seismic Data Interpretation,' a field of geo-physics. The detailed architecture of learner-centric curriculum sequencing module, built to this effect, with its components, sub-components, their interconnected functioning, to generate exclusive learning path, have been described. An algorithm for learner-centric curriculum sequencing, a mathematical model and proposed implementation using a case study has been elaborated.

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Adaptive Instructional Planning in Intelligent Learning Systems

Cited by 14 publications

References 1 publication

Deep Learning: The Impact on Future eLearning

Deep Learning: The Impact on Future eLearning

SeisTutor: A Custom-Tailored Intelligent Tutoring System and Sustainable Education

A Novel Architecture for Learner-Centric Curriculum Sequencing in Adaptive Intelligent Tutoring System

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