An investigation of learners' collaborative knowledge construction performances and behavior patterns in an augmented reality simulation system

The purpose of this study was to investigate how a mobile collaborative augmented reality (AR) simulation system affects learners' knowledge construction behaviors and learning performances. In this study, 40 undergraduate students were recruited and divided into dyads to discuss a given task either with the assistance of a mobile collaborative AR system or traditional 2D simulation system. The participants' knowledge acquisition regarding elastic collision was evaluated through a pre-test and a post-test comparison. Learners' knowledge construction behaviors were qualitatively identified according to an adapted three-category coding scheme including construction of problem space (PS), construction of conceptual space (CS), and construction of relations between conceptual and problem space (CPS), and were then analyzed by adopting lag sequential analysis. The results indicated that the learners who learned with the AR system showed significant better learning achievements than those who learned with the traditional 2D simulation system. Furthermore, the sequential patterns of the learners' behaviors were identified, including three sustained loops (PS->PS, CS->CS, CPS->CPS), a bi-directional path between the PS and CPS activities (PS@?CPS), and a one way path from the PS activity to the CS activity (PS->CS). The revealed behavior patterns suggest that the AR Physics system may serve as a supportive tool and enable dyad learners to respond quickly to the displayed results and support their knowledge construction processes to produce a positive outcome. Based on the behavioral patterns found in this study, suggestions for future studies and further modifications to the system are proposed.

[1]  Ronald Azuma,et al.  A Survey of Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.

[2]  Randy L. Bell,et al.  The use of a computer simulation to promote conceptual change: A quasi-experimental study , 2010, Comput. Educ..

[3]  T. Bryce,et al.  Momentum and Kinetic Energy: Confusable Concepts in Secondary School Physics. , 2009 .

[4]  Ronald Azuma,et al.  Special Section on Mobile Augmented Reality , 2011, Comput. Graph..

[5]  Nai Li,et al.  Sociality of Mobile Collaborative AR: Augmenting a Dual-Problem Space for Social Interaction in Collaborative Social Learning , 2011, 2011 IEEE 11th International Conference on Advanced Learning Technologies.

[6]  Daniel D. Suthers,et al.  An Experimental Study of the Effects of Representational Guidance on Collaborative Learning Processes , 2003 .

[7]  Huei-Tse Hou,et al.  Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG) , 2012, Comput. Educ..

[8]  Asif Iqbal,et al.  A collaborative augmented reality based modeling environment for construction engineering and management education , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[9]  Kurt Squire,et al.  Augmented Reality Simulations on Handheld Computers , 2007 .

[10]  Chien Chou,et al.  Ubiquitous knowledge construction: mobile learning re‐defined and a conceptual framework , 2009 .

[11]  F. Fischer,et al.  Fostering collaborative knowledge construction with visualization tools , 2002 .

[12]  Eric Klopfer,et al.  Augmented Learning: Research and Design of Mobile Educational Games , 2008 .

[13]  Nikol Rummel,et al.  A rating scheme for assessing the quality of computer-supported collaboration processes , 2007, Int. J. Comput. Support. Collab. Learn..

[14]  M. Chi Quantifying Qualitative Analyses of Verbal Data: A Practical Guide , 1997 .

[15]  Liam Rourke,et al.  Validity in quantitative content analysis , 2004 .

[16]  David Rosengrant,et al.  Multiple-choice test of energy and momentum concepts , 2003, 1602.06497.

[17]  Cindy E. Hmelo-Silver,et al.  Analyzing collaborative knowledge construction: multiple methods for integrated understanding , 2003, Comput. Educ..

[18]  F. Fischer,et al.  A framework to analyze argumentative knowledge construction in computer-supported collaborative learning , 2006, Comput. Educ..

[19]  Chih-Ming Chen,et al.  Interactive augmented reality system for enhancing library instruction in elementary schools , 2013, Comput. Educ..

[20]  Jill A. Marshall,et al.  Preservice teachers' theory development in physical and simulated environments , 2006 .

[21]  Larry Johnson,et al.  The 2011 Horizon Report. , 2011 .

[22]  Chin-Chung Tsai,et al.  Affordances of Augmented Reality in Science Learning: Suggestions for Future Research , 2012, Journal of Science Education and Technology.

[23]  Chiung-Hui Chiu,et al.  Group differences in computer supported collaborative learning: Evidence from patterns of Taiwanese students' online communication , 2010, Comput. Educ..

[24]  Jan van Aalst,et al.  Distinguishing knowledge-sharing, knowledge-construction, and knowledge-creation discourses , 2009, Int. J. Comput. Support. Collab. Learn..

[25]  Jan T. van der Veen,et al.  The learning effects of computer simulations in science education , 2012, Comput. Educ..

[26]  Paul A. Kirschner,et al.  Design and effects of representational scripting on group performance , 2010 .

[27]  Roger Bakeman,et al.  Observer agreement for timed-event sequential data: A comparison of time-based and event-based algorithms , 2009, Behavior research methods.

[28]  S. K. Ingeç,et al.  Analysing Concept Maps as an Assessment Tool in Teaching Physics and Comparison with the Achievement Tests , 2009 .

[29]  Elizabeth Anne George,et al.  Learning Energy, Momentum, and Conservation Concepts with Computer Support in an Undergraduate Physics Laboratory , 2000 .

[30]  Ernesto Damiani,et al.  Augmented reality technologies, systems and applications , 2010, Multimedia Tools and Applications.

[31]  P. Milgram,et al.  A Taxonomy of Mixed Reality Visual Displays , 1994 .

[32]  Caroline Mei Lin Ho,et al.  Fostering argumentative knowledge construction through enactive role play in Second Life , 2009, Comput. Educ..

[33]  Manuel Castro,et al.  New technology trends in education: Seven years of forecasts and convergence , 2011, Comput. Educ..

[34]  Roger Bakeman,et al.  Observing Interaction: An Introduction to Sequential Analysis , 1986 .

[35]  Jyh-Chong Liang,et al.  Current status, opportunities and challenges of augmented reality in education , 2013, Comput. Educ..

[36]  Heinz Mandl,et al.  Supporting learning using external representations , 2008, Comput. Educ..

[37]  George Papagiannakis,et al.  A survey of mobile and wireless technologies for augmented reality systems , 2008 .

[38]  M. Lombard,et al.  Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability , 2002 .

[39]  Kurt Squire,et al.  Environmental Detectives—the development of an augmented reality platform for environmental simulations , 2008 .

[40]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[41]  John Berry,et al.  A hierarchical model of the development of student understanding of momentum , 1996 .

[42]  Hong Gao,et al.  Designer Support for Online Collaboration and Knowledge Construction , 2005, J. Educ. Technol. Soc..