Towards a Learning Style and Knowledge Level-Based Adaptive Personalized Platform for an Effective and Advanced Learning for School Students

Sayed, Wafaa S.; Gamal, Mostafa; Abdel-Razek, Moemen; El-Tantawy, Samah

doi:10.1007/978-3-030-39847-7_22

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…[36]. Several studies have been found in the literature to automatically identify the learner style using different artificial intelligent classification techniques that include, Reinforcement learning [37] Fuzzy logic, Fuzzy C Means(FCM) algorithm [38], Decision trees techniques, Bayesian Networks techniques, Neural Network Techniques, [35] [39]Software Agents [40], [41] etc. Each technique's performance varies from one to another based on the choice of features, parameters, dataset size, etc.…”

Section: A Learner Modelmentioning

confidence: 99%

A Novel Personalized Learning Framework With Interactive e-Mentoring

Nouman,

Shaikh,

Wasi

2024

IEEE Access

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E-learning has established itself as a new alternative to conventional learning styles to accomplish the goal of education and learning for everyone. The classroom learning style is based on a "one size fits all" approach because, in a typical classroom environment, an instructor has to deal with several students at the same time. A similar problem goes along with the traditional learning management systems in which every student must learn the course devised by the instructor, within a specific timeline and achieve specific objectives, despite students' preferences and capabilities. The recent global pandemic has pushed educational institutes worldwide to recourse to an "online-only" mode of education and teaching delivery which has raised many challenges that need to be addressed. The existing e-learning systems fail to fulfill the expectations of a learner or an instructor in certain ways including the style of content delivery, mode of teaching, an adaptation of learner style that doesn't match with the teaching style, the content type, and above all the e-learning lacks to provide the e-mentoring capability to deal with the challenges a learner and an instructor's face during the learning process. This research aims to develop personalized learning by incorporating an intelligent e-mentor. The proposed e-mentor based learning model is capable of customizing a course for individuals by automatically adapting it to their unique learning styles, preferences, abilities, existing knowledge, and expectations from the course. The outcome of the proposed model shows that e-mentoring not only increased learner satisfaction but also enhanced the learning process making it a preferred choice.

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Section: A Learner Modelmentioning

confidence: 99%

A Novel Personalized Learning Framework With Interactive e-Mentoring

Nouman,

Shaikh,

Wasi

2024

IEEE Access

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“…The advantage of such methods is the ability to model the stochastic variation of outcomes as transition probabilities between states and action [ 72 ]. RL and MDPs have been successfully applied to personalized learning systems [ 54 , 57 ], intelligent tutoring systems [ 8 , 65 ], adaptive serious games for ASD [ 33 ] and robot assisted therapy [ 72 ]. For instance, Bennane [ 12 ] automated the selection of the content of a tutoring system and its pedagogical approach to provide differentiated instruction.…”

Section: Related Workmentioning

confidence: 99%

Toward automatic motivator selection for autism behavior intervention therapy

Siyam

Abdallah²

2022

Univ Access Inf Soc

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Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

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Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

Alshmrany

2021

Cluster Comput

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Towards a Learning Style and Knowledge Level-Based Adaptive Personalized Platform for an Effective and Advanced Learning for School Students

Cited by 4 publications

References 21 publications

A Novel Personalized Learning Framework With Interactive e-Mentoring

A Novel Personalized Learning Framework With Interactive e-Mentoring

Toward automatic motivator selection for autism behavior intervention therapy

Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

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