Analyzing “real-world” anomalous data after experimentation with a virtual laboratory

Developing effective pedagogies to help students examine anomalous data is critical for the education of the next generation of scientists and engineers. By definition anomalous data do not concur with prior knowledge, theories and expectations. Such data are the common outcome of empirical investigation in hands-on laboratories (HOLs). These aberrations can be counter intuitive for students when they investigate real-world processes with technology tools, such as virtual laboratories (VRLs), that may project a simplified view of data. This study blended learning with a VRL and the examination of real-world data in two engineering classrooms. The results indicated that students developed new knowledge with the VRL and were able to apply this knowledge to evaluate anomalous data from an HOL. However, students continued to experience difficulties in transferring their newly constructed knowledge to reason about how anomalous data results may have come about. The results provide directions for continued research on students’ perceptions of anomalous data and also suggest the need for evidence-based instructional design decisions for the use of existing VRLs to prepare science and engineering students for real-world investigations.

[1]  C. Dede.  Using Virtual Reality Technology to Convey Abstract Scientific Concepts , 1997 .

[2]  G. Olympiou,et al.  Blending Physical and Virtual Manipulatives: An Effort to Improve Students' Conceptual Understanding through Science Laboratory Experimentation , 2012 .

[3]  Marios Papaevripidou,et al.  Effects of experimenting with physical and virtual manipulatives on students' conceptual understanding in heat and temperature , 2008 .

[4]  L. Mason.  Responses to anomalous data on controversial topics and theory change , 2001 .

[5]  R. Kozma.  The material features of multiple representations and their cognitive and social affordances for science understanding , 2003 .

[6]  William A. Sandoval,et al.  The Quality of Students' Use of Evidence in Written Scientific Explanations , 2005 .

[7]  Yael Kali,et al.  The Design Principles Database as a Means for Promoting Design-Based Research , 2008 .

[8]  P. Kirschner,et al.  Mindtools for teacher communities: a European perspective , 2003 .

[9]  Jeffrey V. Nickerson,et al.  Hands-on, simulated, and remote laboratories: A comparative literature review , 2006, CSUR.

[10]  Maoguo Gong,et al.  An efficient negative selection algorithm with further training for anomaly detection , 2012, Knowl. Based Syst..

[11]  David H. Jonassen,et al.  Modeling with Technology: Mindtools for Conceptual Change , 2005 .

[12]  Dietrich Dörner,et al.  The Logic Of Failure: Recognizing And Avoiding Error In Complex Situations , 1997 .

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

[14]  Stephanie Henderson-Begg,et al.  The Learning Gains and Student Perceptions of a Second Life Virtual Lab , 2009 .

[15]  P. Drenth,et al.  Responsible conduct in research , 2006, Science and engineering ethics.

[16]  William F. Brewer,et al.  Models of Data: A Theory of How People Evaluate Data , 2001 .

[17]  Jeffrey V. Nickerson,et al.  Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories , 2011, Comput. Educ..

[18]  P. Hewson,et al.  Accommodation of a scientific conception: Toward a theory of conceptual change , 1982 .

[19]  Z. Zacharia,et al.  Physical and Virtual Laboratories in Science and Engineering Education , 2013, Science.

[20]  Z. C. Zacharia,et al.  Comparing and combining real and virtual experimentation: an effort to enhance students' conceptual understanding of electric circuits , 2007, J. Comput. Assist. Learn..

[21]  F. Paas,et al.  Cognitive Architecture and Instructional Design , 1998 .

[22]  Mark Windschitl,et al.  Supporting the development of science inquiry skills with special classes of software , 2000 .

[23]  Sangarapillai Kanapathipillai,et al.  Engineering students' understanding of the role of experimentation , 2000 .

[24]  Amos Dreyfus,et al.  Applying the cognitive conflict strategy for conceptual change - some implications, difficulties, and problems , 1990 .

[25]  Andrew Nafalski,et al.  Remote laboratories versus virtual and real laboratories , 2003, 33rd Annual Frontiers in Education, 2003. FIE 2003..

[26]  B. BALAMURALITHARA,et al.  Virtual laboratories in engineering education: The simulation lab and remote lab , 2009, Comput. Appl. Eng. Educ..

[27]  Eva Erdosne Toth,et al.  "Virtual Inquiry" in the Science Classroom: What is the Role of Technological Pedagogial Content Knowledge? , 2009, Int. J. Inf. Commun. Technol. Educ..

[28]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[29]  Nancy L. Leech,et al.  IBM SPSS for Intermediate Statistics: Use and Interpretation, Fifth Edition , 2014 .

[30]  Zdeslav Hrepic,et al.  Dynamic Transfer: A Perspective from Physics Education Research , 2005 .

[31]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[32]  Eva Erdosne Toth,et al.  Interdisciplinary Collaboration for Educational Innovation: Integrating Inquiry Learning with a Virtual Laboratory to an Engineering Course on “Cellular Machinery”. , 2012 .

[33]  Lloyd P. Rieber,et al.  Computer-based microworlds: A bridge between constructivism and direct instruction , 1992 .

[34]  Chris DiGiano,et al.  IDEA: Identifying design principles in educational applets , 2005 .

[35]  Melissa S. Anderson,et al.  Scientists behaving badly , 2005, Nature.

[36]  Yehudit Judy Dori,et al.  A Web-Based Chemistry Course as a Means To Foster Freshmen Learning , 2003 .

[37]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[38]  D. Perkins,et al.  Rocky Roads to Transfer: Rethinking Mechanism of a Neglected Phenomenon , 1989 .

