Dynamic Modeling as a Cognitive Regulation Scaffold for Developing Complex Problem-Solving Skills in an Educational Massively Multiplayer Online Game Environment
Restricted accessResearch articleFirst published online October, 2011
Dynamic Modeling as a Cognitive Regulation Scaffold for Developing Complex Problem-Solving Skills in an Educational Massively Multiplayer Online Game Environment
Following a design-based research framework, this article reports two empirical studies with an educational MMOG, called McLarin's Adventures, on facilitating 9th-grade students' complex problem-solving skill acquisition in interdisciplinary STEM education. The article discusses the nature of complex and ill-structured problem solving and, accordingly, how the game-based learning environment can facilitate complex problem-solving skill acquisition. The findings of the first study point to the importance of supporting cognitive regulation of students for successful complex problem-solving skill acquisition in digital game-based learning. The findings of the follow-up study show that when scaffolded by dynamic modeling, students made significant improvement in their complex problem-solving outcomes. Implications drawn from the findings of these two studies are discussed related to: (1) educational game design strategies to effectively facilitate complex problem-solving skill development; and (2) stealth or embedded assessment of progress in complex problem solving during digital game-based learning.
AzevedoR.GuthrieJ. T.SeibertD. (2004). The role of self-regulated learning in fostering students' conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30, 87–111.
3.
BarabS.TomasM.DodgeT.CarteauxR.TuzunH. (2005). Making learning fun: Quest Atlantis, a game without guns. Educational Technology Research & Development, 53(1), 86–107.
4.
ChiM. T. H.GlaserR. (1985). Problem solving ability. In SternbergR. J. (Ed.), Human abilities: An information processing approach (pp. 227–250). New York: W. H. Freeman and Company.
5.
Coalition of the Assessment & Teaching of 21st Century Skills. (2010). Assessment & teaching of 21st century skills: Status report as of January 2010. Retrieved December 22, 2010 from http://atc21s.org/default.aspx
EseryelD.GeX. (2010). Designing effective game-based learning environments: Implications for design research. Paper presented at “Educational Design Research: Local Change, Global Impact”: A special conference to honor Professor Thomas C. Reeves upon his retirement from the University of Georgia, March 26–27, 2010.
8.
EseryelD.SwearingenD. K. (2010). Student characteristics that affect ninth grade students' mathematics achievement in digital game-based learning. Paper presented at the annual meeting of the American Educational Research Association, Denver, CO, April 30-May 4, 2010.
9.
EseryelD.IfenthalerD.GeX. (2011). Alternative assessment strategies for game-based learning environments. In IfenthalerD.KinshukP.IsaiasD. G.SampsonD. G.SpectorJ. M. (Eds.), Multiple perspectives on problem solving and learning in the digital age (pp. 159–178). New York: Springer.
10.
EseryelD.IfenthalerD.LawV.GuoY. (2009). A validation study of a methodology for assessing progress of learning and complex, ill-structured problem solving in STEM. Proceedings of the IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA), Rome Italy.
11.
EseryelD.MillerR. B.GeX.IfenthalerD.LawV.GuoY. (2010). A longitudinal study on the impact of digital game-based learning on complex problem solving skill acquisition and student motivation. Paper presented at the 2010 annual convention of Association for Education Communications and Technology, Anaheim, CA, October 26–30, 2010.
12.
FunkeJ. (1985). Steuerung dynamischer Prozesse durch Aufbau und Anwendung subjektiver Kausalmodelle. Zeitschrift für Psychologie, 193(4), 443–465.
13.
FunkeJ. (1991). Solving complex problems: Exploration and control of complex problems. In SternbergR. J.FrenschP. A. (Eds.), Complex problem solving: Principles and mechanisms (pp. 185–222). Hillsdale, NJ: Lawrence Erlbaum Associates.
14.
GeeJ. P. (2003). What video games have to teach us about learning and literacy. New York: Palgrave-Macmillan.
15.
HannafinM.LandS.OliverK. (1999). Open learning environments: Foundations, methods, and models. In ReigeluthC. M. (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. 2, pp. 115–140). Mahwah, NJ: Lawrence Erlbaum Associates.
16.
IfenthalerD. (2008). Practical solutions for the diagnosis of progressing mental models. In IfenthalerD.Pirnay-DummerP.SpectorJ. M. (Eds.), Understanding models for learning and instruction. Essays in honor of Norbert M. Seel (pp. 43–61). New York: Springer.
17.
IfenthalerD. (2009). Model-based feedback for improving expertise and expert performance. Technology, Instruction, Cognition and Learning, 7(2), 83–101.
18.
IfenthalerD. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97.
19.
International Commission on Education for the 21st Century (1996). Learning: The treasure within (Tech Report no. 265). UNESCO.
20.
KitchnerK. S. (1983). Cognition, metacognition, and epistemic cognition: A three-level model of cognitive processing. Human Development, 26, 222–232.
21.
ManloveS.LazonderA. W.de JongT. (2006). Regulative support for collaborative scientific inquiry learning. Journal of Computer Assisted Learning, 22, 87–98.
22.
Partnership for 21st Century Skills. (2008). Results that matter: 21st century skills and higher school reform. Retrieved December 22, 2010 from http://www.p21.org/index.php
23.
Pirnay-DummerP.IfenthalerD. (2010). Automated knowledge visualization and assessment. In IfenthalerD.Pirnay-DummerP.SeelN. M. (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 77–115). New York: Springer.
24.
Pirnay-DummerP.IfenthalerD. (2011). Text-guided automated self assessment. A graph-based approach to help learners with ongoing writing. In IfenthalerD.KinshukP.IsaiasD.SampsonG.SpectorJ. M. (Eds.), Multiple perspectives on problem solving and learning in the digital age (pp. 217–225). New York: Springer.
25.
Pirnay-DummerP.IfenthalerD.SpectorJ. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1), 3–18.
26.
PrenskyM. (2001). Digital game-based learning. New York: McGraw-Hill.
27.
PrenskyM. (2006). Don't bother me mom—I'm learning. St. Paul, MN: Paragon House.
28.
ReitmanW. R. (1964). Heuristic decision procedures, open constraints, and the structure of ill-defined problems. In ShellyM. W.BryanG. L. (Eds.), Human judgments and optimality (pp. 282–315). New York: John Wiley & Sons, Inc.
29.
SchönD. A. (1987). Educating the reflective practitioner: Toward a new design for teaching and learning in the professions. San Francisco, CA: Jossey-Bass.
30.
SchraderP. G.LawlessK. A.McCreeryM. (2009). Intertextuality in massively multiplayer online games. In FerdigR. E. (Ed.), Handbook of research on effective electronic gaming in education (Vol. III, pp. 791–807). Hershey, PA: Information Science Reference.
31.
SeelN. M. (1999). Educational diagnosis of mental models: Assessment problems and technology-based solutions. Journal of Structural Learning and Intelligent Systems, 14(2), 153–185.
32.
SeelN. M.IfenthalerD.Pirnay-DummerP. (2009). Mental models and problem solving: Technological solutions for measurement and assessment of the development of expertise. In BlumscheinP.HungW.JonassenD. H.StrobelJ. (Eds.), Model-based approaches to learning: Using systems models and simulations to improve understanding and problem solving in complex domains (pp. 17–40). Rotterdam: Sense Publishers.
33.
ShafferD. W. (2006). How computer games help children learn?New York: Palgrave Macmillan.
