Construction and evaluation of an online environment to reduce off-topic messaging

ABSTRACT

Online discussions have become more common as social network services have become more ubiquitous and complement various learning activities. However, studies investigating online discussions in recent years have shown that off-topic messaging has increased with the use of social network services. Thus, determining the design of a mechanism to reduce the frequency of off-topic messaging is an issue deserving attention. This study develops a Facebook-based system and employs two strategies (a filter reminder strategy and a self-reflection strategy) aiming to reduce off-topic messaging in comparative and empirical studies. The research questions are as follows: (a) Which strategy is more effective in reducing off-topic messaging? (b) What are the influences of the strategies on the patterns of students’ cognitive processes? and (c) Does this influence occur during discussions? The results indicate that the filter reminder strategy can not only reduce off-topic messaging but also elicit more diversified cognitive behaviors. Finally, based on the findings, this study provides suggestions for future research and advice regarding instruction.

KEYWORDS:

• Online discussion
• off-topic
• Facebook
• cognitive process
• lag sequential analysis

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by Ministry of Science and Technology [grant number MOST-104-2511-S-153-005-MY2].

Notes on contributors

Sheng-Yi Wu

Sheng-Yi Wu received the M.S. degrees in Graduate Institute of Information and Computer Education from National Kaohsiung Normal University, Taiwan, and Ph.D. degree in Graduate Institute of Network Learning Technology from National Central University, Taiwan. He is currently an associate professor in the Department of Science Communication, National Pingtung University, Taiwan. His current research interests focus on online discussion, computer-supported collaborative learning, computational thinking and physiological signals.