How instructors frame students' interactions with educational technologies can enhance or reduce learning with multiple representations
暂无分享,去创建一个
[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.