The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Online Synchronous vs. Asynchronous Software Training Through the Behavioral Modeling Approach: A Longitudinal Field Experiment
|
|
Author(s): Charlie C. Chen (Appalachian State University, USA)and R. S. Shaw (Tamkang University, Taiwan)
Copyright: 2008
Pages: 15
Source title:
Handbook of Distance Learning for Real-Time and Asynchronous Information Technology Education
Source Author(s)/Editor(s): Solomon Negash (Kennesaw State University, USA), Michael Whitman (Kennesaw State University, USA), Amy Woszczynski (Kennesaw State University, USA), Ken Hoganson (Kennesaw State University, USA)and Herbert Mattord (Kennesaw State University, USA)
DOI: 10.4018/978-1-59904-964-9.ch004
Purchase
|
Abstract
The continued and increasing use of online training raises the question of whether the most effective training methods applied in live instruction will carry over to different online environments in the long run. Behavior Modeling (BM) approach—teaching through demonstration—has been proven as the most effective approach in a face-to-face (F2F) environment. A quasi-experiment was conducted with 96 undergraduate students who were taking a Microsoft SQL Server 2000 course in a university in Taiwan. The BM approach was employed in three learning environments—F2F, online synchronous and online asynchronous classes. The results were compared to see which produced the best performance, as measured by knowledge near-transfer and knowledge far-transfer effectiveness. Overall satisfaction with training was also measured. The results of the experiment indicate that during a long duration of training no significant difference in learning outcomes could be detected across the three learning environments.
Related Content
|
Carolyne Nekesa Obonyo.
© 2024.
30 pages.
|
|
Darina M. Slattery.
© 2024.
19 pages.
|
|
Derrick Raphael Pacheco, Brittany Devies.
© 2024.
24 pages.
|
|
Yakkala B. V. L. Pratyusha, Bindi Varghese.
© 2024.
19 pages.
|
|
Daniel Otieno.
© 2024.
12 pages.
|
|
Youmei Liu.
© 2024.
27 pages.
|
|
Kathleen O'Brien.
© 2024.
36 pages.
|
|
|