A Model for an Adaptive Hypermedia Learning System Based on Data Mining Technique

2017 ◽  
pp. 65-85
Author(s):  
Mahnane Lamia ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Learning Performance ◽  
Adaptive Hypermedia ◽  
Data Mining Technique ◽  
Pedagogical Strategies ◽  
Hypermedia Learning

Since the learning style of each learner is different. Adaptive hypermedia learning system (AHLS) must fit different learner's needs. A number of AHLS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: less attention was paid to the relationship between learning styles and learning performance. This paper proposes an AHLS model based on learning styles and learning performance. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her learning style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

A Model for an Adaptive e-Learning Hypermedia System

2013 ◽  
Vol 9 (4) ◽  
pp. 21-39
Author(s):  
Lamia Mahnane ◽  
Laskri Mohamed Tayeb ◽  
Philippe Trigano
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Personal Characteristics ◽  
Learning Objects ◽  
Thinking Styles ◽  
Pedagogical Strategies ◽  
Learning Content ◽  
Hypermedia System ◽  
E Learning ◽  
Specific Teaching

Recent years have shown increasing awareness for the importance of adaptivity in e-learning. Since the learning style of each learner is different. Adaptive e-learning hypermedia system (AEHS) must fit different learner’s needs. A number of AEHS have been developed to support learning styles as a source for adaptation. However, these systems suffer from several problems, namely: lack of maintenance, adaptation to learning style, less attention was paid to thinking styles and the insertion of specific teaching strategies into learning content. This paper proposes an AEHS model based on thinking styles and knowledge level. On one hand, the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her thinking style and level of knowledge). On the other hand, it will facilitate the learning content teacher in the creation of appropriate learning objects and their application to suitable pedagogical strategies.

Gender Differences and Hypermedia Navigation

2011 ◽  
pp. 278-292
Author(s):  
Jing Ping Fan ◽  
Robert D. Macredie
Keyword(s):  
Gender Differences ◽  
Learning Style ◽  
Empirical Studies ◽  
Experimental Studies ◽  
Individual Characteristics ◽  
Learning System ◽  
Adaptive Hypermedia ◽  
Adaptive Presentation ◽  
Hypermedia Learning ◽  
Navigation Support

Adaptive hypermedia learning systems can be developed to adapt to a diversity of individual differences. Many studies have been conducted to design systems to adapt to learners’ individual characteristics, such as learning style and cognitive style to facilitate student learning. However, no research has been done specifically regarding the adaptation of hypermedia learning system to gender differences. This chapter therefore attempts to fill this gap by examining the published findings from experimental studies of interaction between gender differences and hypermedia learning. Analysis of findings of the empirical studies leads to a set of principles being proposed to guide adaptive hypermedia learning system design onthe basis of gender differences in relation to (i) adaptive presentation and (ii) adaptive navigation support.

Gender Differences and Hypermedia Navigation

2006 ◽  
pp. 1-20 ◽  
Author(s):  
Jing P. Fan ◽  
Robert D. Macredie
Keyword(s):  
Gender Differences ◽  
Learning Style ◽  
Empirical Studies ◽  
Experimental Studies ◽  
Individual Characteristics ◽  
Learning System ◽  
Adaptive Hypermedia ◽  
Adaptive Presentation ◽  
Hypermedia Learning ◽  
Navigation Support

Adaptive hypermedia learning systems can be developed to adapt to a diversity of individual differences. Many studies have been conducted to design systems to adapt to learners’ individual characteristics, such as learning style and cognitive style to facilitate student learning. However, no research has been done specifically regarding the adaptation of hypermedia learning system to gender differences. This chapter therefore attempts to fill this gap by examining the published findings from experimental studies of interaction between gender differences and hypermedia learning. Analysis of findings of the empirical studies leads to a set of principles being proposed to guide adaptive hypermedia learning system design onthe basis of gender differences in relation to (i) adaptive presentation and (ii) adaptive navigation support.

Learning Object Retrieval and Aggregation Based on Learning Styles

2016 ◽  
Vol 55 (6) ◽  
pp. 757-788
Author(s):  
Aldo Ramirez-Arellano ◽  
Juan Bory-Reyes ◽  
Luis Manuel Hernández-Simón
Keyword(s):  
Learning Styles ◽  
Student Learning ◽  
Learning Style ◽  
Learning Objects ◽  
Experimental Results ◽  
Learning Object ◽  
Learning Performance ◽  
Second Step ◽  
Object Retrieval ◽  
Merging Process

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students’ learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students’ learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.

An Intelligent and Adaptive Hypermedia System Based on Thinking Style (IAHS-TS)

2016 ◽  
pp. 111-130
Author(s):  
Mahnane M. L. Lamia ◽  
Hafidi Mohamed
Keyword(s):  
Personal Characteristics ◽  
Learning Objects ◽  
Learning System ◽  
Thinking Style ◽  
Brain Dominance ◽  
Pedagogical Strategies ◽  
Learning Content ◽  
Hypermedia System ◽  
E Learning ◽  
Level Of Knowledge

In this paper, an adaptive and intelligent hypermedia system, AIHS was designed, developed and implemented. This e-learning system was intended for bachelor degree program that is offered in all Algerian public universities, where the studied subjects are: “ORL”, “Dermatology”, “Ophthalmology” and “Language”. Content which was transformed into learning objects in four different ways in accordance with Herrmann Brain Dominance Instrument (HBDI). the developed prototype will assist a learner in accessing and using learning resources which are adapted according to his/her personal characteristics (in this case his/her thinking style and level of knowledge). It will facilitate the learning content teacher in the creation of appropriate learning objects and applying them to suitable pedagogical strategies.

