Combining the Overlay Model and Bayesian Networks to Determine Learning Styles in AHES

Author(s):  
Mouenis Anouar Tadlaoui ◽  
Rommel Novaes Carvalho ◽  
Mohamed Khaldi

First of all, it is important to note that the work presented here lies within the modeling part of the learner in an adaptive educational system construed as computational modeling of the learner. Modeling the learner in adaptive systems involves different information such as knowledge of the domain, the performance of the learning goals, background, learning styles, etc. Although there are several methods to manage the learner model, like the stereotype model or learner profiles, they do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this work is to show the link between the structure of the learner model and especially the characteristics of a learning profile and the learning style of a learning situation. This chapter shows how the combination of these two approaches to learner modeling can address the dynamic aspect of the problem in the modeling of the learner.

This chapter aims to treat the problem of dynamic modeling in an adaptive educational system construed as computational modeling of the learner. Modeling the learner in adaptive systems involves different information such as knowledge of the domain, the performance of the learning goals, background, learning styles, etc. Although there are several methods to manage the learner model, like the stereotype model or learner profiles, they do not handle the uncertainty in the dynamic modeling of the learner. The main purpose of this chapter is to show the link between the structure of the learner model and the characteristics of a learning profile and the learning style of a learning situation. This chapter shows how the combination of these two approaches to learner modeling can address the dynamic aspect of the problem in the modeling of the learner. The experiments and results presented in this work are arguments in favor of the hypothesis and can also promote reusing the modeling obtained through different systems and similar modeling situations.


Author(s):  
Mouenis Anouar Tadlaoui ◽  
Rommel Novaes Carvalho ◽  
Mohamed Khaldi

Modeling the learner in adaptive systems involves different information. There are several methods to manage the learner model. They do not handle the uncertainty in the dynamic modeling of the learner. The main hypothesis of this chapter is the management of the learner model based on multi-entity Bayesian networks. This chapter focuses on modeling the learner model in a dynamic and probabilistic way. The authors propose in this work the use of the notion of fragments and m-theory to lead to a Bayesian multi-entity network. The use of this Bayesian method can handle the whole course of a learner as well as all of its shares in an adaptive educational hypermedia.


Author(s):  
Aymane Qodad ◽  
Abdelilah Benyoussef ◽  
Abdallah El Kenz ◽  
Mourad Elyadari

In this paper we introduce a new design of an adaptive educational hypermedia system for job seekers, this proposal is based, for the part of learning objectives, on a job model which allows adapting the content and the path of education to the intended jobs, and, for the learner model construction, on a specific use of the learning styles of Felder and Silverman. First, we present existing literature to give a general review on adaptive edu-cational hypermedia systems, in that way; we have reported the related items to different notions in the adaptive educational Systems area as the differentiated pedagogy, the learning objects, and the learner profile. Then we argued our choice of the components of our model and we detailed the new ones. As designed, the model can produce a suitable learning path for the user to match the job characteristics and the learning style of the person in order to help the user owning the job sought. With the possibility of linking the required com-petencies to the education skills, we aim to map business tasks to learning activi-ties. Based on this approach, we designed an Adaptive Educational Hypermedia System named AEHS-JS that will help to improve the efficiency and pragmatism of job search activities. In plus of the social impact of this work as it help job seekers to complete their profiles and get the career they are looking for, this work will allow companies to find the candidates that match the job criteria sought.


ScienceEdu ◽  
2019 ◽  
pp. 37
Author(s):  
Nia Savira Febrianti

This article is made based on the diversity of different learning styles and student interest in talent, especially participation in organizational activities. This article was made with the aim to find out the different learning styles for each student and the participation of organizational activities and their influence in gaining understanding and learning outcomes. The differences in learning styles and talent interests of each student are abilities in themselves. Teachers can also play a role in helping understanding learning in each individual who has different learning styles and interests. In this article it can be seen that organizational activities have positive and negative influences in learning and learning outcomes. The writing of this article uses the literature study method, by looking at various sources or references, such as books or several other journals concerning the same material as the article I made. With this article, it can be taken as a reference in learning. Thus, learning goals can be achieved without having to put pressure on students.   Kata Kunci: learning style, interest in talent, organizational activities, understanding of learning


2019 ◽  
Vol 1 (1) ◽  
pp. 34
Author(s):  
Saida Ulfa ◽  
Deddy Barnabas Lasfeto ◽  
Citra Kurniawan

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research.   Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits.Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model


2020 ◽  
Vol 20 (2) ◽  
pp. 143
Author(s):  
Safrul Muluk ◽  
Habiburrahim Habiburrahim ◽  
Siti Rechal Rechal

Learning style is one of the significant elements in learning process that helps students achieve their learning goals. Nevertheless, students should be aware of their own style in learning which make them maximally enhance learning. Therefore, in study, the researcher examined students’ awareness of learning styles and their perception of their learning styles. A quantitative descriptive research was used in this study. Then, the data was collected through online questionnaire distributed for 100 students of fifth semester of English Language Education Department by using random sampling technique. VARK (Visual, Auditory, Reading, and Kinestetic) questionnaire created by Fleming and Likert scales were used to explore the issue under investigation. Findings of the research showed that students had moderate to high awareness of their learning styles. Within the context of this study, this findings were new in a way that it showed students’ perception on learning styles and how it affected their learning experiences. the Moreover, research findings also indicated that students believes that  the learning styles they adopted helped them in achieving their learning goals supported by their learning environment. The result of the study indicated that students preferred visual learning styles.


