An Automatic Approach for Identifying Triple - Factor in e - Learning Process (original) (raw)

An Automatic Approach for Detecting Dynamic Learning Style For E- Learning System

There are many learning management systems (LMSs) around us. Of them WebCT, Blackboard, and Moodle are widely and successfully used when e-education is concerned. They help teachers in creating and conducting online courses though the individual differences of learners are not considered. The learners possess different characteristics and requirements like motivation, learning styles, prior knowledge and cognitive abilities. Some of the characteristics such as learning styles, their effect on learning abilities can be supported by some of the recent learning systems. It makes learning easier according to some educational theories. Considering these facts, we focus on dynamically changed learning styles of students to provide them with a better learning material. This system will hopefully enhance the learning ability and quality of learners. Finally we propose to find differences between learning style detected by ILS questionnaires and detected those dynamically.

An Approach to Detect Learning Types Based on Triple-Factor In e-Learning Process

2014

In previous studies, it was revealed that the importance of learning styles, motivation, and knowledge ability factors are facilitated diverse learners in e-learning. However, the overall of factors are not yet fully accommodated in e-learning process. Meanwhile, the results of preliminary study of this research indicated that the existence of these factors as the inherent structure that reflect the relationship among learning styles and motivation to the knowledge ability (Triple-Factor) in elearning process. In order to accommodate the existence of inherent structure, this study explores the learning types based on triple-factor in e-learning process. The approach can be used as the basis for the learning recommendation and personalization in e-learning process. The approach consists of three steps: (1) identifying activity and evaluation of learning outcome, (2) forming Triple-Factor, and (3) detecting learning types based on Triple-Factor. The approach use 36 types of learning which consists of: 18 learning types that has the ability of knowledge good or very good, irrespective of the learning styles and motivation; and 18 learning types that has the ability of knowledge fail or sufficient, irrespective of the learning styles and motivation. Furthermore, the experiment for testing steps was carried by dividing two stages: the first stage did not implement the approach, second stage using learning recommendation and personalization. Result of the testing of these two stages are: step of " identifying activity and evaluation of learning outcome " at the second stage, showed that there was a significant increase on the activity of learning (0.007<0.05), and discussion forums (0.006<0.05), meanwhile the evaluation of learning outcomes (0.227>0.05) did not increase significantly. Step of "forming Triple-Factor", and " detecting learning type base on Triple-Factor" at the second stage showed: learning styles and motivation with the knowledge ability of good and very good increased from 57 to 69 students. In contrast learning styles and motivation with the knowledge ability of fail and sufficient decreased from 61 to 49. The results show that the approach used in the study successfully improve the learning process and its outcomes through learning recommendation and personalization.

Behavioral Feature Extraction to Determine Learning Styles in e-Learning Environments

International Association for Development of the Information Society, 2015

Learning Style (LS) is an important parameter in the learning process. Therefore, learning styles should be considered in the design, development, and implementation of e-learning environments. Consequently, an important capability of an e-learning system could be the automatic determination of a student's learning style. In this paper, a set of features which are important in extracting the learning style automatically from students' behavior has been determined. These features, which are recognized based on Myers-Briggs Type Indicator's (MBTI), play a key role in predicting learning styles in an online course. The features are determined and ranked using pattern recognition techniques, such as K-means clustering algorithm, to show which features can be better to separate learning style dimensions. The results show several features can be used to predict learning styles with high precision.

Enhanced Detection of Learners Learning Styles for E-Learning

2015

Learning Style is: “A particular way in which an individual learns”. Different kinds of learners are distinguished according to their learning styles based on the explicit characteristics shown by the learners, during the earlier period. The latent nature of the learners in addition to the explicit nature, addressed by most of the traditional learning style models also influences the learning style of an individual and such identification could provide better E-Learning framework in terms of content delivery. This paper categorizes new kind of learners: “Intelligent Learners” who are identified by two varying dimensions: Uncovering the latent attitude (Browsing History in an E-Learning server) in them and testing of linguistic intelligence and are trained using a neural-network algorithm. The paper also provides a brief summary of the different categories of learning styles available in the past. The experimental results shown are compared with other models and are found to be promi...

