AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform

Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentatio...

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Published inMultimedia tools and applications Vol. 82; no. 3; pp. 3303 - 3333
Main Authors Sayed, Wafaa S., Noeman, Ahmed M., Abdellatif, Abdelrahman, Abdelrazek, Moemen, Badawy, Mostafa G., Hamed, Ahmed, El-Tantawy, Samah
Format Journal Article
LanguageEnglish
Published New York Springer US 01.01.2023
Springer Nature B.V
Subjects
Online AccessGet full text
ISSN1380-7501
1573-7721
DOI10.1007/s11042-022-13076-8

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Abstract Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student’s learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.
AbstractList Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student’s learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.
Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student's learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student's learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.
Author Noeman, Ahmed M.
Hamed, Ahmed
Sayed, Wafaa S.
Abdellatif, Abdelrahman
Badawy, Mostafa G.
El-Tantawy, Samah
Abdelrazek, Moemen
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Issue 3
Keywords Primary school mathematics
Dynamic learner model
Learning style
Gamification
Reinforcement learning
Language English
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PublicationDecade 2020
PublicationPlace New York
PublicationPlace_xml – name: New York
– name: United States
– name: Dordrecht
PublicationSubtitle An International Journal
PublicationTitle Multimedia tools and applications
PublicationTitleAbbrev Multimed Tools Appl
PublicationTitleAlternate Multimed Tools Appl
PublicationYear 2023
Publisher Springer US
Springer Nature B.V
Publisher_xml – name: Springer US
– name: Springer Nature B.V
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CovaciAGhineaGLinCHHuangSHShihJLMultisensory games-based learning-lessons learnt from olfactory enhancement of a digital board gameMultimed Tools Appl20187716212452126310.1007/s11042-017-5459-2
LeiteWLSvinickiMShiYAttempted validation of the scores of the vark: Learning styles inventory with multitrait–multimethod confirmatory factor analysis modelsEduc Psychol Meas201070232333910.1177/0013164409344507
Johnson L, Becker SA, Estrada V, Freeman A (2014) NMC horizon report: 2014 K. The New Media Consortium
ArqubOAAbo-HammourZNumerical solution of systems of second-order boundary value problems using continuous genetic algorithmInf Sci201427939641532120951354.6515510.1016/j.ins.2014.03.128
JamesWBBlankWEReview and critique of available learning-style instruments for adultsNew Directions for Adult and Continuing Education1993199359475710.1002/ace.36719935907
DolencKAberšekBTech8 intelligent and adaptive e-learning system: Integration into technology and science classrooms in lower secondary schoolsComput Educ20158235436510.1016/j.compedu.2014.12.010
HubalovskySHubalovskaMMusilekMAssessment of the influence of adaptive e-learning on learning effectiveness of primary school pupilsComput Hum Behav20199269170510.1016/j.chb.2018.05.033
Analytica O (2016) Gamification and the future of education. World Government Summit
PruetPAngCSFarzinDUnderstanding tablet computer usage among primary school students in underdeveloped areas: Students’ technology experience, learning styles and attitudesComput Hum Behav2016551131114410.1016/j.chb.2014.09.063
SarwarSQayyumZUGarcía-castroRSafyanMMunirRFOntology based e-learning framework: A personalized, adaptive and context aware modelMultimed Tools Appl20197824347453477110.1007/s11042-019-08125-8
Chen CH (2014) An adaptive scaffolding e-learning system for middle school students? physics learning. Australasian Journal of Educational Technology 30(3)
Faiella F, Ricciardi M (2015) Gamification and learning: a review of issues and research. Journal of e-Learning and Knowledge Societ 11(3)
WebbSChangACSHow does prior word knowledge affect vocabulary learning progress in an extensive reading program?Stud Second Lang Acquis201537465167510.1017/S0272263114000606
NolanJMcBrideMBeyond gamification: reconceptualizing game-based learning in early childhood environmentsInf Commun Soc201417559460810.1080/1369118X.2013.808365
Alhathli M, Masthoff J, Siddharthan A (2017) Should learning material’s selection be adapted to learning style and personality?. In: Adjunct publication of the 25th conference on user modeling, adaptation and personalization, pp 275–280
KnutasAVan RoyRHynninenTGranatoMKasurinenJIkonenJA process for designing algorithm-based personalized gamificationMultimed Tools Appl20197810135931361210.1007/s11042-018-6913-5
Krathwohl DR, Anderson L W (2009) A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman
XiaoLWanXLuXZhangYWuDIot security techniques based on machine learning: How do iot devices use ai to enhance security?IEEE Signal Proc Mag2018355414910.1109/MSP.2018.2825478
HuangCJLiuMCChangKESungYTHuangTHChenCHShenHYHuangKLLiaoJJHuKWA learning assistance tool for enhancing ict literacy of elementary school studentsJ Educ Technol Soc2010133126138
IglesiasAMartínezPAlerRFernándezFReinforcement learning of pedagogical policies in adaptive and intelligent educational systemsKnowl Based Syst200922426627010.1016/j.knosys.2009.01.007
EarlyDMMaxwellKLBurchinalMAlvaSBenderRHBryantDCaiKCliffordRMEbanksCGriffinJATeachers’ education, classroom quality, and young children’s academic skills: Results from seven studies of preschool programsChild Dev200778255858010.1111/j.1467-8624.2007.01014.x
ChuYSYangHCTsengSSYangCCImplementation of a model-tracing-based learning diagnosis system to promote elementary students’ learning in mathematicsJ Educ Technol Soc2014172347357
VandewaetereMDesmetPClareboutGThe contribution of learner characteristics in the development of computer-based adaptive learning environmentsComput Hum Behav201127111813010.1016/j.chb.2010.07.038
