An overview of adaptive e-learning systems

A personalized e-learning system is effective in imparting enhanced learning to its users. As compared to a conventional e-learning system, which provides similar contents to each learner, a personalized learning system provides specific learning contents and assessments to the learners. Personalization is based on Artificial Intelligence (AI) based techniques in which appropriate contents for each learner are determined using the level of comprehension of the learner and the preferred modes of learning. This paper presents requirements and challenges for a personalized e-learning system. The paper is focused in elaborating four research questions, which are related to identifying key factors of personalized education, elaborating on state of the art research in the domain, utilizing benefits of AI in personalized education, and determining future research directions. The paper utilizes an extensive survey of current research papers in answering these questions. It provides a comprehensive review of existing solutions in offering personalized e-learning solutions. It also elaborates on different learning models and learning theories, which are significant in providing personalized education. It proposes an efficient framework, which can offer personalized elearning to each learner and identifies significant directions for future research. The paper is beneficial for academicians and researchers in understanding the requirements of such a system, comprehending its methodologies, and identifying challenges which are needed to be addressed.

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“…4. These attributes are computed for every learner and are used iteratively to generate personalized learning path for each learner [151].…”

Section: A Data Modulementioning

confidence: 99%

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

et al. 2022

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“…Since the means whereby these environments integrate technology into learning processes is said to improve learning outcomes [7], TELEs have been discussed before the crisis. TELEs enable adaptive learning [8], thus allowing not only students to learn at their own pace [9] but also to adjust "instructional actions" according to the students' characteristics [10, p.336]. Scientific research emphasizes different relevant aspects of TELEs.…”

Section: Introductionmentioning

confidence: 99%

Technology-Enhanced Learning Environments and Adaptive Learning Systems – Development of Functionality Taxonomies

Bockshecker¹,

Ebner²,

Smolnik³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Especially against the background of the current coronavirus crisis, technology-enhanced learning environments (TELEs) increasingly characterize teaching at universities. For the successful use and integration of TELEs, it is important to understand the functionalities of the technologies used. Based on the state of the art and following [1], we develop two taxonomies. The first taxonomy depicts eleven functionalities with different dimensions relevant for successfully designing TELEs. Sound knowledge of the functionalities supports research on adaptive learning within TELEs and the implementation of studentcentered learning opportunities, which is structured in a second functionality taxonomy for adaptive learning systems (ALSs). We contribute to current research on TELEs and ALSs by providing a structured overview of functionalities and suggestions for further research with our research opportunities.

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“…Research in adaptive learning goes back in the1990s. During that time researchers were looking at two major areas: hypertext and user modelling (Ennouamani & Mahani, 2017). Research in adaptive learning has grown ever since.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

Anne¹,

Maina²

2021

OJIT

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We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

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An overview of adaptive e-learning systems

Cited by 55 publications

References 34 publications

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions

Technology-Enhanced Learning Environments and Adaptive Learning Systems – Development of Functionality Taxonomies

Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

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