Self-Regulated Learning: A Key of a Successful Learner in Online Learning Environments in Thailand

2013 ◽  
Vol 48 (1) ◽  
pp. 45-69 ◽  
Author(s):  
Buncha Samruayruen ◽  
Judith Enriquez ◽  
Onjaree Natakuatoong ◽  
Kingkaew Samruayruen
Keyword(s):  
Online Learning ◽  
Learning Environments ◽  
Online Learning Environments ◽  
Self Regulated Learning ◽  
Regulated Learning

scholarly journals Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review

2018 ◽  
Vol 35 (4-5) ◽  
pp. 356-373 ◽  
Author(s):  
Jacqueline Wong ◽  
Martine Baars ◽  
Dan Davis ◽  
Tim Van Der Zee ◽  
Geert-Jan Houben ◽  
...  
Keyword(s):  
Systematic Review ◽  
Online Learning ◽  
Learning Environments ◽  
Online Learning Environments ◽  
Self Regulated Learning ◽  
Regulated Learning

A Conceptual Educational Data Mining Model for Supporting Self-Regulated Learning in Online Learning Environments

2021 ◽  
pp. 278-292
Author(s):  
Eric Araka ◽  
Robert Oboko ◽  
Elizaphan Maina ◽  
Rhoda K. Gitonga
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Learning Environments ◽  
Domain Knowledge ◽  
Educational Data Mining ◽  
Online Learning Environments ◽  
Student Activities ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
E Learning

Self-regulated learning is attracting tremendous researches from various communities such as information communication technology. Recent studies have greatly contributed to the domain knowledge that the use self-regulatory skills enhance academic performance. Despite these developments in SRL, our understanding on the tools and instruments to measure SRL in online learning environments is limited as the use of traditional tools developed for face-to-face classroom settings are still used to measure SRL on e-learning systems. Modern learning management systems (LMS) allow storage of datasets on student activities. Subsequently, it is now possible to use Educational Data Mining to extract learner patterns which can be used to support SRL. This chapter discusses the current tools for measuring and promoting SRL on e-learning platforms and a conceptual model grounded on educational data mining for implementation as a solution to promoting SRL strategies.

Strategies to Promote Self-Regulated Learning in Online Environments

2010 ◽  
pp. 122-144 ◽  
Author(s):  
Bruce R. Harris ◽  
Reinhard W. Lindner ◽  
Anthony A. Piña
Keyword(s):  
Online Learning ◽  
Learning Environments ◽  
Learning Strategies ◽  
Cognitive Processing ◽  
Online Courses ◽  
Self Regulation ◽  
Online Learning Environments ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Main Components

The primary purpose of this chapter is to present techniques and strategies that can be incorporated in online courses to promote students’ use of self-regulated learning strategies. In addition, the authors discuss why self-regulated learning skills are particularly critical in online learning environments, present a model of self-regulated learning, discuss issues related to measuring self-regulated learning, address the issue of whether or not self-regulated learning can be taught, and discuss why online learning environments are ideal environments to scaffold self-regulation. The authors present several strategies and techniques they have found successful for promoting self-regulated learning that can be readily incorporated and implemented in online courses. These strategies are organized by the three main components of the Self-Regulated Learning Model: Executive Processing, Cognitive Processing, and Motivation. The chapter concludes with a scenario that represents an idealized model of how to promote self-regulated learning in an online learning environment by employing an intelligent tutoring component as a tool to support students’ use and development of self-regulated learning tactics and strategies.

Preparing Online Learning Readiness with Learner-Content Interaction

2016 ◽  
pp. 586-614
Author(s):  
Juhong Christie Liu ◽  
Elaine Roberts Kaye
Keyword(s):  
Online Learning ◽  
Learning Environments ◽  
Online Courses ◽  
Learning Readiness ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Content Interaction ◽  
Online Learning Readiness ◽  
Synchronous Online

Online learning readiness is fundamental to student successful participation, presence, and interaction in online courses. Effective facilitation of these key components depends on sound instructional design. In self-directed online environments, learner-content interaction and scaffolding self-regulated learning have been found of primary importance to generate meaningful learning. To provide a solution to the challenges of interoperability of various functions in synchronous online learning environments, this chapter presents a case study about the design and development of a self-paced orientation to help students acquire online learning readiness. Learner-content interaction is strategically utilized in the design to scaffold self-regulated learning. The results of the case study demonstrate that this orientation positively prepares students to be ready for learning in a synchronous online environment. The approach can be of practical use to individuals and groups.

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