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.

Preparing Online Learning Readiness with Learner-Content Interaction

2016 ◽  
pp. 216-243 ◽  
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.

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.

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.

The Value of Metacognition and Reflectivity in Computer-Based Learning Environments

2016 ◽  
pp. 105-136
Author(s):  
Sammy Elzarka ◽  
Valerie Beltran ◽  
Jessica C. Decker ◽  
Mark Matzaganian ◽  
Nancy T. Walker
Keyword(s):  
Learning Environments ◽  
Real World ◽  
Self Regulation ◽  
Future Research ◽  
Self Regulated Learning ◽  
Computer Based Learning ◽  
Real World Application ◽  
Regulated Learning ◽  
Computer Based

The purposes of this chapter are threefold: to explore the research on and relationships among metacognition, reflection, and self-regulated learning; to analyze students' experiences with metacognition, reflection, and self-regulated learning activities in computer-based learning (CBL) courses; and to provide strategies that can be used in a CBL environment to promote students' metacognition, reflection, and self-regulation. A review of underlying frameworks for and prior study findings in metacognition and reflection are presented. Case study findings are also described and form the basis for the suggested strategies. The value and implications of using such strategies are also offered. Finally, future research should address the teaching of metacognition and reflection in CBL environments with an emphasis on real world application.

scholarly journals The Online Learning Academic Achievement of Chinese Students during the COVID-19 Pandemic: the Role of Self-Regulated Learning and Academic Entitlement

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Yan DAI
Keyword(s):  
Academic Achievement ◽  
Online Learning ◽  
Online Courses ◽  
Chinese Higher Education ◽  
Equation Modeling ◽  
Academic Entitlement ◽  
Spring Semester ◽  
Chinese College Students ◽  
Self Regulated Learning ◽  
Regulated Learning

In the spring semester of 2020, all Chinese higher education institutions delivered courses online across the nation in response to the COVID-19. This study explores Chinese college students’ self-regulated learning, academic entitlement, and academic achievement during the transitioning from face-to-face to online learning environments during this special time. Structure equation modeling was conducted, and results indicate that academic entitlement associates with students’ online learning academic achievement. Whereas self-regulated learning does not relate to academic achievement in the online learning setting. Additionally, academic entitlement is marginally associated with academic achievement only among male students, while self-regulated learning is not a significant predictor for both genders. However, self-regulated learning is marginally linked with higher academic achievement among students who do not have previous online learning experiences but not among those who took online courses before.

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