Applying Fuzzy Logic to Customize Learning Materials in e-Learning Systems

2021 ◽  
Vol 14 (2) ◽  
pp. 49-61
Author(s):  
Saida U Ulfa ◽  
Deddy Barnabas Lasfeto ◽  
Izzul Fatawi
Keyword(s):  
Fuzzy Logic ◽  
Learning Systems ◽  
Learning Materials ◽  
E Learning

A comparative study of the use of fuzzy logic in e-learning systems

2015 ◽  
Vol 29 (3) ◽  
pp. 1241-1249 ◽  
Author(s):  
M. Guijarro-Mata-García ◽  
M. Guijarro ◽  
R. Fuentes-Fernández
Keyword(s):  
Fuzzy Logic ◽  
Comparative Study ◽  
Learning Systems ◽  
E Learning

Classroom for the Semantic Web

2005 ◽  
pp. 251-283 ◽  
Author(s):  
Goran Shimic ◽  
Dragan Gasevic ◽  
Vladan Devedzic
Keyword(s):  
Semantic Web ◽  
Petri Net ◽  
Learning Systems ◽  
Learning Materials ◽  
Semantic Web Technologies ◽  
Web Based ◽  
Tutoring Systems ◽  
Web Technologies ◽  
E Learning ◽  
Server System

This chapter emphasizes integration of Semantic Web technologies in intelligent learning systems by giving a proposal for an intelligent learning management system (ILMS) architecture we named Multitutor. This system is a Web-based environment forth development of e-learning courses and for the use of them by the students. Multitutor is designed as a Web-classroom client-server system, ontologically founded, and is built using modern intelligent and Web-related technologies. This system enables the teachers to develop tutoring systems for any course. The teacher has to define the metadata of the course: chapters, the lessons and the tests, the references of the learning materials. We also show how the Multitutor system can be employed to develop learning systems that use ontologically created learning materials as well as Web services. As an illustration we describe a simple Petri net teaching system that is based on the Petrinet infrastructure for the Semantic Web.

scholarly journals Summarizing Learning Materials Using Graph Based Multi-Document Summarization

2021 ◽  
Vol 16 (5) ◽  
pp. 39-57
Author(s):  
Krishnaveni P ◽  
Balasundaram S R
Keyword(s):  
Learning Systems ◽  
Generation Process ◽  
Learning Context ◽  
Learning Materials ◽  
Document Summarization ◽  
Generation Algorithm ◽  
E Learning ◽  
Teaching Learning ◽  
Better Than ◽  
And Task

The learners and teachers of the teaching-learning process highly depend on online learning systems such as E-learning, which contains huge volumes of electronic contents related to a course. The multi-document summarization (MDS) is useful for summarizing such electronic contents. This article applies the task of MDS in an E-learning context. The objective of this article is threefold: 1) design a generic graph based multi-document summarizer DSGA (Dynamic Summary Generation Algorithm) to produce a variable length (dynamic) summary of academic text based learning materials based on a learner's request; 2) analyze the summary generation process; 3) perform content-based and task-based evaluations on the generated summary. The experimental results show that the DSGA summarizer performs better than the graph-based summarizers LexRank (LR) and Aggregate Similarity (AS). From the task-based evaluation, it is observed that the generated summary helps the learners to understand and comprehend the materials easily.

InteliWeb

2008 ◽  
pp. 156-177
Author(s):  
Francine Bica ◽  
Regina Verdin
Keyword(s):  
Biological Sciences ◽  
Fuzzy Logic ◽  
Self Efficacy ◽  
Fuzzy Theory ◽  
Efficacy Beliefs ◽  
Learning Systems ◽  
The Self ◽  
Student Model ◽  
Behavior Motivation ◽  
E Learning

This chapter presents the InteliWeb environment that combines agents, fuzzy logic, and student model to recognize aspects of student’ self-efficacy beliefs to improve the effectiveness of e-learning systems. The self-efficacy construct means the student’s belief on his/her own capacity of performing a task. This belief affects his/her behavior, motivation, affectivity, and the choices he/she makes. We use fuzzy theory for dealing with uncertainty in the assessment of the students and the incomplete knowledge about his/her self-efficacy. The InteliWeb offers instruction material on biological sciences.

Modeling and Evaluating Tutors' Function using Data Mining and Fuzzy Logic Techniques

2016 ◽  
Vol 11 (2) ◽  
pp. 39-60 ◽  
Author(s):  
Safia Bendjebar ◽  
Yacine Lafifi ◽  
Hamid Seridi
Keyword(s):  
Fuzzy Logic ◽  
Nearest Neighbor ◽  
Learning Systems ◽  
K Nearest Neighbor ◽  
New Approach ◽  
First Results ◽  
E Learning ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Fuzzy Logic Technique

In e-learning systems, the tutors play many roles and carry out several tasks that differ from one system to another. The activity of tutoring is influenced by many factors. One factor among them is the assignment of the appropriate profile to the tutor. For this reason, the authors propose a new approach for modeling and evaluating the function of the tutors. This technique facilitates the classification among tutors for adapting tutoring to student's problems. The component of the proposed tutor model is a set of profiles which are responsible for representing the necessary information about each tutor. A fuzzy logic technique is used in order to define tutor's tutoring profile. Furthermore, the K nearest neighbor algorithm is used to offer much information for each new tutor based on the models of other similar tutors. This new approach has been tested by tutors from an Algerian University. The first results were very encouraging and sufficient. They indicate that the use of fuzzy logic technique is very useful and estimate the adaptation of the tutoring process according to tutors' skills.

Assessment of Cognitive Load in Multimedia Learning Using Dual-Task Methodology

2002 ◽  
Vol 49 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Roland Brünken ◽  
Susan Steinbacher ◽  
Jan L. Plass ◽  
Detlev Leutner
Keyword(s):  
Cognitive Load ◽  
Dual Task ◽  
Cognitive Load Theory ◽  
Multimedia Learning ◽  
Learning Systems ◽  
Learning Materials ◽  
New Approach ◽  
Direct Assessment ◽  
Load Theory ◽  
Audiovisual Presentation

Abstract. In two pilot experiments, a new approach for the direct assessment of cognitive load during multimedia learning was tested that uses dual-task methodology. Using this approach, we obtained the same pattern of cognitive load as predicted by cognitive load theory when applied to multimedia learning: The audiovisual presentation of text-based and picture-based learning materials induced less cognitive load than the visual-only presentation of the same material. The findings confirm the utility of dual-task methodology as a promising approach for the assessment of cognitive load induced by complex multimedia learning systems.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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