Neuroscience Perspectives for Science and Mathematics Learning in Technology-Enhanced Learning Environments: Editorial

International Journal of Science and Mathematics Education

Volumn 12, Issue 3, 2014, Pages 467-474

  Anderson, O Roger ^a   Love, Bradley C ^b   Tsai, Meng Jung ^c  

^a Mathematics Science and Technology Teachers College Columbia University ^*  (United States)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84901932448     PISSN: 15710068     EISSN: 15731774     Source Type: Journal    
DOI: 10.1007/s10763-014-9540-2     Document Type: Editorial

References (28)

1. Anderson, O. R. (1983). A neuromathematical model of human information processing. Journal of Research in Science Teaching, 20, 603-620.
2. Anderson, O. R. (1997). A neurocognitive perspective on current learning theory and science instructional strategies. Science Education, 81, 67-90.
3. Anderson, O. R. (2009). Neurocognitive theory and constructivism in science education: A review of neurobiological, cognitive and cultural perspectives. Brunei International Journal of Mathematics and Science Education, 1, 1-32.
4. Anderson, O. R. (2014). Progress in application of the neurosciences to an understanding of human learning: The challenge of finding a middle-ground neuroeducational theory. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-013-9455-3.
5. Anderson, O. R. & Contino, J. (2013). The role of visualization in conceptual learning and conceptual change (Chap. 1 in Section 1: Brain functioning and conceptual change). In K. Finson & J. Pedersen (Eds.), Visual data and their use in science education (pp. 3-21). Charlotte: Information Age Publishing.
6. Ariasi, N. & Mason, L. (2014). From covert processes to overt outcomes of refutation text reading: The interplay of science text structure and working memory capacity through eye fixations. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-013-9494-9.
7. Canham, M. & Hegarty, M. (2010). Effects of knowledge and display design on comprehension of complex graphics. Learning and Instruction, 20, 155-166.
8. Chang, H.-Y. & Linn, M. C. (2013). Scaffolding learning from molecular visualizations. Journal of Research in Science Teaching, 50(7), 858-886.
9. Chen, Y. C. & Yang, F. Y. (2014). Probing the relationship between process of spatial problems solving and science learning: An eye tracking approach. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-013-9504-y.
10. Duncan, R. G. & Rivet, A. (2013). Science learning progressions. Science, 339, 396-397.
11. Ho, H. N. J., Tsai, M.-J., Wang, C.-Y. & Tsai, C.-C. (2014). Prior knowledge and online inquiry-based science reading: Evidence from eye-tracking. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-013-9489-6.
12. Jarodzka, H., Scheiter, K., Gerjets, P. & van Gog, T. (2010). In the eyes of the beholder: How experts and novices interpret dynamic stimuli. Learning and Instruction, 20, 146-154.
13. Kwon, Y.-J. & Lawson, A. E. (2000). Linking brain growth with scientific reasoning ability and conceptual change during adolescence. Journal of Research in Science Teaching, 37(1), 44-62.
14. Lai, M. L., Tsai, M.-J., Yang, F.-Y., Hsu, C.-Y., Liu, T. C., Lee, S. W. Y., Lee, M.-H., Chiou, G. L., Liang, J. C. & Tsai, C.-C. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90-115.
15. Lawson, A. E. (1986). A neurological model of sensory-motor problem solving with possible implications for higher-order cognition and instruction. Journal of Research in Science Teaching, 23, 503-522.
16. Lawson, A. E. (2003). The neurological basis of learning, development and discovery: Implications for teaching science and mathematics. Dordrecht: Kluwer.
17. Lawson, A. E. (2004). Reasoning and brain function. In R. J. Sternberg & J. P. Leighton (Eds.), The nature of reasoning. New York: Cambridge University Press.
18. Lee, S. W. Y., Tsai, C.-C., Wu, Y. T., Tsai, M.-J., Liu, T. C., Huang, F. K., Lai, C. H., Liang, J. C., Wu, H. C. & Chang, C. Y. (2011). Internet-based science learning: A review of journal publications. International Journal of Science Education, 33(14), 1893-1925.
19. Lin, J. J. H. & Lin, S. S. J. (2014). Cognitive load for configuration comprehension in computer-supported geometry problem solving: An eye movement perspective. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-013-9479-8.
20. Liu, C. J. & Chiang, W. W. (2014). Theory, method and practice of neuroscientific findings in science education. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-013-9482-0.
21. Longo, P., Anderson, O. R. & Wicht, P. (2002). Visual thinking networking promotes problem-solving achievement for 9th grade Earth Science students. Electronic Journal of Science Education, 7, 1-50.
22. Norton, A. & Deater-Deckard, K. (2014). Mathematics in mind, brain, and education: A neo-Piagetian approach. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-014-9512-6.
23. Schrag, F. (2011). Does neuroscience matter for education? Educational Theory, 61, 221-236.
24. She, H. C. & Chen, Y. Z. (2009). The impact of multimedia effect on science learning: Evidence from eye movements. Computers & Education, 53(4), 1297-1307.
25. Susac, A., Bubic, A., Kaponja, J., Planinic, M. & Palmovic, M. (2014). Eye movements reveal students' strategies in simple equation solving. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-014-9514-4.
26. Tsai, M.-J., Hou, H. T., Lai, M. L., Liu, W.-Y. & Yang, F. Y. (2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58(1), 375-385.
27. van Gog, T. & Scheiter, K. (2010). Eye tracking as a tool to study and enhance multimedia learning. Learning and Instruction, 20, 95-99.
28. Waisman, I., Leikin, M., Shaul, S. & Leikin, R. (2014). Brain activity associated with translation between graphical and symbolic representations of functions in generally gifted and excelling in mathematics adolescents. International Journal of Science and Mathematics Education. doi: 10. 1007/s10763-014-9513-5.