A Framework to Enhance the Learning Outcome with Fuzzy Logic-Based ABLS (Adaptive Behaviourial Learning System)

2017 ◽  
pp. 3-11
Author(s):  
Suman Deb ◽  
Jagrati ◽  
Paritosh Bhattacharya
Keyword(s):  
Fuzzy Logic ◽  
Learning Outcome ◽  
Learning System

FUZZY DYNAMIC LOGIC

2006 ◽  
Vol 02 (01) ◽  
pp. 43-55 ◽  
Author(s):  
LEONID I. PERLOVSKY
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Logic ◽  
Learning System ◽  
Dynamic Adaptation ◽  
Fuzzy Models ◽  
Uncertain Knowledge ◽  
Model Improvement ◽  
The Mind ◽  
Fuzzy State ◽  
Theory Of Forms

Fuzzy logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as a joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an image recognition example of patterns below clutter. The paper discusses relationships to formal logic, fuzzy logic, complexity and draws tentative connections to Aristotelian theory of forms and working of the mind.

scholarly journals Using Different Assessment Indicators in Supporting Online Learning

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Online Learning ◽  
Statistical Analysis ◽  
Learning Outcomes ◽  
Learning Process ◽  
Learning Outcome ◽  
Learning System ◽  
Difficulty Level ◽  
Learning Methods ◽  
Analysis Algorithm ◽  
Efficient Learning

The assessment outcome for many online learning methods are based on the number of correct answers and than convert it into one final mark or grade. We discovered that when using online learning, we can extract more detail information from the learning process and these information are useful for the assessor to plan an effective and efficient learning model for the learner. Statistical analysis is an important part of an assessment when performing the online learning outcome. The assessment indicators include the difficulty level of the question, time spend in answering and the variation in choosing answer. In this paper we will present the findings of these assessment indicators and how it can improve the way the learner being assessed when using online learning system. We developed a statistical analysis algorithm which can assess the online learning outcomes more effectively using quantifiable measurements. A number of examples of using this statistical analysis algorithm are presented.

scholarly journals Innovative Shift in Smart Learning Environment

2020 ◽  
pp. 36-44
Author(s):  
Hadya S. Hawedi ◽  
Abdulghader Abu Reemah A. Abdullah
Keyword(s):  
Learning Environment ◽  
Information And Communication Technologies ◽  
Learning Outcome ◽  
Learning System ◽  
Organizational Setting ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Learning Platform ◽  
Search Tool ◽  
Teaching Learning

Innovations in smart learning represent a domain of knowledge transfer platform to boost the effectiveness of the educational practices and learning outcome. The uniqueness of this form of electronic-assisted has been widely accepted as it provides its users with a powerful multi-search tool to access learning content that meet their intended needs. The transformative changes in the learning platform provide a flexible teaching/learning online outlet that has increasingly been adopted to improve learning outcome. In this study, the effectiveness of innovation in information and communication technologies (ICT) as a platform to rapidly transformed learning environment is discussed. Potential of smart learning environment is discussed and relevance of its flexibility in the learning environment and adaptable features for training workers in an organizational setting. This review extensively highlights the position of smart learning system in improving conventional learning and organizational practices that are limited in scope and their functioning environment.

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

2016 ◽  
Vol 6 (2) ◽  
pp. 81-101 ◽  
Author(s):  
Khalid Almohammadi ◽  
Hani Hagras ◽  
Daniyal Alghazzawi ◽  
Ghadah Aldabbagh
Keyword(s):  
Fuzzy Logic ◽  
Learning Environment ◽  
Adaptive Learning ◽  
Learning System ◽  
Superior Performance ◽  
Teaching Models ◽  
Learning Platform ◽  
E Learning ◽  
Interval Type

Abstract 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.

scholarly journals Effects of using E-learning system on learning outcomes of students in Can Tho University

2020 ◽  
Vol 12 (3) ◽  
pp. 27-37
Author(s):  
Ngo My Tran ◽  
Thach Keo Sa Rate ◽  
Nguyen Ngoc Thao
Keyword(s):  
Technology Acceptance ◽  
Learning Outcomes ◽  
Structural Equation ◽  
Community Building ◽  
Learning Outcome ◽  
Equation Model ◽  
Learning System ◽  
Perceived Learning ◽  
E Learning ◽  
The Impact

This study is conducted to assess the impact of using E-learning on students’ learning outcomes through combining models of technology acceptance model (TAM) and an information system success model (D&M model) basing on surveyed data from 294 students using E-learning system in Can Tho university. The main method used to evaluate this impact is structural equation model analysis method (SEM). The empirical results showed that perceived learning outcome is statistically influenced by three factors including learning assistance, community building assistance and perceived motivation. Of which, the community building assistance factor was found to play a strong role of students’ perceived learning outcome. Therefore, building solutions to develop of perceived motivation and community building assistance of the system in order to improve E-learning usage in Can Tho university should be paid attention in the coming time.

Context-based Reasoning through Fuzzy Logic for Edge Intelligence

2021 ◽  
Vol 15 (01) ◽  
pp. 17-25
Author(s):  
Ramin Firouzi ◽  
Rahim Rahmani ◽  
Theo Kanter
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Edge Computing ◽  
Learning System ◽  
Process Data ◽  
Real Time Processing ◽  
Low Level ◽  
Context Reasoning ◽  
Novel Approach ◽  
Rich Data

With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that are physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.

DEVELOPMENT OF PERSONALISED MOBILE LEARNING SYSTEM USING FUZZY LOGIC TECHNIQUE

2019 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
CATHERINE AGBONIFO OLUWATOYIN ◽  
FATAI ADELEKE KAYODE ◽  
◽  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Learning ◽  
Learning System ◽  
Mobile Learning System ◽  
Fuzzy Logic Technique
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