Assessing Science Inquiry and Reasoning Using Dynamic Visualizations and Interactive Simulations
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[1] Gaea Leinhardt,et al. "One Firm Spot": The Role of Homework as Lever in Acquiring Conceptual and Performance Competence in College Chemistry , 2007 .
[2] Helen R. Quinn,et al. A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas , 2013 .
[3] Ric Lowe,et al. Learning from animation: Where to look, when to look , 2008 .
[4] Jodi L. Davenport,et al. Comparing three online testing modalities: Using static, active, and interactive online testing modalities to assess middle school students' understanding of fundamental ideas and use of inquiry skills related to ecosystems , 2014 .
[5] Miriam Reiner,et al. Conceptual classroom environment - a system view of learning , 2001 .
[6] Richard Lehrer,et al. Similarity of form and substance: modeling material kind , 2001 .
[7] Paul Horwitz,et al. Looking inside the black box: assessing model-based learning and inquiry in BioLogicaTM , 2010, Int. J. Learn. Technol..
[8] Joseph Krajcik,et al. Inquiry Based Science Supported by Technology: Achievement among Urban Middle School Students. , 2000 .
[9] Katharina Scheiter,et al. The influence of text modality on learning with static and dynamic visualizations , 2011, Comput. Hum. Behav..
[10] Anna N. Rafferty,et al. ChemVLab+: Evaluating a Virtual Lab Tutor for High School Chemistry , 2012, ICLS.
[11] M. Hegarty. Dynamic visualizations and learning: getting to the difficult questions , 2004 .
[12] Jan L. Plass,et al. Design factors for educationally effective animations and simulations , 2009, J. Comput. High. Educ..
[13] Richard Lowe,et al. A Composition Approach to Design of Educational Animations , 2017 .
[14] Mireille Betrancourt,et al. The Cambridge Handbook of Multimedia Learning: The Animation and Interactivity Principles in Multimedia Learning , 2005 .
[15] Fernando Flores,et al. Preconceptions and relations used by children in the construction of food chains , 1994 .
[16] Daniel L. Schwartz,et al. Inferences through imagined actions: Knowing by simulated doing. , 1999 .
[17] Sam Reid,et al. A Study of Educational Simulations Part 1 - Engagement and Learning , 2008 .
[18] Jodi L. Davenport,et al. Next-Generation Environments for Assessing and Promoting Complex Science Learning. , 2013 .
[19] D. Schwartz,et al. The Cambridge Handbook of the Learning Sciences: Spatial Representations and Imagery in Learning , 2005 .
[20] Richard Mayer,et al. Multimedia Learning , 2001, Visible Learning Guide to Student Achievement.
[21] Michael J. Timms,et al. Research Article Science Assessments for All: Integrating Science Simulations Into Balanced State Science Assessment Systems , 2012 .
[22] K. Holyoak,et al. The use of diagrams in analogical problem solving , 2001, Memory & cognition.
[23] Paul Horwitz,et al. Learning Genetics from Dragons: From Computer-Based Manipulatives to Hypermodels , 2010 .
[24] Ashok K. Goel,et al. Understanding Complex Natural Systems by Articulating Structure-Behavior-Function Models , 2011, J. Educ. Technol. Soc..
[25] Mary Hegarty,et al. Top-down and bottom-up influences on learning from animations , 2007, Int. J. Hum. Comput. Stud..
[26] Ann L. Brown,et al. How people learn: Brain, mind, experience, and school. , 1999 .
[27] D. Campbell,et al. Convergent and discriminant validation by the multitrait-multimethod matrix. , 1959, Psychological bulletin.
[28] Lloyd P. Rieber,et al. The role of meaning in interpreting graphical and textual feedback during a computer-based simulation , 1996, Comput. Educ..
[29] Herbert A. Simon,et al. Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987 .
[30] B. Homer,et al. Optimizing cognitive load for learning from computer-based science simulations , 2006 .
[31] Edys S. Quellmalz,et al. Supporting and Assessing Complex Biology Learning with Computer-Based Simulations and Representations , 2013 .
[32] Richard Lowe,et al. Animation principles in multimedia learning , 2014 .
[33] 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..
[34] Lloyd P. Rieber,et al. Discovery learning, representation, and explanation within a computer-based simulation: finding the right mix , 2004 .
[35] Marios Papaevripidou,et al. Modeling complex marine ecosystems: an investigation of two teaching approaches with fifth graders , 2007, J. Comput. Assist. Learn..
[36] Ngss Lead States. Next generation science standards : for states, by states , 2013 .
[37] James W Pellegrino,et al. Proficiency in Science: Assessment Challenges and Opportunities , 2013, Science.
[38] C Loehlin John,et al. Latent variable models: an introduction to factor, path, and structural analysis , 1986 .
[39] Richard K. Lowe,et al. An Eye Tracking Comparison of External Pointing Cues and Internal Continuous Cues in Learning with Complex Animations , 2010 .
[40] Björn B. de Koning,et al. Attention Guidance Strategies for Supporting Learning from Dynamic Visualizations , 2017 .
[41] Michael O. Martin,et al. TIMSS 2015 Assessment Frameworks. , 2013 .
[42] Marian Petre,et al. Learning to Read Graphics: Some Evidence that 'Seeing' an Information Display is an Acquired Skill , 1993, J. Vis. Lang. Comput..
[43] Michael J. Timms,et al. The promise of simulation-based science assessment: the Calipers project , 2010, Int. J. Learn. Technol..
[44] Richard E. Mayer,et al. e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning , 2002 .
[45] Liesbeth Kester,et al. The Four-Component Instructional Design Model : Multimedia Principles in Environments for Complex Learning , 2014 .
[46] Rolf Ploetzner,et al. Learning from Dynamic Visualization , 2017 .
[47] Richard Lowe,et al. Learning with Animation: Research Implications for Design , 2007 .
[48] James W Pellegrino,et al. Technology and Testing , 2009, Science.
[49] Lei Liu,et al. Focusing on Function: Thinking below the Surface of Complex Natural Systems. , 2008 .
[50] David C. Webb,et al. Mr. Vetro: A Collective Simulation for teaching health science , 2010, Int. J. Comput. Support. Collab. Learn..
[51] Shaaron Ainsworth,et al. The Educational Value of Multiple-representations when Learning Complex Scientific Concepts , 2008 .
[52] Ton de Jong,et al. Technological Advances in Inquiry Learning , 2006 .
[53] M. Just,et al. Constructing mental models of machines from text and diagrams. , 1993 .
[54] Susan R. Goldman,et al. Learning in complex domains: When and why do multiple representations help? , 2003 .
[55] James D. Slotta,et al. Helping Students Understand Challenging Topics in Science Through Ontology Training , 2006 .
[56] Richard E. Mayer,et al. The Cambridge Handbook of Multimedia Learning: Principles for Reducing Extraneous Processing in Multimedia Learning : Coherence, Signaling, Redundancy, Spatial Contiguity, and Temporal Contiguity Principles , 2005 .
[57] Cheryl I. Johnson,et al. Revising the Redundancy Principle in Multimedia Learning. , 2008 .