Introducing a new intelligent adaptive learning content generation method

Haghshenas, Ehsan; Arya, Mazaheri; Gholipour, Ameneh; Tavakoli, Maryam; Zandi, Nasim; Narimani, Hajar; Rahimi, Fahimeh; Nouri, Shima

doi:10.1109/icelet.2010.5708382

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…The Rete algorithm is a rule-based algorithm commonly used for efficient pattern matching and inference in expert systems. [39] Bayesian networks Ant colony optimization Bayesian networks are used to infer the learner's features based on some input data (probably their interactions or preferences in the e-learning system). Once these features are understood, the system uses the 0/1 knapsack problem to select the best learning objects for the learner in the given time constraint.…”

Section: Citation Of Author(s) Algorithm(s)/method(s)mentioning

confidence: 99%

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Gligorea,

Cioca,

Oancea

et al. 2023

Education Sciences

114

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The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

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Section: Citation Of Author(s) Algorithm(s)/method(s)mentioning

confidence: 99%

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Gligorea,

Cioca,

Oancea

et al. 2023

Education Sciences

114

View full text Add to dashboard Cite

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“…The attribute set for learners is input by the learner in case of a current learner and calculated by the system in case of past learners. Haghshenas et al (2010) proposed a method which helped the learners to find the content for learning in an efficient manner. The proposed method found out the learner characteristics using a Bayesian network.…”

Section: Review Of Learning Path Generation Methodsmentioning

confidence: 99%

Sequence generation for learning: a transformation from past to future

Rasheed

Wahid

2019

IJILT

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Purpose The purpose of this paper is to identify the different sequence generation techniques for learning, which are applied to a broad category of personalized learning experiences. The papers have been classified using different attributes, such as the techniques used for sequence generation, attributes used for sequence generation; whether the learner is profiled automatically or manually; and whether the path generated is dynamic or static. Design/methodology/approach The search for terms learning sequence generation and E-learning produced thousands of results. The results were filtered, and a few questions were answered before including them in the review. Papers published only after 2005 were included in the review. Findings The findings of the paper were: most of the systems generated non-adaptive paths. Systems asked the learners to manually enter their attributes. The systems used one or a maximum of two learner attributes for path generation. Originality/value The review pointed out the importance and benefits of learning sequence generation systems. The problems in existing systems and future areas of research were identified which will help future researchers to pursue research in this area.

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“…TherearetworolesinlearningprocessofHE,oneisthelearner,theotheristheteacher (Huang etal.,2014;Badia&Chumpitaz-Campos,2018).ItisthroughinfluencingthesetworolesthatTEL achievedenhancement.Asforinfluencinglearners,technologymainlymakesthemadaptivetothe learningprocess,whichiscalledadaptivelearning.Adaptivelearningisalearningenvironmentto provideindividualorpersonalizedlearninganditisbenefitforimprovinglearningeffectiveness (Haghshenas et al, 2011). As for influencing teachers, technology mainly enhances experiential teaching,whichmeansdeliveringtheirexperiencetolearners.Experientiallearningisaprocessof knowledgecreationinthesocialenvironmentthroughtransformationofexperienceanditismainly benefitforlearningoutcomes (A.Y.Kolb&Kolb,2005).Intheconsequence,asanewtechnology ofTEL,cognitivecomputingbringsabrand-newinfluenceonadaptivelearningandexperiential learningtochangetheformsofTEL.Fromthephasedstageclusteringintheabovesection,weget thekeytechnologieswhichcognitivecomputinghastoinfluencetheTELinnewera,soweconclude differentinfluenceontheadaptiveandexperientiallearningofthesetechnologies.…”

Section: Influencing Framework Of Cognitive Computing On Telmentioning

confidence: 99%

Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing

Cheng

Zhang

Wang

et al. 2021

International Journal on Semantic Web and Information Systems

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Technology-enhanced learning (TEL) is important in social web. Recently, cognitive computing became significant to analyze sentiment and improve effectiveness in TEL field. So analyzing the development of cognitive computing, what and how its abilities improve TEL are necessary. For solving these issues, this study used systematic review approach based on technology view and enhancement view of TEL. Specifically, this study used topic search results in computer science field of “cognitive computing” and “anticipatory computing” in Web of Science database to do map analysis. Besides development footprints, the manuscript describes three development stages and key technologies of cognitive computing through burst study and step-by-step clustering. Finally, this study proposed influencing framework of cognitive computing on TEL and some research trends. This work provides an advanced background of TEL and a systemic review of cognitive computing, contributing to theory development and application of cognitive computing in TEL.

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Introducing a new intelligent adaptive learning content generation method

Cited by 12 publications

References 23 publications

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Sequence generation for learning: a transformation from past to future

Rethinking the Development of Technology-Enhanced Learning and the Role of Cognitive Computing

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