2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Kristina Lisum ◽  
Sondang Ratnauli Sianturi

Identification and socialization about importance of nursing students’ learning style should be performed by nursing educator to achieve learning goals.  The purpose of this study is to explore nursing students’ perception of their learning style. Method of this study was qualitative with descritive interpretative design involving 10 nursing students, using purposive sampling. Students were divided into two groups, there were academic and profesion group continued by focus group discussion (FGD). The analysis data used  thematik analysis content with Collaizi method.  The results of this study  consists of five themes, namely : (1) easy and comfort ways to learn (2) variation of learning strategy at classroom and clinic   (3)  think, analyse about theory and use it at practice  (4) influence of passing the nurse national competency test   (5)  more profesional and contribute to nurses development. It was recommended to nurses educator at nursing school to identify sthrengthness of nursing student toward milenial learning style.  In order to improve learning outcome, nurse educator must combine variation  teaching strategy with nursing students’ learning style Keywords : learning, learning styles, nursing students, perception of nursing students.


Author(s):  
Vedamoni Ranjan

To develop in children a broad range of skills, including the problem solving, interpersonal and communication skills that are essential for successful living in a rapidly changing society. The curriculum encourages student initiative by providing children with materials, equipment, and time to pursue activities they choose. At the same time, it provides teachers with a framework for guiding children’s independent activities toward sequenced learning goals. There are seven specific types of learning styles. Visual learners prefer to learn mathematics through pictures, diagrams etc. A well-balanced intelligent child is able to develop all the types of learning styles. The students have to understand and accept their type of learning style earlier so that learning becomes easier and less stressful in the future. But it is important to train and practice the other types of learning styles so that the children can utilize them as effectively as possible. The teacher plays a key role in instructional activities by selecting appropriate, developmentally sequenced material and by encouraging children to adopt an active problem-solving approach to learning. This teacher-student interaction teachers helping students achieve developmentally sequenced goals while also encouraging them to set many of their own goals uniquely distinguishes the High/Scope Curriculum from direct-instruction and child-centered curricula (high/Scope Educational Research Foundation, 1989). Teachers keep notes about significant behaviors, changes, statements, and things that help them better understand a child’s way of thinking and learning. Teachers use two mechanisms to help them collect data: the key experiences note form and a portfolio. The High/Scope Child Observation Record is also used to assess children’s development. According to Ronald Barnett, learning may or may not take place when a subject is taught. While discussing this point he has presented two contrasting images of quality. They are institutional performance and student experience, student learning or student achievement. The teacher in his opinion is central to higher education. Teaching may be able to improve the quality of student’s learning but the teacher should remind himself that it may also impair the quality of student’s learning. This is partly because student’s learning strategies vary under two polarities, one between deep and surface understanding and the other between holistic and atomistic understanding of their learning experiences. He goes on to add that for a student, learning has three distinct aspects: learning style, motivation and curriculum demands. Therefore teachers have to pursue, beyond teaching strategies to enable their students to attain certain specific skills.


This chapter aims to propose a new way to initialize a learner model in adaptive educational hypermedia systems. Learner modelling in adaptive systems contains several indicators. Even if there are several methods for initializing the learner model, they do not manage the side of uncertainty in the dynamic modeling of the learner. The main purpose of this chapter is the initialization of the learner model based on the combination of the Bayesian networks and the stereotypes method. In order to carry out a complete initialization of this model, the authors propose to use a combination of the stereotype method to process the content of the specific domain of information and the Bayesian networks to process the contents of the independent domain of information. The experiments and results presented in this work are arguments in favor of the hypothesis and can promote also reusing the modeling obtained through different systems and similar modeling situations.


Author(s):  
Elvira Popescu

Individualizing the learning experience for each student is an important goal for educational systems and accurately modeling the learner is the first step towards attaining this goal. This chapter addresses learner modeling from the point of view of learning styles, an important factor for the efficiency and effectiveness of the learning process. A critical review of existing modeling methods is provided, outlining the specificities and limitations of current learning style based adaptive educational systems (LSAES). The controversy regarding the multitude of partially overlapping learning style models proposed in the literature is addressed, by suggesting the use of a complex of features, each with its own importance and influence (the so called Unified Learning Style Model). An implicit modeling method is introduced, based on analyzing students’ behavioral patterns. The approach is validated experimentally and good precision rates are reported.


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