An adaptive fuzzy logic based system for improved knowledge delivery within intelligent E-Learning platforms

Almohammadi, Khalid; Hagras, Hani

doi:10.1109/fuzz-ieee.2013.6622350

Cited by 29 publications

(23 citation statements)

References 22 publications

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“…It also renders an algorithm which is used for context matching. In 2013, Almohammadi and Hagras [1] proposed a system which was based on adaptive fuzzy logic and used for improving the knowledge delivery on the intelligent e-learning platforms. This research work created a self-learning system which could be able to generate a fuzzy logic based model.…”

Section: Intelligent Tutoring Systemmentioning

confidence: 99%

Intelligent e-Learning Systems: An Educational Paradigm Shift

Bhattacharya¹,

Nath²

2016

IJIMAI

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-Learning is the long process of transforming information as well as experience into knowledge, skills, attitude and behaviors. To make up the wide gap between the demand of increasing higher education and comparatively limited resources, more and more educational institutes are looking into instructional technology. Use of online resources not only reduces the cost of education but also meet the needs of society. Intelligent e-learning has become one of the important channels to reach out to students exceeding geographic boundaries. Besides this, the characteristics of e-learning have complicated the process of education, and have brought challenges to both instructors and students. This paper will focus on the discussion of different discipline of intelligent e-learning like scaffolding based e-learning, personalized e-learning, confidence based e-learning, intelligent tutoring system, etc. to illuminate the educational paradigm shift in intelligent e-learning system.

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Section: Intelligent Tutoring Systemmentioning

confidence: 99%

Intelligent e-Learning Systems: An Educational Paradigm Shift

Bhattacharya¹,

Nath²

2016

IJIMAI

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“…This layer aims at first extracting interval type-2 fuzzy sets in relation to system output and input on the basis of creating type-2 fuzzy sets and method centering on methodology detailed in [28], [29], [30], [31], out of a sample of respondents (30 students in the case of the conducted experiments) for managing linguistic uncertainty. Having gathered data (one week is needed in the case of conducting experiments) and accumulated the fuzzy sets, the system will enter the state of building fuzzy rules with the purpose of detailing the most required instructional actions having the satisfactory present conditions of student capabilities and characteristics based on an unsupervised one-pass approach, as motivated through [32], [33], [34].…”

Section: The Proposed User-centricmentioning

confidence: 99%

“…Significantly, the data (present outputs and inputs) will be actively recorded upon the change of the knowledge delivery needs or characteristics. Therefore, a descriptive model of the students' knowledge delivery needs and characteristics is created and learned by our system; this is achieved through the data gathered, generating a set of multiinput and output data pairs, which take the following form [32], [33], [34]:…”

Section: The Observer Componentmentioning

confidence: 99%

“…The following rule will be referred to as the rule generated by (x (t) ;; y (t) ) [32], [33] and [34]:…”

Section: Extracting the Fuzzy Rule From The Collected Datamentioning

confidence: 99%

“…An improved approach of the Wang-Mendel method is the basis of the applied method of rule extraction [32], [33] and [34].…”

Section: Extracting the Fuzzy Rule From The Collected Datamentioning

confidence: 99%

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Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Almohammadi

Hagras

Alghazzawi

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

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Technological advancements within the educational sector and online learning promoted portable data-based adaptive techniques to influence the developments within transformative learning and enhancing the learning experience. However, many common adaptive educational systems tend to focus on adopting learning content that revolves around pre-black box learner modelling and teaching models that depend on the ideas of a few experts. Such views might be characterized by various sources of uncertainty about the learner response evaluation with adaptive educational system, linked to learner reception of instruction. High linguistic uncertainty levels in e-learning settings result in different user interpretations and responses to the same techniques, words, or terms according to their plans, cognition, pre-knowledge, and motivation levels. Hence, adaptive teaching models must be targeted to individual learners' needs. Thus, developing a teaching model based on the knowledge of how learners interact with the learning environment in readable and interpretable white box models is critical in the guidance of the adaptation approach for learners' needs as well as understanding the way learning is achieved. This paper presents a novel interval type-2 fuzzy logic-based system which is capable of identifying learners' preferred learning strategies and knowledge delivery needs that revolves around characteristics of learners and the existing knowledge level in generating an adaptive learning environment. We have conducted a large scale evaluation of the proposed system via real-word experiments on 1458 students within a massively crowded e-learning platform. Such evaluations have shown the proposed interval type-2 fuzzy logic system's capability of handling the encountered uncertainties which enabled to achieve superior performance with regard to better completion and success rates as well as enhanced learning compared to the non-adaptive systems, adaptive system versions led by the teacher, and type-1-based fuzzy based counterparts.

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Improved fuzzy‐assisted multimedia‐assistive technology for engineering education

Chen

Subramani

et al. 2020

Comp Applic In Engineering

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Presently, technological developments within the academic and online education sectors have enabled mobile data‐based interactive strategies to impact transformative learning innovations for improving the learning experience. Nowadays, fuzzy logic has become a part of engineering educational systems, and it provides uncertainty‐defining logic. In this paper, the following method has been proposed for the engineering educational system: Recommending groups in E‐learning‐based fuzzy learning approach with multimedia‐assistive technology. In the recent past, assistive technologies have served as a tool or platform for resolving problems and improved learning through many factors, such as critical thinking, multimodal communication, and motivational approaches for students. Currently, the application of fuzzy logic in education has helped in making decisions in an effective manner. These technologies and experiments have significant roles in student success, because a classical student evaluation and a group study approach for high‐quality learning are no longer sufficient.

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An adaptive fuzzy logic based system for improved knowledge delivery within intelligent E-Learning platforms

Cited by 29 publications

References 22 publications

Intelligent e-Learning Systems: An Educational Paradigm Shift

Intelligent e-Learning Systems: An Educational Paradigm Shift

Users-Centric Adaptive Learning System Based on Interval Type-2 Fuzzy Logic for Massively Crowded E-Learning Platforms

Improved fuzzy‐assisted multimedia‐assistive technology for engineering education

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