Graph-based domain model for adaptive learning path recommendation

2017 ◽  
Author(s):  
Muhammad Fiqri ◽  
Dade Nurjanah
Keyword(s):  
Adaptive Learning ◽  
Domain Model ◽  
Learning Path

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 Adaptive Learning Model Based on Ant Colony Algorithm

2019 ◽  
Vol 14 (01) ◽  
pp. 49 ◽  
Author(s):  
Rongxia Li
Keyword(s):  
Adaptive Learning ◽  
Ant Colony Algorithm ◽  
Learning Model ◽  
Learning Objects ◽  
Ant Colony ◽  
Learning Path ◽  
Knowledge Levels ◽  
Actual Learning ◽  
Learning Frameworks ◽  
Adaptive Online Learning

To better respond to people’s demands for multimedia learning, appropriate learn-ing paths should be offered based on their actual learning demands and different knowledge levels. Adaptive online learning model integrates and improves exist-ing learning frameworks to offer a set of knowledge paths that can cater to dif-fer7ent preferences, tastes, and knowledge levels of learners, no need for them to be aware of this. Based on the improved ant colony algorithm, an adaptive learn-ing system model that can satisfy learners’ demands is built herein with reference to the foraging approach of ants to traverse the paths, thereby to find the best learning path, while the classification method for some learning objects can de-termine the search parameters. This innovative approach proposed hereof can help improve learners' academic performance and learning efficiency.

MOOCs – A Challenge or an Opportunity

2015 ◽  
pp. 1-21 ◽  
Author(s):  
Keld Hvam
Keyword(s):  
Learning Outcomes ◽  
Adaptive Learning ◽  
Teaching And Learning ◽  
Individual Learning ◽  
Learning Goals ◽  
Individual Student ◽  
Personal Learning ◽  
Learning Path ◽  
Set Up ◽  
The Individual

This article discusses whether universities should create some MOOCs themselves or use the existing ones constructively in their teaching – thus meeting the potential challenges head-on and turning these into opportunities. After presenting various definitions the writer goes on to discuss whether MOOCs are a challenge or an opportunity for universities. The answer to this question depends on the strategies adopted by the individual university. Therefore, a strategy for embedding MOOCs in current courses is presented. It seems that we are moving towards teaching and learning in networks rather than following the centuries-old linear thinking. Lecturers will agree with their students what their individual learning goals are – so there will be much more focus on goals or aims (learning outcomes), and a system will then be set up in which the individual student is motivated to seek and identify his or her own personal learning path towards that goal – also called adaptive learning. And MOOCs can be an integral part of this.

An Adaptive Learning Path Builder based on a Context Aware Recommender System

2021 ◽  
Author(s):  
Marianna Carbone ◽  
Francesco Colace ◽  
Marco Lombardi ◽  
Francesco Marongiu ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Recommender System ◽  
Adaptive Learning ◽  
Context Aware ◽  
Learning Path

scholarly journals Automated Adaptive Mobile Learning System using Shortest Path Algorithm and Learning Style

2018 ◽  
Vol 12 (5) ◽  
pp. 4 ◽  
Author(s):  
Ibrahim Alkore Alshalabi ◽  
Samir E Hamada ◽  
Khaled Elleithy ◽  
Ioana Badara ◽  
Saeid Moslehpour
Keyword(s):  
Learning Style ◽  
Directed Graph ◽  
Adaptive Learning ◽  
Domain Knowledge ◽  
Learning System ◽  
Learning Goals ◽  
Educational Systems ◽  
Weighted Directed Graph ◽  
Path Graph ◽  
Learning Path

<p class="Abstract"><span>A directed graph represents an accurate picture of course descriptions for online courses through computer-based implementation of various educational systems. E-learning and m-learning systems are modeled as a weighted, directed graph where each node represents a course unit. The Learning Path Graph (LPG) represents and describes the structure of domain knowledge, including the learning goals, and all other available learning paths. In this paper, we propose a system prototype that implements a propose adaptive learning path algorithms that uses the student’s information from their profile and their learning style in order to improve the students’ learning performances through an m-learning system that provides a suitable course content sequence in a personalized manner.</span></p>

Sign in / Sign up

Export Citation Format

Share Document