Abstract
This paper focuses on modeling collaborative interaction in Ubiquitous Learning Environment (ULE) based on the assumption that the collaborative interaction can be perceived through interpersonal interactions, which can be described as local dynamic behaviors of the team. In this paper, the collaborative interaction is collected from the experiment with 50 students having 5 members per team. Then the collaborative interaction is coded with 16 participation shift (P-shifts) from 5 different types of turns including turn receiving, turn claiming, turn usurping, turn continuing, and turn noreturning to represent the participation status of each member. Three types of participation statuses used in this paper are the contributor, the target and the unaddressed recipient. Then the discovered local dynamic behavior is used for constructing the model by using agent-based modeling. The model consists of student agents working together according to the discovered behavior. Then, the constructed model is verified by comparing the actual behavior with the simulated behavior. Finally, the comparison result shows that the constructed model can reasonably be the model for modeling collaborative interaction in ULE.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Olivares OJ (2005) Collaborative critical thinking: conceptualizing and defining a new construct from known constructs. Issues Educ Res 15(1):86–100
Toth P (2010) Collaborative Learning in a VLE Based Common Module. Online Submission 7(1):85–95
Mandula K, Meda SR, Jain DK, Kambham R (2011) Implementation of ubiquitous learning system using sensor technologies. In: Technology for Education (T4E), 2011 IEEE International Conference on (pp 142–148). IEEE
Weiser M (1991) The computer for the 21st century. Sci Am 265(3):94–104
Martinez-Maldonado R, Dimitriadis Y, Clayphan A, Muñoz-Cristóbal JA, Prieto LP, Rodríguez-Triana MJ, Kay J (2013) Integrating orchestration of ubiquitous and pervasive learning environments. In: Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration (pp 189–192). ACM
Takaffoli M, Zaïane OR (2012) Social network analysis and mining to support the assessment of on-line student participation. ACM SIGKDD Explor Newsl 13(2):20–29
Gibson DR (2003) Participation shifts: order and differentiation in group conversation. Soc Forces 81(4):1335–1380
Li Y, Gao G, Chen Z, Huang R (2009) Research on New Generation e-Learning System for Ubiquitous Learning. In: Information Technology and Applications, 2009. IFITA’09. International Forum on (Vol. 2, pp 275–279). IEEE
Lee JR, Jung YJ, Park SR, Yu J, Jin DS, Cho K. (2012) A ubiquitous smart learning platform for the 21st smart learners in an advanced science and engineering education. In: Network-Based Information Systems (NBiS), 2012 15th International Conference on (pp 733–738). IEEE
Scott K, Benlamri R (2010) Context-aware services for smart learning spaces. Learn Technol IEEE Trans 3(3):214–227
Nino CP, Marques J, Barbosa DNF, Geyer CF, Barbosa JLV, Augustin I (2007) Context-aware model in a ubiquitous learning environment. In: Pervasive Computing and Communications Workshops, 2007. PerCom Workshops’ 07. Fifth Annual IEEE International Conference on (pp 182–186). IEEE
Temdee P (2014) Ubiquitous learning environment: smart learning platform with multi-agent architecture. Wirel Pers Commun 76(3):627–641
Koschmann TD, Myers AC, Feltovich PJ, Barrows HS (1994) Using technology to assist in realizing effective learning and instruction: a principled approach to the use of computers in collaborative learning. J Learn Sci 3(3):227–264
Olguín CJM, Delgado ALN, Ricarte ILM (2000) An agent infrastructure to set collaborative environments. J Educ Technol Soc 3(3):65–73
Nandi D, Hamilton M, Harland J, Warburton G (2011) How active are students in online discussion forums?. In: Proceedings of the Thirteenth Australasian Computing Education Conference-Volume 114 (pp 125–134). Australian Computer Society, Inc
Saltz JS, Hiltz SR, Turoff M (2004) Student social graphs: visualizing a student’s online social network. In: Proceedings of the 2004 ACM conference on Computer supported cooperative work (pp 596–599). ACM
L’huillier G, Ríos SA, Alvarez H, Aguilera F (2010) Topic-based social network analysis for virtual communities of interests in the dark web. In: ACM SIGKDD Workshop on Intelligence and Security Informatics (p 9). ACM
Temdee P, Pattison P Understanding Leadership Roles in Online Collaborative Learning Teams. In: Workshop Proceedings: Supplementary Proceedings of ICCE2008 (p 11)
Hattori H, Nakajima Y, Ishida T (2011) Learning from humans: agent modeling with individual human behaviors. Syst Man Cybern Part A Syst Hum IEEE Trans 41(1):1–9
Wang Q, Shen S, Wang F, Liang Y (2012) Research on battle agent model in the combat modeling. In: Electrical and Electronics Engineering (EEESYM), 2012 IEEE Symposium on (pp 86–89). IEEE
Corniglion S, Tournois PN (2011) Simulating tourists’ behaviour using multi-agent modeling. In Research Challenges in Information Science (RCIS), 2011 Fifth International Conference on (pp 1–9). IEEE
Acknowledgments
The work of this paper has the financial support from the Thailand Research Fund (Project Code: MRG5280240). The publication of this paper is supported by Mae Fah Luang University.
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Temdee, P. Agent-based modeling of collaborative interaction in ubiquitous learning environment using local dynamic behavior. Artif Life Robotics 21, 215–220 (2016). https://doi.org/10.1007/s10015-015-0256-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-015-0256-3