An adaptive collaboration script for learning with multiple visual representations in chemistry
暂无分享,去创建一个
[1] Vincent Aleven,et al. Different Futures of Adaptive Collaborative Learning Support , 2016, International Journal of Artificial Intelligence in Education.
[2] Shaaron Ainsworth,et al. Examining the Effects of Different Multiple Representational Systems in Learning Primary Mathematics , 2002 .
[3] Robert B. Kozma,et al. Students Becoming Chemists: Developing Representationl Competence , 2005 .
[4] Daniel L. Schwartz,et al. The Emergence of Abstract Representations in Dyad Problem Solving , 1995 .
[5] Pierre Dillenbourg,et al. Over-scripting CSCL: The risks of blending collaborative learning with instructional design , 2002 .
[6] Vicente A Talanquer,et al. Chemistry Education: Ten Facets to Shape Us. , 2013 .
[7] Kenneth R. Koedinger,et al. CTRL: A research framework for providing adaptive collaborative learning support , 2009, User Modeling and User-Adapted Interaction.
[8] B. Laeng,et al. A Redrawn Vandenberg and Kuse Mental Rotations Test - Different Versions and Factors That Affect Performance , 1995, Brain and Cognition.
[9] Anastasios Karakostas,et al. Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field , 2011, IEEE Transactions on Learning Technologies.
[10] Adam Kraft,et al. What happens when representations fail to represent? Graduate students’ mental models of organic chemistry diagrams , 2010 .
[11] Vincent Aleven,et al. A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors , 2009, Int. J. Artif. Intell. Educ..
[12] Y. Lou,et al. Small Group and Individual Learning with Technology: A Meta-Analysis , 2001 .
[13] F. Fischer,et al. Collaboration Scripts – A Conceptual Analysis , 2006 .
[14] D. Uttal. On the relation between play and symbolic thought: The case of mathematics manipulatives , 2003 .
[15] M. Stieff. Mental rotation and diagrammatic reasoning in science , 2007 .
[16] John K. Gilbert,et al. Models and Modelling: Routes to More Authentic Science Education , 2004 .
[17] S. Ainsworth. DeFT: A Conceptual Framework for Considering Learning with Multiple Representations. , 2006 .
[18] Zachary A. Pardos,et al. How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study , 2013, International Journal of Artificial Intelligence in Education.
[19] Tina Seufert. Supporting Coherence Formation in Learning from Multiple Representations , 2003 .
[20] Roy D. Pea,et al. Distributed by Design: On the Promises and Pitfalls of Collaborative Learning with Multiple Representations , 2011 .
[21] Fred Paas,et al. Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect , 2011 .
[22] Martina A. Rau,et al. A Framework for Educational Technologies that Support Representational Competencies , 2017, IEEE Transactions on Learning Technologies.
[23] 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..
[24] Karsten Stegmann,et al. Computer-supported collaborative learning in higher education: scripts for argumentative knowledge construction in distributed groups , 2005, CSCL.
[25] F. Fischer,et al. Toward a Script Theory of Guidance in Computer-Supported Collaborative Learning , 2013, Educational psychologist.
[26] Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.
[27] 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..
[28] 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..
[29] Karsten Stegmann,et al. Computer-supported collaborative learning in higher education: Scripts for argumentative knowledge c , 2005 .
[30] Benjamin S. Bloom,et al. Taxonomy of Educational Objectives: The Classification of Educational Goals. , 1957 .
[31] John K. Gilbert,et al. Visualization: An Emergent Field of Practice and Enquiry in Science Education , 2008 .
[32] K. VanLehn. The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems , 2011 .
[33] Martina A. Rau,et al. Enhancing undergraduate chemistry learning by helping students make connections among multiple graphical representations , 2015 .
[34] Michelle K. Smith,et al. Active learning increases student performance in science, engineering, and mathematics , 2014, Proceedings of the National Academy of Sciences.
[35] Daniel D. Suthers,et al. Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments , 2008, Comput. Educ..
[36] Paul A. Kirschner,et al. The social and interactive dimensions of collaborative learning , 2014 .
[37] Benjamin S. Bloom,et al. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives , 2000 .