Roles are one of the most important concepts in understanding human sociocognitive behavior. During group interactions, members take on different roles within the discussion. Roles have distinct patterns of behavioral engagement (i.e., active or passive, leading or following), contribution characteristics (i.e., providing new information or echoing given material), and social orientation (i.e., individual or group). Different combinations of these roles can produce characteristically different group outcomes, being either less or more productive towards collective goals. In online collaborative learning environments, this can lead to better or worse learning outcomes for the individual participants. In this study, we propose and validate a novel approach for detecting emergent roles from the participants' contributions and patterns of interaction. Specifically, we developed a group communication analysis (GCA) by combining automated computational linguistic techniques with analyses of the sequential interactions of online group communication. The GCA was applied to three large collaborative interaction datasets (participant N = 2,429; group N = 3,598). Cluster analyses and linear mixed-effects modeling were used to assess the validity of the GCA approach and the influence of learner roles on student and group performance. The results indicate that participants' patterns in linguistic coordination and cohesion are representative of the roles that individuals play in collaborative discussions. More broadly, GCA provides a framework for researchers to explore the micro intra- and interpersonal patterns associated with the participants' roles and the sociocognitive processes related to successful collaboration.
LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation
Tsinghua University（清华大学）；University of California, Berkeley（加州大学伯克利分校）；Peking University（北京大学）；East China University of Political Science and Law（中国政法大学）
Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text
Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington（复杂网络与系统研究中心，信息学、计算与工程学院，印第安纳大学布卢明顿分校）；Cognitive Science Program, Indiana University Bloomington（认知科学项目，印第安纳大学布卢明顿分校）；School of Data Science, University of Virginia（数据科学学院，弗吉尼亚大学）；CulturePulse, Inc.（CulturePulse公司）
