Fuzzy Logic Based Context Aware Recommender for Smart E-learning Content Delivery

Gogo, Kevin Otieno; Nderu, Lawrence; Mwangi, Ronald Waweru

doi:10.1109/iscmi.2018.8703247

Cited by 17 publications

(4 citation statements)

References 18 publications

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“…e integration of the context-awareness in the e-learning system will be an efficient technique to enhance learning. e authors in [19][20][21][22][23][24] suggested using artificial intelligence (AI) techniques, such as data mining and fuzzy logic, to enhance the e-learning strategies with a smart way and augment interactions between learners. Most of these systems limit the context to the learner's assessment score, the time needed to complete the assessment test, histories, etc.…”

Section: Related Workmentioning

confidence: 99%

Cognitive IoT-Based e-Learning System: Enabling Context-Aware Remote Schooling during the Pandemic

Zaguia

Ameyed

Haddar

et al. 2021

Journal of Healthcare Engineering

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The 2019–2020 coronavirus pandemic had far-reaching consequences beyond the spread of the disease and efforts to cure it. Today, it is obvious that the pandemic devastated key sectors ranging from health to economy, culture, and education. As far as education is concerned, one direct result of the spread of the pandemic was the resort to suspending traditional in-person classroom courses and relying on remote learning and homeschooling instead, by exploiting e-learning technologies, but many challenges are faced by these technologies. Most of these challenges are centered around the efficiency of these delivery methods, interactivity, and knowledge testing. These issues raise the need to develop an advanced smart educational system that assists home-schooled students, provides teachers with a range of smart new tools, and enable a dynamic and interactive e-learning experience. Technologies like the Internet of things (IoT) and artificial intelligence (AI), including cognitive models and context-awareness, can be a driving force in the future of e-learning, opening many opportunities to overcome the limitation of the existing remote learning systems and provide an efficient reliable augmented learning experience. Furthermore, virtual reality (VR) and augmented reality (AR), introduced in education as a way for asynchronous learning, can be a second driving force of future synchronous learning. The teacher and students can see each other in a virtual class even if they are geographically spread in a city, a country, or the globe. The main goal of this work is to design and provide a model supporting intelligent teaching assisting and engaging e-learning activity. This paper presents a new model, ViRICTA, an intelligent system, proposing an end-to-end solution with a stack technology integrating the Internet of things and artificial intelligence. The designed system aims to enable a valuable learning experience, providing an efficient, interactive, and proactive context-aware learning smart services.

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Section: Related Workmentioning

confidence: 99%

Cognitive IoT-Based e-Learning System: Enabling Context-Aware Remote Schooling during the Pandemic

Zaguia

Ameyed

Haddar

et al. 2021

Journal of Healthcare Engineering

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show abstract

“…Most of the changes we will make to our systems in the future will involve adding more computer-aided services to help teachers see and respond to how students are acting. In the work of [69,70], the authors proposed that artificial intelligence approaches such as data mining and fuzzy logic be used to smarten up e-learning tactics and augment students' learning. Most of these systems are limited by the time to finish the assessment exam, the learner's evaluation criteria, history, and so on [24].…”

Section: Related Workmentioning

confidence: 99%

Investigating Internet of Things Impact on e-Learning System: An Overview

Elneel¹,

Kahtan²,

Fakharudin³

et al. 2023

IJACSA

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Learning systems have reached their peak with the revolution of smart technologies. In the past few years, the Internet of Things (IoT) has become one of the most advanced and popular technologies, affecting many different areas. Using IoT in an e-learning system is a fantastic technology that improves the e-learning system and makes it more inventive and cutting-edge. The key challenge addressed in this study is the acceptance of IoT usage in e-learning systems as well as how to improve it so that it can be utilized properly. This research concentrates on how IoT can benefit e-learning systems and how it might benefit users of e-learning systems. A comprehensive literature review was conducted to get acquainted with the important research related to IoT technology and e-learning systems through online research databases and reliable scientific journals. The first research finding is that e-learning systems need such modern techniques as IoT to enable interconnection, increase reliability, and enhance the enjoyment of the educational process. The second result is that research related to the development of new technologies like the IoT has a significant impact on enhancing the performance of new systems and bringing about positive change. This study highlights the value of IoT, particularly in e-learning systems. It aids in the development of new strategies that will improve the efficacy of e-learning systems and stimulate researchers to develop advanced technology.

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“…and generated recommendations for mobile learners. Also, Gogo et al [94] have created a context-aware recommender for learners using the fuzzy logic data mining method. In [95], Sarwar et al categorized the learners based on their profiles and aimed to provide adaptive and personalized learning content for the learners according to their learning abilities.…”

Section: Context-aware Recommendation Systems In Smart Educationmentioning

confidence: 99%

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

Nawara

Kashef

2021

IEEE Access

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An essential goal of recommendation systems is to provide users with accurate and personalized recommendations that meet their preferences. With the rapid growth of IoT-connected sensors, the availability of contextual information has increased, and this has necessitated the fast development of Context-Aware Recommendation Systems (CARS). Context-Aware recommenders are different from traditional recommenders because of their ability to predict the ratings of target users/items by exploiting the knowledge of contextual information. Context-aware recommenders define the context as any information that characterizes the situations of items and users at a particular interaction. They are essential for some contexts where prediction can be more precise in generating specific personalized recommendations. This paper provides a comprehensive review of context-based recommendation systems in IoT environments, namely IoT-CARS, and sheds light on their requirements, characteristics, and applications. We characterize context-aware recommenders in terms of the different IoT contexts and how these contexts are modeled. We also highlight the used metrics to evaluate the performance of various context-based recommenders.

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Fuzzy Logic Based Context Aware Recommender for Smart E-learning Content Delivery

Cited by 17 publications

References 18 publications

Cognitive IoT-Based e-Learning System: Enabling Context-Aware Remote Schooling during the Pandemic

Cognitive IoT-Based e-Learning System: Enabling Context-Aware Remote Schooling during the Pandemic

Investigating Internet of Things Impact on e-Learning System: An Overview

Context-Aware Recommendation Systems in the IoT Environment (IoT-CARS)–A Comprehensive Overview

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