Gender Differences and Hypermedia Navigation

2008 ◽  
pp. 1778-1792
Author(s):  
J. P. Fan
Keyword(s):  
Gender Differences ◽  
Learning Style ◽  
Empirical Studies ◽  
Experimental Studies ◽  
Individual Characteristics ◽  
Learning System ◽  
Adaptive Hypermedia ◽  
Adaptive Presentation ◽  
Hypermedia Learning ◽  
Navigation Support

Adaptive hypermedia learning systems can be developed to adapt to a diversity of individual differences. Many studies have been conducted to design systems to adapt to learners’ individual characteristics, such as learning style and cognitive style to facilitate student learning. However, no research has been done specifically regarding the adaptation of hypermedia learning system to gender differences. This chapter therefore attempts to fill this gap by examining the published findings from experimental studies of interaction between gender differences and hypermedia learning. Analysis of findings of the empirical studies leads to a set of principles being proposed to guide adaptive hypermedia learning system design onthe basis of gender differences in relation to (i) adaptive presentation and (ii) adaptive navigation support.

scholarly journals Adaptyvios programavimo mokymosi sistemos modelis

2013 ◽  
Vol 66 ◽  
pp. 108-122
Author(s):  
Jūratė Urbonienė
Keyword(s):  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Style ◽  
Learning Difficulties ◽  
Learning Objects ◽  
Learning System ◽  
System Model ◽  
Brain Dominance ◽  
Adaptive Programming ◽  
Selection Of

Straipsnyje nagrinėjami programavimo mokymo ypatumai, apžvelgiama mokymosi stilių įvairovė bei mokymosi stiliaus įtaka mokymosi rezultatams. Plačiau aptariamas Herrmanno smegenų dominavimo instrumentas1 (HBDI), kuris leidžia besimokančiuosius suskirstyti pagal jų smegenų pusrutulio išsivystymą. Remiantis programavimo mokymo patirtimi ir fundamentaliais programavimo mokymo srities mokslininkų darbais, nagrinėjamos programavimo mokymosi sunkumų priežastys (programavimo srities specifiškumas, mokymosi būdų ir mokymosi metodų parinkimas), išskiriamos ir apibendrinamos esminės idėjos. Pagrindinis dėmesys skiriamas adaptyvios programavimo mokymosi sistemos (APMS) koncepciniam modeliui pristatyti. Modeliuojama APMS leis priderinti mokymosi objektus, išrenkant juos iš mokymosi objektų saugyklų, ir užtikrinti atitinkamų kompetencijų pasiekimo lygį atsižvelgiant į besimokančiojo mokymosi stilių pagal Herrmanno skirstymą bei ankstesnę jo mokymosi patirtį.Pagrindiniai žodžiai: programavimo mokymasis, adaptyvi mokymosi sistema, Herrmanno mokymosi stiliai.Model of an adaptive programming learning systemJūratė Urbonienė SummaryThe paper analyzes the characteristics of programming training, reviews students’ learning styles and learning style influence on learning outcomes, the Herrmann brain dominance Instrument (HBDI), which enables to classify learners according to their brain hemisphere development. Based on the programming learning experiences and fundamental programming training, scientists’ works, the programming learning difficulties (sych as the programming field specificity, selection of learning styles and learning methods selection) are analyzed, and the key ideas are identified and summarized. The main focus is on adaptive programming learning system. The proposed adaptive programming learning system model will improve students’ motivation and the level of achieved results according to the pre-defined rules. The motivation will be improved by selecting a student’s learning style and most appropriate learning objects. It is planned to create the system of introducing this model and testing it with real learners.  

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.

scholarly journals A Proposed Model for Detecting Learning Styles Based on Agent Learning

2016 ◽  
Vol 11 (10) ◽  
pp. 65 ◽  
Author(s):  
M S Hasibuan ◽  
L E Nugroho ◽  
P I Santosa ◽  
S S Kusumawardani
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Evaluation Model ◽  
Learning System ◽  
Learning Activities ◽  
Instructional Materials ◽  
Data Driven ◽  
Automatic Learning ◽  
Proposed Model ◽  
Agent Learning

A learning style is an issue related to learners. In one way or the other, learning style could assist learners in their learning activities if students ignore their learning styles, it may influence their effort in understanding teaching materials. To overcome these problems, a model for reliable automatic learning style detection is needed. Currently, there are two approaches in detecting learning styles: data driven and literature based. Learners, especially those with changing learning styles, have difficulties in adopting these two approach since they are not adaptive, dynamic and responsive (ADR). To solve the above problems, a model using agent learning approach is proposes. Agent learning involves performing activities in four phases, i.e. initialization, learning, matching and, recommendations to decide the learning styles the students use. The proposed system will provide instructional materials that match the learning style that has been detected. The automatics detection process is performed by combining the data-driven and literature-based approaches. We propose an evaluation model agent learning system to ensure the model is working properly.

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