Individual Learning and Emotional Characteristics in Web-based Communities of Practice

2010 ◽  
pp. 113-127
Author(s):  
Nikos Tsianos ◽  
Zacharias Lekkas ◽  
Panagiotis Germanakos ◽  
Constantinos Mourlas
Keyword(s):  
Learning Styles ◽  
Communities Of Practice ◽  
Learning Environments ◽  
Learning Style ◽  
Individual Characteristics ◽  
Web Based ◽  
Shared Learning ◽  
Processing Styles ◽  
Theoretical Assumptions

The knowledge management paradigm of communities of practice can be efficiently realized in Web-based environments, especially if one considers the extended social networks that have proliferated within the Internet. In terms of increasing performance through the exchange of knowledge and shared learning, individual characteristics, such as learners’ preferences that relate to group working, may be of high importance. These preferences have been summarized in cognitive and learning styles typologies, as well as emotional characteristics which define implications that could serve as personalization guidelines for designing collaborative learning environments. This chapter discusses the theoretical assumptions of two distinct families of learning style models, cognitive personality and information processing styles (according to Curry’s onion model), and the role of affection and emotion, in order to explore the possibilities of personalization at the group level of CoP.

Using Web-Based Technologies and Communities of Practice for Transformative Hybrid and Distance Education

2010 ◽  
pp. 156-166
Author(s):  
Nory Jones ◽  
Omar J. Khan
Keyword(s):  
Social Networks ◽  
Distance Education ◽  
Learning Styles ◽  
Communities Of Practice ◽  
Web 2.0 ◽  
Learning Environments ◽  
Learning Experience ◽  
Online Classes ◽  
Web Based

This chapter explores the use of Web-based technologies incorporating communities of practice and social networks to enhance the learning experience in hybrid and distance (online) classes. Research suggests that using a variety of technologies and methods to reach people with different learning styles improves overall learning in a class delivery though different methods are more effective in traditional vs. online classes. Moreover, using new, emerging Web-based technologies, including both Web 1.0 and Web 2.0 software, further enhances the engagement and value of the learning experience in these classes. This chapter examines the methods and technologies that can be potentially used to create excellence learning environments in traditional hybrid and online classes.

EXPRESS: Testing Theories of Goal Progress in Online Learning

2021 ◽  
pp. 002224372199110
Author(s):  
Joy Lu ◽  
Eric T. Bradlow ◽  
J. Wesley Hutchinson
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Detection System ◽  
Traditional Education ◽  
Performance Outcomes ◽  
Learner Engagement ◽  
Goal Progress ◽  
Business Courses ◽  
Using Data

Online educational platforms increasingly allow learners to consume content at their own pace with on-demand formats, in contrast to the synchronous content of traditional education. Thus, it is important to understand and model learner engagement within these environments. Using data from four business courses hosted on Coursera, we model learner behavior as a two-stage decision process, with the first stage determining across-day continuation versus quitting and the second stage determining within-day choices among lectures, quizzes, and breaks. By modeling the heterogeneity across learners pursuing lecture and quiz completion goals, we capture different patterns of consumption that correspond to extant theories of goal progress within an empirical field setting. We find that most individuals exhibit a learning style where lecture utility changes as an inverted-U-shaped function of current progress. Our model may also be used as an early detection system to anticipate changes in engagement and allows us to relate learning styles to final performance outcomes and enrollment in additional courses. Finally, we examine the role of quizzes in how consumption patterns vary across learners in different courses and between those who have paid or not paid for the option to earn a course certificate.

scholarly journals PENGARUH MOTIVASI BELAJAR, GAYA BELAJAR, DAN LINGKUNGAN BELAJAR PADA HASIL BELAJAR EKONOMI DI SMA NEGERI 1 WRINGINANOM GRESIK

2017 ◽  
Vol 2 (2) ◽  
pp. 142
Author(s):  
Muhammad Miftah Farid
Keyword(s):  
Linear Regression ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Outcomes ◽  
Learning Environments ◽  
Learning Style ◽  
Total Sample ◽  
Learning Motivation ◽  
Analysis Method ◽  
Regression Techniques

This study aims to analyze the influence of learning motivation, learning styles and learning environments on learning outcomes economy partially or simultaneously. The population in this study is IPS students a total of 314 people and a total sample of 153 students in SMA Negeri 1 Wringinanom Gresik. The data collection techniques used were questionnaires and documentation. Analysis method using multiple linear regression techniques. The results showed that the learning motivation, learning style and learning environment partially positive significant effect on learning outcomes. Learning motivation, learning style and the learning environment simultaneously positive significant effect on learning outcomes

Evolving Learning in the Stuff Swamp

2003 ◽  
pp. 211-228 ◽  
Author(s):  
Jon Dron ◽  
Chris Boyne ◽  
Richard Mitchell
Keyword(s):  
Learning Environments ◽  
Cold Start ◽  
Learning Resources ◽  
Future Directions ◽  
Web Based ◽  
Natural Systems ◽  
Self Organisation ◽  
Emergent Structure ◽  
Cold Start Problem

This chapter describes the theory, background and some uses of CoFIND (Collaborative Filter in N Dimensions), a Web-based database of learning resources which is created by and for learners. CoFIND is designed to exploit principles of evolution and self-organisation to create an emergent structure to learning resources. Through the manipulation of learner-supplied metadata such as classifications and ratings, this structure shapes itself to the needs of the learners who create it, providing something akin to guidance traditionally supplied by a teacher. The chapter starts with a discussion of the weaknesses of existing means for groups of learners to discover learning resources including search engines, directories, seals of approval, and collaborative filters. It considers a range of methods by which self-organisation is achieved in natural systems (notably evolution and stigmergy) and which underpin the CoFIND system. CoFIND is described and examples are given of some of its uses. The authors discuss some issues which arise, especially its cold-start problem, influences of surrounding systems and the role of motivation. The chapter concludes with a discussion of potential future directions for CoFIND and identifies some other aspects of learning environments which may benefit from such a self-organising system.

