A type-2 fuzzy logic recommendation system for adaptive teaching

Almohammadi, Khalid; Hagras, Hani; Yao, Bo; Alzahrani, Abdulkareem; Alghazzawi, Daniyal; Aldabbagh, Ghadah

doi:10.1007/s00500-015-1826-y

Cited by 34 publications

(19 citation statements)

References 24 publications

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“…In our system, we use interior FOU models and right and left shoulder MFs (shown in Figure 3a, Figure 3b and Figure 3c) for the upper and lower MF parameters from all the embedded non-symmetric triangular type-1 MFs. As shown in Figure 3a, the resulting interior interval type-2 fuzzy set is described by parameters a MF ,c MF , c MF , and b MF denoting a trapezoidal upper MF and parameters a MF and b MF for a non-symmetric triangular lower MF, with an intersection point (p, p ) [28], [29], [30], [31]. The calculating processes for these parameters are detailed below.…”

Section: Application Layermentioning

confidence: 99%

“…In this way, input and output values are quantified and converted into linguistic labels such as high, very high, low and very low. The details of the implemented approach are found in [28], [29], [30], [31] and cause the creation of the FOU and a type-2 fuzzy set, where several type-1 fuzzy sets are embedded to signify the individual perceptions of every student regarding a specific linguistic label that elaborates requirements, preferences and features. Consequently, different students' opinions regarding the modelling of words will be combined by FOU generated from type-2 fuzzy sets, and the uncertainties will also be managed.…”

Section: Extracting the Interval Type-2 Fuzzy Setsmentioning

confidence: 99%

“…To generate systemlearned rules, a layer of learning fuzzy rules will then receive those inputs and outputs. 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%

“…Here, i th type-1 fuzzy set and ∪ is an operation of aggression [28], [29], [30], [31] embedded in the reference of A i . Approximations of lower MFÃ (x) ofÃ s and upper MFÃ (x) serve as the basis of generating the process ofÃ .…”

Section: Application Layermentioning

confidence: 99%

“…Following this stage, utilizing the approach of [28], [29], [30], [31], the type-2 fuzzy sets are constructed where the type-1 fuzzy sets (representing the students' individual preferences) are combined, resulting in the FOU of the type-2 fuzzy set, which represents the given word. By applying the representation theorem [12], [28], [29], [30], [31] the calculation of each interval type-2 fuzzy setÃ s will be: …”

Section: Extracting the Interval Type-2 Fuzzy Setsmentioning

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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