A Project Teams Creation Based on Communities Detection; CEUR Workshop Proceedings; Vol. 1710 : AIST’2016 — Analysis of Images, Social Networks and Texts

التفاصيل البيبلوغرافية
Parent link:CEUR Workshop Proceedings: Online Proceedings for Scientific Conferences and Workshops
Vol. 1710 : AIST’2016 — Analysis of Images, Social Networks and Texts.— 2016.— [P. 303-314]
مؤلف مشترك: Национальный исследовательский Томский политехнический университет (ТПУ) Физико-технический институт (ФТИ) Кафедра высшей математики и математической физики (ВММФ)
مؤلفون آخرون: Semenov M. E. Mikhail Evgenievich, Bulygin L. E. Lev Eduardovich, Koroleva E. A. Elena Aleksandrovna, Tursunov D. A. Dilmurat Abdillazhanovich
الملخص:Title screen
The purpose of this study is to detect project teams in a group. A key point in considering group’s relationships is the reciprocal influence, whereby group’s members influence each other. There was conducted a survey based on reciprocal nomination method, and then a social network was constructed. Participants were first-year bachelor students of Tomsk Polytechnic University. Various social network analysis algorithms were used to cluster network in communities. The results of analysis were discussed with the teachers and students, and then detected community teams were adjusted within the key actors of group. The results of the study may be used to create project teams, which can make successful collective actions in educational projects
اللغة:الإنجليزية
منشور في: 2016
الموضوعات:
الوصول للمادة أونلاين:http://ceur-ws.org/Vol-1710/paper30.pdf
التنسيق: الكتروني فصل الكتاب
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654859

MARC

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330 |a The purpose of this study is to detect project teams in a group. A key point in considering group’s relationships is the reciprocal influence, whereby group’s members influence each other. There was conducted a survey based on reciprocal nomination method, and then a social network was constructed. Participants were first-year bachelor students of Tomsk Polytechnic University. Various social network analysis algorithms were used to cluster network in communities. The results of analysis were discussed with the teachers and students, and then detected community teams were adjusted within the key actors of group. The results of the study may be used to create project teams, which can make successful collective actions in educational projects 
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