Implementation of Personalized Adaptive E-learning System

Vagale, Vija; Niedrite, Laila; Ignatjeva, Svetlana

doi:10.22364/bjmc.2020.8.2.06

Cited by 13 publications

(15 citation statements)

References 8 publications

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“…In this section, we compare adaptive recommender systems proposed firstly in terms of adaptability techniques used: Knowledge-based (KB), Content-Based (CB), Userbased (UB) and Method-Used (MU); and secondly in terms of adaptation itself, based on the two types mentioned in above Section I Table I gathers recently proposed adaptive e-learning systems using different methods. We notice that [22], [23] are generally using ontology to model content or user profile in their work along side with machine learning techniques, whereas [26], [24], [25] used knowledge designed graphs to represent the user model, or Q-learning [29] with machine learning techniques.…”

Section: Comparison Of Adaptive Recommender Systemmentioning

confidence: 99%

“…We notice that only Boussakssou et al [25] integrate an adaptation based on user action in their proposed work. Whereas [22], [24] described a group based adaptation and then a content adaptation in [24]. In [28] along with [24] propose an adaptation model that assures course adaptation.…”

Section: Comparison Of Adaptive Recommender Systemmentioning

confidence: 99%

“…Whereas [22], [24] described a group based adaptation and then a content adaptation in [24]. In [28] along with [24] propose an adaptation model that assures course adaptation. This adaptation relies on certain characteristics of the user such as background Knowledge and learning style using Q-learning in El Fazazi et als' works [28].…”

Section: Comparison Of Adaptive Recommender Systemmentioning

confidence: 99%

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Towards Personalized Adaptive Learning in e-Learning Recommender Systems

Sabeima¹,

Lamolle²,

Nanne³

2022

IJACSA

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An adaptive e-learning scenario not only allows people to remain motivated and engaged in the learning process, but it also helps them expand their awareness of the courses they are interested in. e-Learning systems in recent years had to adjust with the advancement of the educational situation. Therefore many recommender systems have been presented to design and provide educational resources. However, some of the major aspects of the learning process have not been explored quite enough; for example, the adaptation to each learner. In learning, and in a precise way in the context of the lifelong learning process, adaptability is necessary to provide adequate learning resources and learning paths that suit the learners' characteristics, skills, etc. e-Learning systems should allow the learner to benefit the most from the presented learning resources content taking into account her/his learning experience. The most relevant resources should be recommended matching her/his profile and knowledge background not forgetting the learning goals she/he would like to achieve and the spare time she/he has in order to adjust the learning session with her/his goals whether it is to acquire or reinforce a certain skill. This paper proposes a personalized elearning system that recommends learning paths adapted to the users profile.

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Section: Comparison Of Adaptive Recommender Systemmentioning

confidence: 99%

Section: Comparison Of Adaptive Recommender Systemmentioning

confidence: 99%

Section: Comparison Of Adaptive Recommender Systemmentioning

confidence: 99%

See 1 more Smart Citation

Towards Personalized Adaptive Learning in e-Learning Recommender Systems

Sabeima¹,

Lamolle²,

Nanne³

2022

IJACSA

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“…We have referred to the articles, published works from other institutions and adaptive learning providers (Pugliese, 2016;Nurjanah, 2016;Dhupia & Alameen, 2019;Vagale et. al., 2020;O'Sullivan, 2021) to draw the ideas in developing our own PALAS system framework.…”

Section: Design and Development Of A Palas System For Niementioning

confidence: 99%

Providing equitable education through personalised adaptive learning

Palanisamy¹,

Thilarajah²,

Chen³

2021

ASCILITE 2021: Back to the Future – ASCILITE ‘21 Proceedings ASCILITE 2021 in Armidale

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Educators around the globe are striving to promote equity in their classrooms by adopting adaptive learning systems to provide customised learning resources and tools to help the learners achieve mastery at their own pace. This personalised approach goes a long way in closing the divide between students' socioeconomic status and special needs. Over the years, the advancement in technology offers more sophisticated adaptive technologies. It aggregates data such as students' prior knowledge and academic performances to predict and better adapt the learning paths. This paper presents the evaluation of adaptive technologies for personalised learning and the vision of a Personalised Adaptive Learning and Assessment (PALAS) system for Higher Education. This vision could be an imperative piece supporting Singapore's ‘National AI Strategy’, set to focus on personalised education through adaptive learning and assessment.

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“…The large-scale application of online education gives more and more learners have access to personalized E-learning resources. Many E-learning systems were proposed which provide unique learning experiences for each learner, such as [1]- [3], to name a few. Among these, the learning path personalization is an essential implementation towards the learner-oriented curriculum design [4].…”

Section: Introductionmentioning

confidence: 99%

Learning Path Optimization Based On Multi-Attribute Matching And Variable Length Continuous Representation

Xiao

Zhang

Song

et al. 2022

Preprint

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Personalized learning path considers both learner and resource attributes. The evolutionary algorithm approach usually forms the learning path generation problem into a problem that optimizes the matching degree of the learner and the generated learning path. The proposed work considers the learner attributes as ability level, learning objective, learning style, and expected learning time. The learning path attributes include difficulty level, covered concept, supported learning styles, required learning time, and prerequisites. A variable-length representation of the learning path is adopted based on floating numbers, which significantly reduces the encoding length and simplifies the learning path generating process. An improved differential evolution algorithm is applied to optimize the matching degree of learning path and learner. The quantitative experiments on different problem scales show the proposed system outperforms the binary-based representation approaches in scaling ability and outperforms the comparative algorithms in efficiency.

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Implementation of Personalized Adaptive E-learning System

Cited by 13 publications

References 8 publications

Towards Personalized Adaptive Learning in e-Learning Recommender Systems

Towards Personalized Adaptive Learning in e-Learning Recommender Systems

Providing equitable education through personalised adaptive learning

Learning Path Optimization Based On Multi-Attribute Matching And Variable Length Continuous Representation

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