Finding Out How They Find It Out: An empirical analysis of inquiry learners’ need for support

Inquiry learning environments increasingly incorporate modelling facilities for students to articulate their research hypotheses and (acquired) domain knowledge. This study compared performance success and scientific reasoning of university students with high prior knowledge (n = 11), students from senior high‐school (n = 10), and junior high‐school (n = 10) with intermediate and low prior knowledge, respectively, in order to reveal domain novices’ need for support in such environments. Results indicated that the scientific reasoning of both groups of high‐school students was comparable to that of the experts. As high‐school students achieved significantly lower performance success scores, their expert‐like behaviour was rather ineffective; qualitative analyses substantiated this conclusion. Based on these findings, implications for supporting domain novices in inquiry learning environments are advanced.

[1]  D. Leutner.  Guided discovery learning with computer-based simulation games: Effects of adaptive and non-adaptive instructional support , 1993 .

[2]  Ton de Jong.  Computer simulations. Technological advances in inquiry learning. , 2006, Science.

[3]  Barbara Y. White,et al.  Causal Model Progressions as a Foundation for Intelligent Learning Environments , 1990, Artif. Intell..

[4]  Marcel V. J. Veenman,et al.  The assessment of metacognitive skills: what can be learned from multi-method designs? , 2005 .

[5]  Elizabeth A. Davis,et al.  Scaffolding students' knowledge integration: prompts for reflection in KIE , 2000 .

[6]  Joseph Krajcik,et al.  A Scaffolding Design Framework for Software to Support Science Inquiry , 2004, The Journal of the Learning Sciences.

[7]  A. Lazonder,et al.  Unraveling the influence of domain knowledge during simulation-based inquiry learning , 2009 .

[8]  A. Lazonder,et al.  The influence of domain knowledge on strategy use during simulation-based inquiry learning , 2008 .

[9]  S. Kay.  On the Nature of Expertise. , 1992 .

[10]  J. Frederiksen,et al.  Enabling Students to Construct Theories of Collaborative Inquiry and Reflective Learning: Computer Support for Metacognitive Development , 1999 .

[11]  Ton de Jong,et al.  Discovery simulations and the assessment of intuitive knowledge , 2001, J. Comput. Assist. Learn..

[12]  David Klahr,et al.  Instructionless Learning about a Complex Device: The Paradigm and Observations , 1986, Int. J. Man Mach. Stud..

[13]  Amy Pallant,et al.  Fostering Students' Epistemologies of Models via Authentic Model-Based Tasks , 2004 .

[14]  J. Clement,et al.  Effects of Student-Generated Diagrams versus Student-Generated Summaries on Conceptual Understanding of Causal and Dynamic Knowledge in Plate Tectonics. , 1999 .

[15]  Dinesh Batra,et al.  Conceptual Data Modelling in Database Design: Similarities and Differences between Expert and Novice Designers , 1992, Int. J. Man Mach. Stud..

[16]  Lloyd P. Rieber,et al.  To Teach or Not to Teach? Comparing the Use of Computer-Based Simulations in Deductive versus Inductive Approaches to Learning with Adults in Science , 1995 .

[17]  Adrianus W. Lazonder,et al.  The guided discovery principle in multimedia learning. , 2005 .

[18]  Ton de Jong,et al.  The Integration of Computer Simulation and Learning Support: An Example from the Physics Domain of Collisions , 1999 .

[19]  W. V. van Joolingen,et al.  The Difficult Process of Scientific Modelling: An analysis of novices' reasoning during computer‐based modelling , 2005 .

[20]  Jos Beishuizen,et al.  Asking Questions During Self-Directed Inductive Learning: Effects on Learning Outcome and Learning Processes , 2004, Interact. Learn. Environ..

[21]  L. Schauble,et al.  Students' transition from an engineering model to a science model of experimentation , 1991 .

[22]  Ton de Jong,et al.  Regulative support for collaborative scientific inquiry learning , 2006, J. Comput. Assist. Learn..

[23]  Ton de Jong,et al.  Co-Lab: research and development of an online learning environment for collaborative scientific discovery learning , 2005, Comput. Hum. Behav..

[24]  C. Chinn,et al.  Epistemologically Authentic Inquiry in Schools: A Theoretical Framework for Evaluating Inquiry Tasks , 2002 .

[25]  T. Jong,et al.  Supporting simulation-based learning; the effects of model progression and assignments on definitional and intuitive knowledge , 1998 .

[26]  David Klahr,et al.  Dual Space Search During Scientific Reasoning , 1988, Cogn. Sci..

[27]  Ton de Jong,et al.  Offering and discovering domain information in simulation-based inquiry learning , 2010 .

[28]  Joseph Krajcik,et al.  Making Dynamic Modeling Accessible to Precollege Science Students , 1994, Interact. Learn. Environ..

[29]  Elwin R. Savelsbergh,et al.  The effect of external representation on constructing computer models of complex phenomena , 2003 .

[30]  Ann L. Brown,et al.  How people learn: Brain, mind, experience, and school. , 1999 .

[31]  Valerie J. Shute,et al.  Inference and discovery in an exploratory laboratory. , 1988 .

[32]  J. Beishuizen,et al.  Content effects in self-directed inductive learning , 2003 .

[33]  Corinne Zimmerman.  The development of scientific reasoning skills. , 2000 .

[34]  Corinne Zimmerman.  The Development of Scientific Thinking Skills in Elementary and Middle School. , 2007 .

[35]  Ton de Jong,et al.  Scientific Discovery Learning with Computer Simulations of Conceptual Domains , 1998 .