NormadhiNBAShuibLNasirHNMBimbaAIdrisNBalakrishnanVIdentification of personal traits in adaptive learning environment: Systematic literature reviewComput Educ201913016819010.1016/j.compedu.2018.11.005
BloomBSThe 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoringEduc Res198413641610.3102/0013189X013006004
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HsiehSWJangYRHwangGJChenNSEffects of teaching and learning styles on students? reflection levels for ubiquitous learningComput Educ20115711194120110.1016/j.compedu.2011.01.004
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PanWFThe effects of using the kinect motion-sensing interactive system to enhance english learning for elementary studentsJ Educ Technol Soc2017202188200
BimbaATIdrisNAl-HunaiyyanAMahmudRBShuibNLBMAdaptive feedback in computer-based learning environments: a reviewAdapt Behav201725521723410.1177/1059712317727590
Huey-MinWBor-ChenKSu-ChenWComputerized dynamic adaptive tests with immediately individualized feedback for primary school mathematics learningJ Educ Technol Soc201720161
VARK (2018) http://vark-learn.com/the-vark-questionnaire/the-vark-questionnaire-for-younger-people/. Accessed 30 April 2020
BeckJWoolfBPBealCRAdvisor: a machine learning architecture for intelligent tutor constructionAAAI/IAAI20002000552-55712
Koć-JanuchtaMMHöfflerTNEckhardtMLeutnerDDoes modality play a role? visual-verbal cognitive style and multimedia learningJ Comput Assist Learn201935674775710.1111/jcal.12381
PrabhaSLShanavasAMEducational data mining applicationsOperations Research and Applications: An International Journal (ORAJ)2014112329
LinCHLiuEZFChenYLLiouPYChangMWuCHYuanSMGame-based remedial instruction in mastery learning for upper-primary school studentsJ Educ Technol Soc2013162271281
PotterKHagenHKerrenADannenmannPMethods for presenting statistical information: The box plotVisualization of large and unstructured data sets2006497106
Balasubramanian V, Anouncia SM (2016) Learning style detection based on cognitive skills to support adaptive learning environment–a reinforcement approach. Ain Shams Engineering Journal
Arqub OA, Al-Smadi M (2020) Fuzzy conformable fractional differential equations: novel extended approach and new numerical solutions. Soft Computing
Shawky D, Badawi A (2018) A reinforcement learning-based adaptive learning system. In: International Conference on Advanced Machine Learning Technologies and Applications. Springer, pp 221–231
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Vandewaetere M, Clarebout G (2014) Advanced technologies for personalized learning, instruction, and performance. In: Handbook of research on educational communications and technology. Springer, pp 425–437
KulikJAFletcherJEffectiveness of intelligent tutoring systems: a meta-analytic reviewRev Educ Res2016861427810.3102/0034654315581420
TurchiTFogliDMaliziaAFostering computational thinking through collaborative game-based learningMultimed Tools Appl20197810136491367310.1007/s11042-019-7229-9
KeefeJWLearning style profile handbook: II1989Accommodating PerceptualStudy, and Instructional Preferences. ERIC
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GylfasonTNatural resources, education, and economic developmentEur Econ Rev2001454-684785910.1016/S0014-2921(01)00127-1
OkpoJAMasthoffJDennisMBeachamNAdapting exercise selection to performance, effort and self-esteemNew Rev Hypermedia Multimed201824319322710.1080/13614568.2018.1477999
YoungSSCKuHHA study of uses of ict in primary education through four winning school cases in the Taiwan schools cyberfairJ Educ Technol Soc20081135266
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El-TantawySAbdulhaiBAbdelgawadHDesign of reinforcement learning parameters for seamless application of adaptive traffic signal controlJ Intell Transp Syst201418322724510.1080/15472450.2013.810991
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– reference: BloomBSThe 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoringEduc Res198413641610.3102/0013189X013006004
– reference: IglesiasAMartínezPAlerRFernándezFReinforcement learning of pedagogical policies in adaptive and intelligent educational systemsKnowl Based Syst200922426627010.1016/j.knosys.2009.01.007
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– reference: PremlathaKGeethaTLearning content design and learner adaptation for adaptive e-learning environment: a surveyArtif Intell Rev201544444346510.1007/s10462-015-9432-z
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– reference: HuangCJLiuMCChangKESungYTHuangTHChenCHShenHYHuangKLLiaoJJHuKWA learning assistance tool for enhancing ict literacy of elementary school studentsJ Educ Technol Soc2010133126138
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– reference: LinCHLiuEZFChenYLLiouPYChangMWuCHYuanSMGame-based remedial instruction in mastery learning for upper-primary school studentsJ Educ Technol Soc2013162271281
– reference: ThepsatitpornSPichitpornchaiCVisual event-related potential studies supporting the validity of vark learning styles’ visual and read/write learnersAdv Physiol Educ201640220621210.1152/advan.00081.2015
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Snippet Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This...
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SubjectTerms Adaptation
Algorithms
Artificial intelligence
Computer Communication Networks
Computer Science
Customization
Data Structures and Information Theory
Decision making
Distance learning
Gamification
Group theory
Instructional scaffolding
Machine learning
Multimedia Information Systems
Navigation
Online instruction
Scaffolding
Special Purpose and Application-Based Systems
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Title AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform
URI https://link.springer.com/article/10.1007/s11042-022-13076-8
https://www.ncbi.nlm.nih.gov/pubmed/35789938
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Volume 82
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