Browse All

Theses & Dissertations

Submissions

  • Submissions (Articles, Chapters, and other finished products)

The Impact of Computer Augmented Online Learning and Assessment

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Anthony Shong-Yu Chow, Assistant Professor (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: The purpose of the study was to investigate the impact of an experimental online learning tool on student performance. By applying cognitive load theory to online learning, the experimental tool used was designed to minimize cognitive load during the instructional and learning process. This tool enabled students to work with programming code that was supplemented with instructor descriptions and feedback, embedded directly within the code while maintaining the original integrity of the coding environment. A sample of 24 online graduate students at a southeastern university were randomly assigned to four groups: Group 1 (Control group), Group 2 (Assessment group: the tool was used to provide feedback on student work), Group 3 (Lecture group: the tool was used to describe examples of code provided in lectures), and Group 4 (Total tool group: the tool was used to provide feedback on student work as well as describe examples of code in lectures). Student learning was measured via analysis of six online quizzes. While provision of tool-facilitated feedback alone did not appear to enhance student learning, the results indicate that students performed best when they had the opportunity to view examples of code facilitated by the tool during the learning process of new material. This implies a carefully designed online learning environment, especially while controlling for and minimizing cognitive load when presenting new information, can enhance that student learning.

Additional Information

Publication
Educational Technology & Society, 8(1), 113-125
Language: English
Date: 2005
Keywords
Online learning, Information technology education, Assessment, Personalized learning, Cognitive load theory