| Dr. Xiangen Hu is a professor in the Department of Psychology, Department of Electrical and Computer Engineering and Computer Science Department at The University of Memphis (UofM) and senior researcher at the Institute for Intelligent Systems (IIS) at the UofM and is professor and Dean of the School of Psychology at Central China Normal University (CCNU). Dr. Hu received his MS in applied mathematics from Huazhong University of Science and Technology, MA in social sciences and Ph.D. in Cognitive Sciences from the University of California, Irvine. Dr. Hu is the Director of Advanced Distributed Learning (ADL) Partnership Laboratory at the UofM, and is a senior researcher in the Chinese Ministry of Education’s Key Laboratory of Adolescent Cyberpsychology and Behavior. Dr. Hu's primary research areas include Mathematical Psychology, Research Design and Statistics, and Cognitive Psychology. More specific research interests include General Processing Tree (GPT) models, categorical data analysis, knowledge representation, computerized tutoring, and advanced distributed learning. Dr. Hu has received funding for the above research from the US National Science Foundation (NSF), US Institute of Education Sciences (IES), ADL of the US Department of Defense (DoD), US Army Medical Research Acquisition Activity (USAMRAA), US Army Research Laboratories (ARL), US Office of Naval Research (ONR), UofM, and CCNU. |
| Most often, a non-monitored/non-moderated group discussion would not reach the desired goal and hence waste of time of the participants. With the presence of a human monitor/moderator, discussions may be focused to achieve a desirable outcome. Can monitoring/moderating of discussions be automated (handled by intelligent monitoring/moderating systems)? This paper explores a possible answer to the question. Our possible solution is to create an automated intelligent monitoring/moderating systems (AIMS). We use an Individual Conversation Characteristics Curves (ICCC) to model each of the participants during a discussion. ICCC is a six-dimension array that is computed each time when any of the participants makes a contribution. The six dimensions of ICCC correspond to six components of the GCA (Group communication analysis). The components are Participation, Overall, Responsivity, Internal Cohesion, Social Impact, Newness, and Communication Density. ICCC is used as the core component of AIMS. We will introduce the computational details of the ICCC when discussion details are captured in the form of xAPI statements. The goal of this talk is to explore the utility of ICCC as a model of the participants during an interactive and synchronous discussion. With this model, automatically monitoring/regulating group behavior is possible. |