Designing Web-Based Educational Virtual Reality Environments

2010 ◽  
pp. 157-178 ◽  
Author(s):  
Kosmas Dimitropoulos ◽  
Athanasios Manitsaris
Keyword(s):  
Distance Education ◽  
Virtual Reality ◽  
Learning Environments ◽  
World Wide ◽  
Virtual Learning Environments ◽  
Virtual Reality Technology ◽  
Web Based ◽  
Learning Content ◽  
The Web

This chapter aims to study the benefits that arise from the use of virtual reality technology and World Wide Web in the field of distance education, as well as to further explore the role of instructors and learners in such a network-centric mode of education. Within this framework, special emphasis is given on the design and development of web-based virtual learning environments so as to successfully fulfil their educational objectives. In particular, the chapter includes research on distance education on the Web and the role of virtual reality, as well as study on basic pedagogical methods focusing mainly on the efficient preparation, approach and presentation of the learning content. Moreover, specific designing rules are presented considering the hypermedia, virtual and educational nature of this kind of applications. Finally, an innovative virtual reality environment for distance education in medicine, which reproduces conditions of the real learning process and enhances learning through a real-time interactive simulator, is demonstrated.

Adult Learner Characteristics Important for Successful Learning in Cross-Cultural Web-Based Learning Environments in Higher Education

2021 ◽  
pp. 436-469
Author(s):  
Joseph George M. Lutta
Keyword(s):  
Higher Education ◽  
Social Learning ◽  
Learning Environments ◽  
Teaching And Learning ◽  
Individual Characteristics ◽  
Cross Cultural ◽  
Future Research ◽  
Web Based ◽  
Research And Practice ◽  
Web Based Learning

For more than 40 years, cognitive psychological perspectives have dominated pedagogical frameworks and models for designing technology-mediated teaching and learning environments. Social learning perspectives are increasingly becoming viable or even desirable frames for research and practice as pertains to teaching and learning, particularly in web-based learning environments (WBLEs). The author considers these social learning perspectives and how they relate to the design and implementation of curricula that are delivered in web-based learning environments in higher education. The author further reviews the foundational theories of adult learning that enhance adult learners' experiences in cross-cultural web-based learning environments. This review and analysis of the research related to social learning perspectives on WBLEs have three implications for future research and practice: (1) examining learners' individual characteristics in WBLEs, (2) identifying strategies for promoting social interaction in WBLEs, and (3) developing effective design principles for WBLEs. The author presents recommendations for future research.

The Role of Learning Styles and Technology

2009 ◽  
Vol 4 (4) ◽  
pp. 50-65 ◽  
Author(s):  
Royce Ann Collins
Keyword(s):  
Adult Learners ◽  
Learning Environment ◽  
Learning Styles ◽  
Learning Style ◽  
Virtual Space ◽  
Classroom Teaching ◽  
Initial Learning ◽  
Education Today ◽  
Electronic Environments

Learning style research has informed effective classroom teaching strategies for decades. Technology has allowed faculty and students to move the learning environment from the four-walled classroom to a fluid global virtual space. Knowledge gained through the application of learning style research to online instruction has enhanced practice; however, research demonstrating the alignment of learning styles with current technological resources has been limited. Learning styles and their interrelationship with technology and adult learners is as important today as initial learning style research was in the six decades after its beginnings in the 1940s. Education today must meet the needs of students who are more comfortable in electronic environments, as well as those who need the four-walled classroom. The ability to use learning style research to accomplish both will lead to enhanced student learning and a more productive experience.

Knowledge Mining for Adaptive Multimedia Web-Based Educational Platform

2008 ◽  
pp. 205-257 ◽  
Author(s):  
Leyla Zhuhadar ◽  
Olfa Nasraoui ◽  
Robert Wyatt
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Intelligent System ◽  
Learning Material ◽  
Online Information ◽  
Learning Sequence ◽  
Online Students ◽  
User Models ◽  
Web Based ◽  
The Web

This chapter introduces an Adaptive Web-Based Educational platform that maximizes the usefulness of the online information that online students retrieve from the Web. It shows in a data driven format that information has to be personalized and adapted to the needs of individual students; therefore, educational materials need to be tailored to fit these needs: learning styles, prior knowledge of individual students, and recommendations. This approach offers several techniques to present the learning material for different types of learners and for different learning styles. User models (user profiles) are created using a combination of clustering techniques and association rules mining. These models represent the learning technique, learning style, and learning sequence, which can help improve the learning experience on the Web site for new users. Furthermore, the user models can be used to create an intelligent system that provides recommendations for future online students whose profile matches one of the mined profiles that represents the discovered user models.

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