What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction

2012 ◽  
Vol 58 (1) ◽  
pp. 338-349 ◽  
Author(s):  
Eugenia Y. Huang ◽  
Sheng Wei Lin ◽  
Travis K. Huang
Keyword(s):  
Learning Environment ◽  
Learning Style ◽  
Mixed Mode ◽  
Online Participation ◽  
E Learning

The Value of Pacing in Promoting Self-Directed Learning

2019 ◽  
pp. 1-22
Author(s):  
Itumeleng I. Setlhodi
Keyword(s):  
Adult Learners ◽  
Learning Environment ◽  
Learning Outcomes ◽  
Learning Style ◽  
Learning Experience ◽  
Core Values ◽  
Self Directed Learning ◽  
Learning Framework ◽  
E Learning ◽  
Directed Learning

The chapter presents significant considerations for pacing amid directing own learning in an open distance e-learning environment (ODeL), assuming principles to achieve learning outcomes through processes that support learning style/s in leading own learning. Looking at a variety of factors, the prototypes for self-directedness and elements for self-pacing are presented. A case of an ODeL institution was explored and interviews conducted (n=57) to examine self-directed learning contextual factors in relation to the speed at which learners assume leadership in achieving learning outcomes within an (ODeL) context and gaining independence towards enhancing learning experience. The outcomes reveal that learners gain independence through adopting suitable speed, adopting core values, collaborating, support provided, and will to improve their skills. Finally, a self-directed paced learning framework for adult learners is offered.

scholarly journals The Customized xLearning Environment Model

2017 ◽  
Vol 7 (4) ◽  
pp. 39-52 ◽  
Author(s):  
Anabela Mesquita ◽  
Fernando Moreira ◽  
Paula Peres
Keyword(s):  
Learning Environment ◽  
Labour Market ◽  
Learning Style ◽  
Communication Technologies ◽  
Learning Objects ◽  
Learning Theories ◽  
Information Communication ◽  
Environment Model ◽  
E Learning ◽  
The One

The evolution in Information Communication Technologies, the changes in the labour market requirements together with the needs and expectation of students who arrive in higher education institutions, are forcing education to adapt. Students do not learn all at the same pace nor have all the same learning style or habits. An ideal learning environment would consider these differences as well as students' background, needs and characteristics. It should take into consideration the latest developments in learning theories, communication, social networks and learning objects available in learning management systems. It should also bridge the gap between education and the labour market by connecting all the actors whether they are students, teachers, experts or potential employers. In this learning environment education is closer to the job market allowing all actors to play in this scenario. In this paper, we propose a model where all the learning and e-learning elements are present and where the student is the focus and the one who decides what should be included in this learning environment in order to create a Customized xLearning Environment.

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

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.

Towards Best Practices in Web-Based Learning and Teaching

2016 ◽  
pp. 317-332
Author(s):  
Mercy A. Iroaganachi
Keyword(s):  
Best Practices ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Learning Objects ◽  
Web Based ◽  
Learning And Teaching ◽  
Web Based Learning ◽  
E Learning ◽  
Level Of Knowledge

The chapter explored best practices in web-based learning and teaching with a view to discover trends and provide valuable information for all in the e-learning environment. It affirms that paradigms in Web-based education have shifted from teacher-centered to learner-centered but basically it remains synchronous or asynchronous. This requires Learning Objects (LOs) to be pedagogically efficient, designed to standard (Multimodal) with designers bearing in mind the varied population and learning styles. LOs are to be personalized thereby creating adaptive content based on learner's abilities, learning style, level of knowledge and preferences. It is recommended that educators have requisite background knowledge and competencies in technology such as hardware, software, and course management systems etcetera. Instructors, designers and all interested persons should consult a checklist of best practices, for assessing learning object repositories. More so, there is need to incorporate hands-on component into the e-learning environment. The chapter provides Indicators for best practices.

scholarly journals Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sayed S. Younes
Keyword(s):  
Learning Environment ◽  
Learning Style ◽  
Achievement Test ◽  
Digital Content ◽  
Creative Design ◽  
Global Learning ◽  
Significant Difference ◽  
E Learning ◽  
Practical Performance ◽  
Design Skills

This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduate students. The research tools included an achievement test and an observation checklist for rating the practical performance. Research results concluded that, regardless of learning styles, the proposed adaptive e-learning environment had a positive effect on developing both cognitive achievement and practical performance of digital content creative design skills. The results also indicated that there is a significant difference at the 0.01 level between the mean scores of the first experimental group’s students using the global learning style-based adaptive e-learning environment and the second experimental group’s students using the sequential learning style-based adaptive AI-enabled e-learning environment in the achievement test and observation checklist after measurement of digital content creative design skills in favor of the second experimental group’s students. The study provided a number of suggestions and recommendations for making the utmost use of various design layouts of adaptive AI-enabled e-learning environments in developing different cognitive and performance aspects of learning as well as taking full advantage of digital content creative design skills mastery in producing a plethora of advanced electronic educational applications in the foreseeable future.

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