Courseware Adaptation to Learning Styles and Knowledge Level

Learning is a fundamental element of people’s everyday lives. Learning experiences can take the form of our interactions with others, through attending an educational establishment, etc. Not everyone learns in the same way, and even people who are considered to have a similar standard of abilities or proficiency will exhibit different learning styles. This does not necessarily mean that some students are better than others; it means that students are different from one another. Adaptive e-learning system should be capable of adapting the content to the user learning style, abilities and knowledge level. In this paper, we investigate the benefits of incorporating learning styles and dyslexia type in adaptive e-learning systems. Adaptivity aspects based on dyslexia type and learning styles enrich each other, enabling systems to provide learners with materials which fit their needs more accurately. Besides, consideration of learning styles and dyslexia type can contribute to more accurate student modelling. In this paper, the relationship between learning styles, the Felder–Silverman learning style model (FSLSM), and dyslexia type, is investigated. These relationships will lead to a more reliable student model.

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Comparison of E-Learning Personalization Systems: Protus and PLeMSys

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v12i01.6085 ◽

2017 ◽

Vol 12 (01) ◽

pp. 57 ◽

Cited By ~ 1

Author(s):

Natasha Blazheska-Tabakovska ◽

Mirjana Ivanovic ◽

Aleksandra Klasnja-Milicevic ◽

Jovana Ivkovic

Keyword(s):

Learning Styles ◽

Cognitive Abilities ◽

Personalized Learning ◽

Knowledge Level ◽

Collaborative Tagging ◽

Tutoring System ◽

E Learning ◽

Knowledge Levels ◽

Individual Needs ◽

Contemporary Education

E-learning is becoming more and more important in contemporary education. It allows learners to learn at their own pace, when their schedule permits it. However, learners have individual needs and disparate traits such as learning styles, knowledge levels, motivation and cognitive abilities. So, a need for personalized learning has been made clear. Two ways of personalized learning are discussed in this paper: the first is Protus 2.1. - a tutoring system that allows personalization based on learning styles and collaborative tagging and the second one is PLeMSys - a model of a Moodle plug-in where personalization is based on learning styles and knowledge level.

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ADAPTIVE ASSESSMENT BASED ON LEARNING STYLES AND STUDENT KNOWLEDGE LEVEL

Proceedings of the 3rd International Conference on Computer Supported Education ◽

10.5220/0003484104490454 ◽

2011 ◽

Keyword(s):

Learning Styles ◽

Knowledge Level ◽

Student Knowledge ◽

Adaptive Assessment

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Mining the Students’ Chat Conversations in a Personalized e-Learning Environment

International Journal of Emerging Technologies in Learning (iJET) ◽

10.3991/ijet.v14i23.11031 ◽

2019 ◽

Vol 14 (23) ◽

pp. 98

Author(s):

Amal Alabri ◽

Zuhoor Al-Khanjari ◽

Yassine Jamoussi ◽

Naoufel Kraiem

Keyword(s):

Learning Environment ◽

Learning Styles ◽

Learning Style ◽

Domain Model ◽

Semantic Relations ◽

Knowledge Level ◽

Social Collaboration ◽

Mining Technique ◽

E Learning ◽

Mining Model

Providing personalized e-learning environment is normally relying on a domain model representing the knowledge to be acquired by learners and learners’ characteristics to be used in the personalization process. Therefore, constructing the domain model and understanding the characteristics of the learners are very crucial in such an environment. With the inclusion of social collaboration tools for collaborative learning activities, the generated data during conversations enrich with valuable information to be used for personalization. However, when considering chat conversations as a source for constructing the domain model, there is a need to perform a mining technique for chat conversations in order to extract the semantic relations from the user-generated contents hidden inside these conversations. As well as the learner’s characteristics like learning style and knowledge level expressed during conversations. Thus in this paper, we are aiming for the best utilization of chat conversation by proposing a model containing a rule-based technique as a form of mining technique. This mining aims at extracting the semantic relations to build the domain model as an ontology-based depiction. In addition, the mining model is proposed to perform some collaborative filtering techniques to identify the learning styles and knowledge level of the learners.

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Technical Feasibility of a Mobile Context-Aware (Social) Learning Schedule Framework

International Journal of Distance Education Technologies ◽

10.4018/jdet.2013010105 ◽

2013 ◽

Vol 11 (1) ◽

pp. 58-73 ◽

Cited By ~ 1

Author(s):

Jane Y. K. Yau ◽

Mike Joy

Keyword(s):

Social Learning ◽

Learning Styles ◽

Mobile Device ◽

Learning Activities ◽

Context Aware ◽

Knowledge Level ◽

Technical Feasibility ◽

Software Application ◽

Functional Prototype ◽

Mobile Context

The purpose of this paper is to show the technical feasibility of implementing their mobile context-aware learning schedule (mCALS) framework as a software application on a mobile device using current technologies, prior to its actual implementation. This process draws a set of compatible mobile and context-aware technologies at present and can be used as a reference point for implementing generic mobile context-aware applications. The authors’ mCALS framework retrieves the learner’s location and available time contexts via the built-in learning schedule (i.e., electronic organizer) on a mobile device. These contexts together with the learner’s learning styles and knowledge level (on a selected topic) are used as the basis for the software application to suggest learning materials that are appropriate for the learner, at the time of usage. This retrieval approach eliminates the use of context-aware technologies and the need to directly request the user to enter context information at the time of usage. The authors develop a fully functional prototype of this framework for learners to plan their individual as well as social learning activities amongst one another to make their individual learning processes collaborative and as a way to enhance individual and social learning experiences.

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Student Anticipations and Reactions to Hybrid Format Education

Perspectives on Issues in Higher Education ◽

10.1044/ihe11.2.76 ◽

2008 ◽

Vol 11 (2) ◽

pp. 76-82 ◽

Cited By ~ 4

Author(s):

Sarah M. Ginsberg

Keyword(s):

Qualitative Study ◽

Learning Styles ◽

Student Perceptions ◽

Course Design ◽

Learning Experience ◽

Learning Experiences ◽

Traditional Classroom ◽

Traditional Learning ◽

Online Platform ◽

Hybrid Format

Abstract This qualitative study examined student perceptions regarding a hybrid classroom format in which part of their learning took place in a traditional classroom and part of their learning occurred in an online platform. Pre-course and post-course anonymous essays suggest that students may be open to learning in this context; however, they have specific concerns as well. Students raised issues regarding faculty communication patterns, learning styles, and the value of clear connections between online and traditional learning experiences. Student concerns and feedback need to be addressed through the course design and by the instructor in order for them to have a positive learning experience in a hybrid format course.

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