Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

Madani, Youness; Bengourram, Jamaâ; Erritali, Mohammed; Hssina, Badr; Birjali, Marouane

doi:10.14569/ijacsa.2017.080851

Cited by 15 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, adaptive e-learning systems have been frequently developed and used to identify the most appropriate learning objects for learners' profiles. [7][8][9]. The vast volume of these www.ijacsa.thesai.org learning objects presents different opportunities, though, presents constraints for learners to locate the most adequate learning objects for their profiles [2].…”

Section: Related Workmentioning

confidence: 99%

Towards an Adaptive e-Learning System Based on Deep Learner Profile, Machine Learning Approach, and Reinforcement Learning

Mustapha,

Soukaina,

Mohammed

et al. 2023

IJACSA

View full text Add to dashboard Cite

Now-a-days, the great challenge of adaptive elearning systems is to recommend an individualized learning scenario according to the specific needs of learners. Therefore, the perfect adaptive e-learning system is the one that is based on a deep learner profile to recommend the most appropriate learning objects for that learner. Yet, the majority of existing adaptive e-learning systems do not give high importance to the adequacy of the real learner profile and its update with the one taken into account in the learning path recommendation. In this paper, we proposed an intelligent adaptive e-learning system, based on machine learning and reinforcement learning. The objectives of this system are the creation of a deep profile of a given learner, via the implementation of K-means and linear regression, and the recommendation of adaptive learning paths according to this deep profile, by implementing the Q-learning algorithm. The proposed system is decomposed into three principal modules, data preprocessing module, learner deep profile creation module, and learning path recommendation module. These three modules interact with each other to provide a personalized adaptation according to the learner's deep profile. The results obtained indicate that taking into account the learner's deep profile improves the quality of learning for learners.

show abstract

Section: Related Workmentioning

confidence: 99%

Towards an Adaptive e-Learning System Based on Deep Learner Profile, Machine Learning Approach, and Reinforcement Learning

Mustapha,

Soukaina,

Mohammed

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…And in order to personalize the learning path [32] proposed the application of supervised learning (decision tree). Among the most used algorithms in adaptive learning is the "Genetic algorithm", which is used to offer learners different concepts to adopt in an optimal way while looking for the most appropriate objectives according to their profiles [33].…”

Section: New Technologies and Adaptive Learningmentioning

confidence: 99%

Design of An Adaptive E-learning Model Based on Artificial Intelligence for Enhancing Online Teaching

Mouna

Lakrami

Labouidya

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Nowadays, teaching involves more and more the usage of digital tools to accompany face-to-face learning. Such a system is being referred to as hybrid teaching. These digital tools include mainly distance learning platforms based on LMS (Learning management systems). LMS help to create on-line digital spaces to store and organize teaching material while providing a pedagogical learning process to connect educational communities. Despite the many forms of content that can be implemented on these platforms, from traditional file-based to interactive and animated content, these systems remain both passive and generic. They lack the ability to adapt to the learner in terms of learning skills, preferences, languages, intellectual abilities, learning patterns and rhythms. This paper proposes a design and modeling of an intelligent and dynamic adaptive learning system based on artificial intelligence with the main objective of identifying and providing personalized learning environments adapted to the learner needs.

show abstract

“…The searching of optimal content for the learner's profile is based on the notion of information retrieval, which measures similarity between the learner profile and the learning objectives. It reduces the size of search space in order to find the relevant documents that fulfill the desired criteria [96] [97] [98]. These algorithms requires parameter setting in order to yield a optimal solution [99].…”

Section: ) Learning Factor Analysismentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Adaptive e-learning using Genetic Algorithm and Sentiments Analysis in a Big Data System

Cited by 15 publications

References 12 publications

Towards an Adaptive e-Learning System Based on Deep Learner Profile, Machine Learning Approach, and Reinforcement Learning

Towards an Adaptive e-Learning System Based on Deep Learner Profile, Machine Learning Approach, and Reinforcement Learning

Design of An Adaptive E-learning Model Based on Artificial Intelligence for Enhancing Online Teaching

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

Contact Info

Product

Resources

About