Start Date
14-12-2012 12:00 AM
Description
This study explores how student learning via asynchronous, threaded discussion boards may be managed successful. We examine the elements of course scaffolding on that affect student learning and engagement in discussion. We explore the role of the instructor in mediating learning. We base our findings on an analysis of 21 online courses in the IS domain, conducted by multiple instructors over a period of eight years. Our findings indicate that three aspects of course scaffolding impact learning outcomes: question structure, question focus, and the design of supporting materials. We also deconstruct the myth of the entertaining professor , concluding that, while students are more satisfied with courses where the professor is deemed to be entertaining – and thus more motivated to learn - this form of course mediation may actually impede deep learning.
Recommended Citation
Waters, Jim and Gasson, Susan, "Using Asynchronous Discussion Boards To Teach IS: Reflections From Practice" (2012). ICIS 2012 Proceedings. 9.
https://aisel.aisnet.org/icis2012/proceedings/ISCurriculum/9
Using Asynchronous Discussion Boards To Teach IS: Reflections From Practice
This study explores how student learning via asynchronous, threaded discussion boards may be managed successful. We examine the elements of course scaffolding on that affect student learning and engagement in discussion. We explore the role of the instructor in mediating learning. We base our findings on an analysis of 21 online courses in the IS domain, conducted by multiple instructors over a period of eight years. Our findings indicate that three aspects of course scaffolding impact learning outcomes: question structure, question focus, and the design of supporting materials. We also deconstruct the myth of the entertaining professor , concluding that, while students are more satisfied with courses where the professor is deemed to be entertaining – and thus more motivated to learn - this form of course mediation may actually impede deep learning.