How instructors frame students' interactions with educational technologies can enhance or reduce learning with multiple representations

Abstract Instructors in STEM classrooms often frame students' interactions with technologies to help them learn content. For instance, in many STEM domains, instructors commonly help students translate physical 3D models into 2D drawings by prompting them to focus on (a) orienting physical 3D models and (b) generating 2D drawings. We investigate whether framing prompts that target either of these practices enhance the effectiveness of an educational technology that supports collaborative translation among multiple representations. To this end, we conducted a quasi-experiment with 565 undergraduate chemistry students. All students collaboratively built physical 3D models of molecules and translated them into 2D drawings. In a business-as-usual control condition, students drew on paper, without support from an educational technology. In two experimental conditions, students drew in an educational technology that provided feedback and prompted collaboration. One condition received framing prompts to focus on physical models (model condition); another received prompts to generate intermediary drawings on paper (draw condition). Compared to the control condition, the model condition showed higher learning gains, but the draw condition showed lower learning gains. Analyses of log data showed that students made more model-based errors, and the prompts in the model condition reduced these model-based errors. However, interviews with instructors showed that they prefer drawing-focused prompts, in contrast to our results. These findings offer theoretical insights into how students learn to translate among representations. Furthermore, they yield practical recommendations for the use of educational technologies that support learning with multiple representations.

[1]  Mike Stieff,et al.  Sketching the Invisible to Predict the Visible: From Drawing to Modeling in Chemistry , 2017, Top. Cogn. Sci..

[2]  Mary Hegarty,et al.  Effects of interface and spatial ability on manipulation of virtual models in a STEM domain , 2016, Comput. Hum. Behav..

[3]  Shaaron Ainsworth,et al.  The Educational Value of Multiple-representations when Learning Complex Scientific Concepts , 2008 .

[4]  Marcia C. Linn,et al.  Can generating representations enhance learning with dynamic visualizations , 2011 .

[5]  W. Pouw,et al.  An Embedded and Embodied Cognition Review of Instructional Manipulatives , 2014 .

[6]  Anastasios Karakostas,et al.  Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field , 2011, IEEE Transactions on Learning Technologies.

[7]  Laura M. Stapleton,et al.  The Effect of Small Sample Size on Two-Level Model Estimates: A Review and Illustration , 2014, Educational Psychology Review.

[8]  K. VanLehn The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems , 2011 .

[9]  Detlev Leutner,et al.  The Cambridge Handbook of Multimedia Learning: The Generative Drawing Principle in Multimedia Learning , 2014 .

[10]  D. Lubinski,et al.  Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance. , 2009 .

[11]  Martina A. Rau,et al.  Effectiveness and efficiency of adding drawing prompts to an interactive educational technology when learning with visual representations , 2017, Learning and Instruction.

[12]  Maria Evagorou,et al.  The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works , 2015 .

[13]  Anastasios Karakostas,et al.  Enhancing collaborative learning through dynamic forms of support: the impact of an adaptive domain-specific support strategy , 2011, J. Comput. Assist. Learn..

[14]  Susan R. Singer,et al.  Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering. , 2012 .

[15]  Antonija Mitrovic,et al.  Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams , 2007, Int. J. Comput. Support. Collab. Learn..

[16]  Mary Hegarty,et al.  Constrained interactivity for relating multiple representations in science: When virtual is better than real , 2015, Comput. Educ..

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

[18]  Michelene T. H. Chi,et al.  Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities , 2009, Top. Cogn. Sci..

[19]  Y. Lou,et al.  Small Group and Individual Learning with Technology: A Meta-Analysis , 2001 .

[20]  Mike T. Springer Improving Students' Understanding of Molecular Structure through Broad-Based Use of Computer Models in the Undergraduate Organic Chemistry Lecture , 2014 .

[21]  Nikol Rummel,et al.  Are two heads always better than one? Differential effects of collaboration on students’ computer-supported learning in mathematics , 2011, Int. J. Comput. Support. Collab. Learn..

[22]  M. Brooks,et al.  Drawing, Visualisation and Young Children’s Exploration of “Big Ideas” , 2009 .

[23]  Detlev Leutner,et al.  Cognitive load and instructionally supported learning with provided and learner-generated visualizations , 2011, Comput. Hum. Behav..

[24]  Richard E. Mayer,et al.  Drawing pictures during learning from scientific text: testing the generative drawing effect and the prognostic drawing effect , 2014 .

[25]  Tina Seufert Supporting Coherence Formation in Learning from Multiple Representations , 2003 .

[26]  Roy D. Pea,et al.  Distributed by Design: On the Promises and Pitfalls of Collaborative Learning with Multiple Representations , 2011 .

[27]  Armin Weinberger,et al.  Scripted collaborative drawing in elementary science education , 2013, Instructional Science.

[28]  Fred Paas,et al.  Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect , 2011 .

[29]  Martina A. Rau,et al.  An adaptive collaboration script for learning with multiple visual representations in chemistry , 2017, Comput. Educ..

[30]  Vincent Aleven,et al.  Successful learning with multiple graphical representations and self-explanation prompts. , 2015 .

[31]  Mitchell J. Nathan,et al.  Expert Blind Spot Among Preservice Teachers , 2003 .

[32]  F. Fischer,et al.  Socio-Cognitive Scaffolding with Computer-Supported Collaboration Scripts: a Meta-Analysis , 2017 .

[33]  M. Hegarty,et al.  Representational Translation With Concrete Models in Organic Chemistry , 2012 .

[34]  D. Uttal,et al.  The malleability of spatial skills: a meta-analysis of training studies. , 2013, Psychological bulletin.

[35]  Kenneth R. Koedinger,et al.  CTRL: A research framework for providing adaptive collaborative learning support , 2009, User Modeling and User-Adapted Interaction.

[36]  B. Laeng,et al.  A Redrawn Vandenberg and Kuse Mental Rotations Test - Different Versions and Factors That Affect Performance , 1995, Brain and Cognition.

[37]  M. Stieff Mental rotation and diagrammatic reasoning in science , 2007 .

[38]  Martina A. Rau,et al.  Conditions for the Effectiveness of Multiple Visual Representations in Enhancing STEM Learning , 2017 .

[39]  T. Höffler Spatial Ability: Its Influence on Learning with Visualizations—a Meta-Analytic Review , 2010 .

[40]  F. Fischer,et al.  Toward a Script Theory of Guidance in Computer-Supported Collaborative Learning , 2013, Educational psychologist.