Learning Style as a Parameter in a Unified e-Learning System Architecture: The Adaptive Diagnosis

2008 ◽  
pp. 384-388
Author(s):  
Sotirios Botsios ◽  
Dimitrios Georgiou
Keyword(s):  
Learning Style ◽  
System Architecture ◽  
Learning System ◽  
Learning System Architecture ◽  
E Learning

The Learning Style-Based Adaptive Learning System Architecture

2015 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Chyun-Chyi Chen ◽  
Po-Sheng Chiu ◽  
Yueh-Min Huang
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
System Architecture ◽  
Learning System ◽  
Learning Materials ◽  
Learning Path ◽  
Different Types ◽  
Learning System Architecture ◽  
Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

scholarly journals ARTIFICIAL INTELLIGENCE INFLUENCE IN EDUCATION 4.0 TO ARCHITECTURE CLOUD BASED E-LEARNING SYSTEM

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Purwono Hendradi
Keyword(s):  
System Architecture ◽  
Learning System ◽  
Application Layer ◽  
Artificial Intelligent ◽  
Business Application ◽  
Learning System Architecture ◽  
E Learning ◽  
Education Today ◽  
System Architecture Design

Business Application Layer in the Architecture of E-learning cloud is an important part, because it is the part that differentiates it from the application of cloud in other fields. The development of education today recognizes the term Education 4.0 which is an adaptation of the Industrial era 4.0 where in this era the role of Artificial Intelligent is important. In this paper the author will review a part of the cloud-based architecture of E-Learning which will correspond with Education 4.0. The aim will be to produce a Cloud-Based E-learning system Architecture design that can be used as a guideline in the direction of Education 4.0.

scholarly journals Gamification of learning activities with the Odin service

2016 ◽  
Vol 13 (3) ◽  
pp. 809-826 ◽  
Author(s):  
José Paiva ◽  
José Leal ◽  
Ricardo Queirós
Keyword(s):  
Formative Assessment ◽  
System Architecture ◽  
Management System ◽  
Learning System ◽  
Learning Activities ◽  
Learning Tools ◽  
Hybrid Courses ◽  
Learning Game ◽  
Learning System Architecture ◽  
E Learning

Existing gamification services have features that preclude their use by e-learning tools. Odin is a gamification service that mimics the API of state-of-theart services without these limitations. This paper presents Odin as a gamification service for learning activities, describes its role in an e-learning system architecture requiring gamification, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) specification. Odin was also integrated in an e-learning tool that provides formative assessment in online and hybrid courses in an adaptive and engaging way.

scholarly journals Personalized E-Learning System Architecture Using Data Mining Approach

2018 ◽  
Author(s):  
Samina Kausar ◽  
Huahu Xu ◽  
Iftikhar Hussain ◽  
Wenhau Zhu ◽  
Misha Zahid
Keyword(s):  
Data Mining ◽  
System Architecture ◽  
Educational Data Mining ◽  
Learning System ◽  
Data Mining Techniques ◽  
Learning Capabilities ◽  
Learning System Architecture ◽  
E Learning ◽  
Hidden Patterns ◽  
Using Data

Educational data mining is an emerging discipline that focuses on development of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the field of education, the heterogeneous data is involved and continuously growing in the paradigm of big data. To extract meaningful knowledge adaptively from big educational data, some specific data mining techniques are needed. This paper presents a personalized e-learning system architecture which detects and responds teaching contents according to the students’ learning capabilities. Furthermore, the clustering approach is also presented to partition the students into different groups based on their learning behavior. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities to boost up their outcomes. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The various clustering methods K-means, Clustering by Fast Search and Finding of Density Peaks (CFSFDP), and CFSFDP via Heat Diffusion (CFSFDP-HD) are also analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of K-means with CFSFDP and CFSFDP-HD. The proposed e-learning system using data mining techniques is vigorous compared to typical e-learning systems. The data mining techniques are equally effective to analyze the big data to make education systems robust.

The Learning Style-Based Adaptive Learning System Architecture

2016 ◽  
pp. 608-618
Author(s):  
Chyun-Chyi Chen ◽  
Po-Sheng Chiu ◽  
Yueh-Min Huang
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
System Architecture ◽  
Learning System ◽  
Learning Materials ◽  
Learning Path ◽  
Different Types ◽  
Learning System Architecture ◽  
Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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