E-Learning personalization based on Dynamic learners' preference

International Journal of Computer Science and Information Technology, 2011

Personalized e-learning implementation is recognized one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different we must to fit elearning to the different needs of learners. This paper discusses teaching strategies matching with learner's personality using the Myers-Briggs Type Indicator (MBTI) tools. Based on an innovative approach, a framework for building an adaptive learning management system by considering learner's preference has been developed. The learner's profile is initialized according to the results obtained by the student in the index of learning styles questionnaire and then fine-tuned during the course of the interaction using the Bayesian model. Moreover, an experiment was conducted to evaluate the performance of our approach. The result reveals the system effectiveness for which it appears that the proposed approach may be promising.

An Adaptive E-Learning System based on Student's Learning Styles

International Journal of Distance Education Technologies, 2016

Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a new Adaptive Educational Hypermedia System based on Honey and Mumford learning style model (AEHS-H&M) and assess the effect of adapting educational materials individualized to the student's learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student's ...

Integrating 'Learning style' Information into Personalized e- learning System

E-Learning environments are based on a range of delivery and collaborative services. Introducing personalized recommender system in e-Learning environments can support learning recommendations to students. The main aim for this paper is to try to increase the student's efficiency by integrating "Learning styles" information into e-learning environment to achieve the idea of personalization. Students with different goals and different backgrounds are thus treated differently by building a model of knowledge and preferences for each one to form a user modeling structure.

Learning Content Personalization Based on Triple-Factor Learning Type Approach in E-learning

2014 International Conference on Advanced Computer Science and Information System, 2014

One of the emerging issue in e- learning is to create adaptive learning based on learner’s perspective. Adaptive learning can be realized through personalization of e-learning. Personalized learning help learners to use their best performance in order to reach learning goals based on their needs, preferences, and characteristics. To accomodate different characteristics of the learners, learning content personalization system based on triple-factor learning type was developed. The characteristics of 36 triple-factor learning type were used as input for learning content personalization algorithm to produce learning content that suitable for the learners’s learning type. The algorithm implemented into a system which called SCELE-Personalization Dynamic E-learning. The system was used by 118 learners in Science Writing course at the Faculty of Computer Science, Universitas Indonesia as experimental group. In order to find the best learning performance, the exam score from experiment group were compared with the exam score from control group. The result shows learning performance of experimental group that used personalized learning feature is better than learning performance of control group who used non-personalized learning feature. It can be seen from significant value (p<0,05) and the different mean score of the experimental group that reach 13,68.

E-Learning personalization based on hybrid recommendation strategy and learning style identification

Computers & Education, 2011

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a programming tutoring system -Protus, which can automatically adapt to the interests and knowledge levels of learners. This system recognizes different patterns of learning style and learners' habits through testing the learning styles of learners and mining their server logs. Firstly, it processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the learners through mining the frequent sequences by the AprioriAll algorithm. Finally, this system completes personalized recommendation of the learning content according to the ratings of these frequent sequences, provided by the Protus system. Some experiments were carried out with two real groups of learners: the experimental and the control group. Learners of the control group learned in a normal way and did not receive any recommendation or guidance through the course, while the students of the experimental group were required to use the Protus system. The results show suitability of using this recommendation model, in order to suggest online learning activities to learners based on their learning style, knowledge and preferences.

Intelligent techniques in personalization of learning in e-learning systems

2010

Abstract This chapter contains an overview of intelligent techniques that can be applied in different stages of e-learning systems to achieve personalization. It describes examples of their application to various e-learning platforms to create profiles of learners and to define learning path. The typical approach to obtain learner's profile is the usage one of the clustering methods, such as: the simple k-means, Self Organizing Map, hierarchical clustering or fuzzy clustering. Classification methods like: C4. 5 or C.