Investigation on the Relationships among Students’ E󰠏learning Readiness, Teaching Presence and Learning Effects in an Online Learning Environment

2015 ◽  
Vol 21 (4) ◽  
pp. 687-710
Author(s):  
Se-Ryon Kim ◽  
Eunkyung Moon ◽  
Innwoo Park
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Teaching Presence ◽  
Online Learning Environment ◽  
Learning Effects ◽  
Learning Readiness ◽  
E Learning

Social Software and E-Learning

2007 ◽  
pp. 228-247
Author(s):  
Jon Dron
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Web 2.0 ◽  
Learning Environments ◽  
Social Software ◽  
Learning Trajectory ◽  
Online Learning Environment ◽  
Networked Learning ◽  
E Learning

This book offers an exploration of the ways that a learning trajectory is determined, and, in particular, how an online learning environment can affect that trajectory. It provides suggestions about how, primarily through technologies that underlie what is vulgarly known as “Web 2.0,” networked learning environments should be constructed to give control to learners if they need it, as they need it, and when they need it.

scholarly journals Development, validation and use of the Online Learning Environment Survey

2005 ◽  
Vol 21 (1) ◽  
Author(s):  
Sue Trinidad ◽  
Jill Aldridge ◽  
Barry Fraser
Keyword(s):  
Online Learning ◽  
Hong Kong ◽  
Learning Environment ◽  
Learning Environments ◽  
Quantitative Data ◽  
Reliability And Validity ◽  
Female Students ◽  
Online Learning Environment ◽  
Male And Female ◽  
E Learning

<span>This article reports the development, validation and use of a survey for assessing students' perceptions of their e-learning environments. The Online Learning Environment Survey (OLES) was administered to 325 students, 131 in Australia and 194 in Hong Kong. The data were analysed to examine 1) the reliability and validity of the survey, 2) differences between the perceptions of a) students' actual and preferred environment, b) students and their teacher and c) male and female students and 3) whether associations exist between students' perceptions of their e-learning environment and their enjoyment of e-learning. In addition to quantitative data, unstructured interviews were used to provide a more in depth understanding of the e-learning environments created. These data provide valuable feedback to educators working in e-learning environments to help teachers to evaluate the effectiveness of the environment and to make adjustments and improvements as required.</span>

scholarly journals Relationship between learning outcomes and online accesses

2004 ◽  
Vol 20 (3) ◽  
Author(s):  
Pannee Suanpang ◽  
Peter Petocz ◽  
Anna Reid
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Learning Outcomes ◽  
Learning System ◽  
Clear Relationship ◽  
Online Learning Environment ◽  
Online Study ◽  
Business Statistics ◽  
E Learning ◽  
The Relationship

<span>This paper reports on a study carried out in Thailand investigating the relationship between students' use of an e-learning system and their learning outcomes in a course on Business Statistics. The results show a clear relationship between accesses to the e-learning system, as measured by number of "hits", and outcomes, as measured by final results. While the results do not establish a direct casual connection, they indicate that under appropriate conditions a component of online study provides significant benefits to learning. In this, it contrasts with the results of recent studies that find no relationship between access and results. Quotes taken from interviews with some of the students illuminate the relationship between the online learning environment and their own learning.</span>

Potential Futures of E-Learning

2007 ◽  
pp. 310-328
Author(s):  
Jon Dron
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Web 2.0 ◽  
Learning Environments ◽  
Learning Trajectory ◽  
Online Learning Environment ◽  
Networked Learning ◽  
E Learning

This book offers an exploration of the ways that a learning trajectory is determined, and, in particular, how an online learning environment can affect that trajectory. It provides suggestions about how, primarily through technologies that underlie what is vulgarly known as “Web 2.0,” networked learning environments should be constructed to give control to learners if they need it, as they need it, and when they need it.

Problems with Social Software for E-Learning

2007 ◽  
pp. 294-309
Author(s):  
Jon Dron
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Web 2.0 ◽  
Learning Environments ◽  
Social Software ◽  
Learning Trajectory ◽  
Online Learning Environment ◽  
Networked Learning ◽  
E Learning

This book offers an exploration of the ways that a learning trajectory is determined, and, in particular, how an online learning environment can affect that trajectory. It provides suggestions about how, primarily through technologies that underlie what is vulgarly known as “Web 2.0,” networked learning environments should be constructed to give control to learners if they need it, as they need it, and when they need it.

Design Principles for Social Software in E-Learning

2007 ◽  
pp. 248-270
Author(s):  
Jon Dron
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Web 2.0 ◽  
Learning Environments ◽  
Design Principles ◽  
Social Software ◽  
Learning Trajectory ◽  
Online Learning Environment ◽  
Networked Learning ◽  
E Learning

This book offers an exploration of the ways that a learning trajectory is determined, and, in particular, how an online learning environment can affect that trajectory. It provides suggestions about how, primarily through technologies that underlie what is vulgarly known as “Web 2.0,” networked learning environments should be constructed to give control to learners if they need it, as they need it, and when they need it.

Social Software in E-Learning

2007 ◽  
pp. 271-293
Author(s):  
Jon Dron
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Web 2.0 ◽  
Learning Environments ◽  
Social Software ◽  
Learning Trajectory ◽  
Online Learning Environment ◽  
Networked Learning ◽  
E Learning

This book offers an exploration of the ways that a learning trajectory is determined, and, in particular, how an online learning environment can affect that trajectory. It provides suggestions about how, primarily through technologies that underlie what is vulgarly known as “Web 2.0,” networked learning environments should be constructed to give control to learners if they need it, as they need it, and when they need it.

A Model for Improving Online Collaborative Learning through Machine Learning

2015 ◽  
pp. 204-219 ◽  
Author(s):  
E. Muuro Maina ◽  
Peter W. Wagacha ◽  
Robert O. Oboko
Keyword(s):  
Machine Learning ◽  
Online Learning ◽  
Collaborative Learning ◽  
Learning Environment ◽  
Online Learning Environment ◽  
Online Collaborative Learning ◽  
Collaborative Software ◽  
Online Tools ◽  
E Learning ◽  
Collaborative Learning Environment

Online collaborative learning provides new opportunities for student collaboration in an online learning environment and at the same time spawns new challenges for teachers supporting group work. With the current Course Management Systems (CMS) such as Moodle, technology has provided online tools that include discussions forums, chat rooms, e-mails, newsgroups, workshops, etc. These tools provide a collaborative learning environment. To include constructivist learning in an online learning environment is a good collaborative strategy that is necessary since it engages learners in learning activities through interaction with their peers and teacher. A good collaborative strategy in an e-learning environment must primarily ensure that the expected interaction occurs in line with the learning mechanism being employed. This cannot merely be met by offering a set of collaborative software tools alone. It also requires the instructors' support. As the number of students studying online continues to increase, there is need to develop models that can improve online collaborative learning with minimal involvement of the instructor because the instructor might not be able to cope with increased number of students. To address this need, this chapter discusses a novel model for improving online collaborative learning that uses Machine Learning (ML) techniques.

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