Multi-methods Approach for Domain-Specific Grounding: An ITS for Connection Making in Chemistry

Making connections between graphical representations is integral to learning in science, technology, engineering, and mathematical (STEM) fields. However, students often fail to make these connections spontaneously. ITSs are suitable tools to support connection making. Yet, when designing an ITS for connection making, we need to investigate what learning processes and concepts play a role within the specific domain. We describe a multi-methods approach for grounding ITS design in the specific requirements of the target domain. Specifically, we applied this approach to an ITS for connection making in chemistry. We used a theoretical framework that describes potential target learning processes and conducted two empirical studies – using tests, eye tracking, and interviews – to investigate how these learning processes play out in the chemistry domain. We illustrate how our findings inform the design of a chemistry tutor. Initial pilot study results suggest that the ITS promotes learning processes that are productive in chemistry.

[1]  Mike Stieff,et al.  Connected Chemistry—A Novel Modeling Environment for the Chemistry Classroom , 2005 .

[2]  Cheryl I. Johnson,et al.  An eye movement analysis of the spatial contiguity effect in multimedia learning. , 2012, Journal of experimental psychology. Applied.

[3]  John K. Gilbert,et al.  Towards a Better Utilization of Diagrams in Research into the Use of Representative Levels in Chemical Education , 2009 .

[4]  Daniel Bodemer,et al.  External and mental referencing of multiple representations , 2006, Comput. Hum. Behav..

[5]  Robert B. Kozma,et al.  The Cambridge Handbook of Multimedia Learning: Multimedia Learning of Chemistry , 2005 .

[6]  David F. Treagust,et al.  Towards a Coherent Model for Macro, Submicro and Symbolic Representations in Chemical Education , 2009 .

[7]  Vincent Aleven,et al.  Sense Making Alone Doesn't Do It: Fluency Matters Too! ITS Support for Robust Learning with Multiple Representations , 2012, ITS.

[8]  Vincent Aleven,et al.  How to Schedule Multiple Graphical Representations? A Classroom Experiment With an Intelligent Tutoring System for Fractions , 2012, ICLS.

[9]  Joseph Krajcik,et al.  Promoting understanding of chemical representations: Students' use of a visualization tool in the classroom , 2001 .

[10]  Ji Y. Son,et al.  Perceptual Learning Modules in Mathematics: Enhancing Students' Pattern Recognition, Structure Extraction, and Fluency , 2010, Top. Cogn. Sci..

[11]  Patrik Pluchino,et al.  Effects of Picture Labeling on Science Text Processing and Learning: Evidence From Eye Movements , 2013 .

[12]  C. W. Bowen,et al.  Representational Systems Used by Graduate Students while Problem Solving in Organic Synthesis. , 1990 .

[13]  Richard Mayer,et al.  Multimedia Learning , 2001, Visible Learning Guide to Student Achievement.

[14]  Robert B. Kozma,et al.  Students Becoming Chemists: Developing Representationl Competence , 2005 .

[15]  George M. Bodner,et al.  Mental Models : The Role of Representations in Problem Solving in Chemistry PROCEEDINGS , 2002 .

[16]  S. Ainsworth DeFT: A Conceptual Framework for Considering Learning with Multiple Representations. , 2006 .