[39]  Mario Carretero,et al.  Conceptual change and anomalous data: A case study in the domain of natural sciences , 1997 .

[40]  M. Gertrude Hennessey,et al.  Sixth-Grade Students' Epistemologies of Science: The Impact of School Science Experiences on Epistemological Development , 2000 .

[41]  Anne Victoria Neale,et al.  Correction and use of biomedical literature affected by scientific misconduct , 2007, Sci. Eng. Ethics.

[42]  Sidney Dekker,et al.  The Field Guide to Understanding 'Human Error' , 2014 .

[43]  William F. Brewer,et al.  An empirical test of a taxonomy of responses to anomalous data in science. , 1998 .

[44]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[45]  J. Piaget,et al.  The equilibration of cognitive structures : the central problem of intellectual development , 1985 .

[46]  Georgios Olympiou,et al.  Making the invisible visible: enhancing students’ conceptual understanding by introducing representations of abstract objects in a simulation , 2013 .

[47]  A. Huberman,et al.  Qualitative Data Analysis: A Methods Sourcebook , 1994 .

[48]  Constantinos P. Constantinou,et al.  Comparing the influence of physical and virtual manipulatives in the context of the Physics by Inquiry curriculum: The case of undergraduate students’ conceptual understanding of heat and temperature , 2008 .

[49]  Elliot Soloway,et al.  Model-It: A Case Study of Learner-Centered Software Design for Supporting Model Building. , 1996 .

[50]  Tilman Wolf,et al.  Assessing Student Learning in a Virtual Laboratory Environment , 2010, IEEE Transactions on Education.

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

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

[53]  Andy Field,et al.  Discovering statistics using SPSS, 2nd ed. , 2005 .

[54]  J. Michael Spector.  Cognition and learning in the digital age: Promising research and practice , 2008, Comput. Hum. Behav..

[55]  Michele D. Dickey.  The pragmatics of virtual worlds for K-12 educators: investigating the affordances and constraints of ActiveWorlds and SecondLife with K-12 in-service teachers , 2011 .

[56]  M. Limón.  On the cognitive conflict as an instructional strategy for conceptual change: a critical appraisal , 2001 .

[57]  Jose P. Mestre,et al.  Toward a Model of Transfer as Sense-Making , 2013 .

[58]  E. Guba,et al.  Competing paradigms in qualitative research. , 1994 .

[59]  Jeremy F. Strayer,et al.  Which Comes First the Use of Computer Simulation of Frog Dissection or Conventional Dissection as Academic Exercise , 2010 .

[60]  Diane Jass Ketelhut,et al.  Distance Learning (Virtual Learning) , 2004 .

[61]  Brian J. Reiser,et al.  Scaffolding Complex Learning: The Mechanisms of Structuring and Problematizing Student Work , 2004, The Journal of the Learning Sciences.

[62]  Sufen Chen,et al.  The view of scientific inquiry conveyed by simulation-based virtual laboratories , 2010, Comput. Educ..

[63]  J. Micael Spector.  Designing Technology Enhanced Learning Environments , 2000 .

[64]  William F. Brewer,et al.  The Role of Anomalous Data in Knowledge Acquisition: A Theoretical Framework and Implications for Science Instruction , 1993 .

[65]  Wolfgang Schnotz,et al.  Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning , 2005 .

[66]  W. Sandoval,et al.  Explanation-Driven Inquiry: Integrating Conceptual and Epistemic Scaffolds for Scientific Inquiry , 2004 .

[67]  J. Michael Spector,et al.  Learning, problem solving, and mindtools : essays in honor of David H. Jonassen , 2013 .

[68]  Maggie Renken,et al.  Computer simulations and clear observations do not guarantee conceptual understanding , 2013 .

[69]  Eva Erdosne Toth,et al.  “Virtual Inquiry”: Teaching Molecular Aspects of Evolutionary Biology Through Computer-Based Inquiry , 2009, Evolution: Education and Outreach.

[70]  R. Duschl.  Science Education in Three-Part Harmony: Balancing Conceptual, Epistemic, and Social Learning Goals , 2008 .

[71]  Eva Erdosne Toth,et al.  Blended inquiry with hands-on and virtual laboratories: the role of perceptual features during knowledge construction , 2014, Interact. Learn. Environ..

[72]  Lara M. Triona,et al.  Point and Click or Grab and Heft: Comparing the Influence of Physical and Virtual Instructional Materials on Elementary School Students' Ability to Design Experiments , 2003 .

[73]  David Klahr,et al.  Error Matters: An Initial Exploration of Elementary School Children's Understanding of Experimental Error , 2003 .

[74]  Joseph Krajcik,et al.  A protocol analysis of the influence of technology on students' actions, verbal commentary, and thought processes during the performance of acid‐base titrations , 1993 .

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

[76]  Zacharias C. Zacharia,et al.  Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: a literature review , 2015, Educational Technology Research and Development.

[77]  Susan Carey,et al.  On understanding the nature of scientific knowledge , 1993 .

[78]  Matthew B. Miles,et al.  Qualitative Data Analysis: An Expanded Sourcebook , 1994 .

[79]  N. Denzin,et al.  Handbook of Qualitative Research , 1994 .

[80]  D. Suthers,et al.  “Mapping to know”: The effects of representational guidance and reflective assessment on scientific inquiry , 2002 .

[81]  R. Driver,et al.  Students’ conceptions and the learning of science , 1989 .