TY - GEN
T1 - Online team-based game development discussions patterns summarised using probabilistic models
AU - Teranishi, Akiko
AU - Nakayama, Minoru
AU - Wyeld, Theodor
AU - Mohamad, Eid A.
N1 - Publisher Copyright:
© 2018 ACM.
PY - 2018/4/9
Y1 - 2018/4/9
N2 - Communications between team members sent as messages about collaboration strategies during online game development were analysed to extract contributions to the discussion of their online activity and their reflections. The task was the collaborative development of online games by a team of university students. Their social media communications were classified into four functions: Proposal, Permission, Encouragement, and Acknowledgement based on the participant's frequency of contribution of reflections and the frequency of their communication activity. In the results, some significant relationships between participant's reflections and communication frequencies of the four categories were extracted in the later discussion cycles. Also, the appearance probabilities of communication activity in each of the four categories were calculated using Bayesian networks and the scores of participant's characteristics, such as personality and information literacy.
AB - Communications between team members sent as messages about collaboration strategies during online game development were analysed to extract contributions to the discussion of their online activity and their reflections. The task was the collaborative development of online games by a team of university students. Their social media communications were classified into four functions: Proposal, Permission, Encouragement, and Acknowledgement based on the participant's frequency of contribution of reflections and the frequency of their communication activity. In the results, some significant relationships between participant's reflections and communication frequencies of the four categories were extracted in the later discussion cycles. Also, the appearance probabilities of communication activity in each of the four categories were calculated using Bayesian networks and the scores of participant's characteristics, such as personality and information literacy.
KW - Computer based learning
KW - Discourse analysis
KW - Self assessment
KW - Transitional analysis
UR - http://www.scopus.com/inward/record.url?scp=85050569442&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050569442&partnerID=8YFLogxK
U2 - 10.1145/3167132.3167169
DO - 10.1145/3167132.3167169
M3 - Conference contribution
AN - SCOPUS:85050569442
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 234
EP - 239
BT - Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
PB - Association for Computing Machinery
T2 - 33rd Annual ACM Symposium on Applied Computing, SAC 2018
Y2 - 9 April 2018 through 13 April 2018
ER -