Abstract
Science educational neuroscience is a new discipline that integrates science education, psychology, and biological processes. The potential of science educational neuroscience is to bridge the gap of research trends, methodologies, and applications between science education and neuroscience, and to translate research challenges into opportunities. In this area, researchers combine science education and the fundamental techniques of cognitive neuroscience such as electroencephalograms (EEG), event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI) to provide specific and objective suggestions to science learners, educators, and curriculum designers. In recent years, a lot of educational neuroscience researchers have focused on students’ cognitive abilities and emotions by analyzing neuroscience data. However, few studies have highlighted students’ science learning abilities and strategies by engaging neuroscience. Furthermore, the orientations of methodology, data analysis, and philosophy differ between science education and neuroscience. Although there are many research challenges to face, there are some studies that provide practical implications for engaging neuroscience in science education.
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References
Baker, D. P., Salinas, D., & Eslinger, P. J. (2012). An envisioned bridge: Schooling as a neurocognitive developmental institution. Developmental Cognitive Neuroscience, 2(1), 6–17.
Bakhurst, D. (2008). Minds, brains and education. Journal of Philosophy of Education, 42(3–4), 415–432.
Bruer, J. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26, 4–16.
Byrnes, J. P. (2001). Minds, brains, and learning: Understanding the psychological and educational relevance of neuroscientific research. New York: Guilford.
Carew, T. J., & Magsamen, S. H. (2010). Neuroscience and Education: An ideal partnership for producing evidence-based solutions to guide 21th century learning. Neuron, 67(5), 685–688.
Cassady, J. C., & Gridley, B. E. (2005). The effects of online formative and summative assessment on test anxiety and performance. The Journal of Technology, Learning, and Assessment, 4(1), 1–31.
Coch, D., & Ansari, D. (2009). Thinking about mechanisms is crucial to connecting neuroscience and education. Cortex, 45, 546–547.
Economides, A. A. (2009). Conative feedback in computer-based assessment. Computers in the Schools, 26(3), 207–223.
Filipowicz, A. (2006). From positive affect to creativity: The surprising role of surprise. Creativity Research Journal, 18(2), 141–152.
George, J. M., & Zhou, J. (2002). When openness to experience and conscientiousness are related to creative behavior: An interactional approach. Journal of Applied Psychology, 86(3), 513–524.
Gilbert, J. K., & Treagust, D. (2009). Multiple representations in chemical education. Dordrecht: Springer.
Ho, M.-C., Chou, C.-Y., Huang, C.-F., Lin, Y.-T., Shih, C.-S., Han, S.-Y., et al. (2012). Age-related changes of task-specific brain activity in normal aging. Neuroscience Letters, 507, 78–83.
Howard-Jones, P. (2008). Philosophical challenges for researchers at the interface between neuroscience and education. Journal of Philosophy of Education, 42(3–4), 361–380.
Huang, C.-F., & Liu, C.-J. (2012). An event-related potentials study of mental rotation in identifying chemical structural formulas. European Journal of Educational Research, 1(1), 37–54.
Huang, C. F., & Liu, C. J. (2013). The effects of chemical element symbols in identifying 2D chemical structural formulas. New Educational Review, 31(1), 40–50.
Huang, C.-F., Shen, M.-H., & Liu, C.-J. (2008 February). Explore the influences of positive emotions on scientific creativity. Paper presented at the meeting of the Conference of Asian Science Education (CASE2008), Kaohsiung, Taiwan.
Huang, Y. M., Liu, C. J., Shadiev, R., Shen, M. H., & Hwang, W. Y. (2014). Investigating an application of speech-to-text recognition: a study on visual attention and learning behavior. Journal of Computer Assisted Learning. doi: 10.1111/jcal.12093.
Korakakis, G., Pavlatou, E. A., Palyvos, J. A., & Spyrellis, N. (2009). 3D visualization types in multimedia applications for science learning: A case study for 8th grade students in Greece. Computers & Education, 52, 390–401.
Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208(4450), 1335–1342.
Liu, C. J., Hou, I. L., Chiu, H. L. & Treagust, D. F. (2014a). An exploration of secondary students’ mental states when learning about acids and bases. Research in Science Education, 44(1), 133–154.
Liu, C. J., Huang, C. F., Huang, R. Y., Shih, C. S., Ho, M. C., & Ho, H. C. (2014b). Solving reality problems by using mutual information analysis. Mathematical Problems in Engineering. doi:10.1155/2014/631706.
Liu, C. J., Huang, C. F., Liu, M. C., Chien, Y. C., Lai, C. H., & Huang, Y. M. (2015). Does gender influence emotions resulting from positive applause feedback in self-assessment testing? Evidence from neuroscience. Educational Technology & Society, 18(1), 337–350.
Mason, L. (2009). Bridging neuroscience and education: A twoway path is possible. Cortex, 45, 548–549.
Mayer, T. (2001). Multimedia learning. New York: Cambridge University Press.
Moridis, C. N., & Economides, A. A. (2012). Applause as an achievement-based reward during a computerised self-assessment test. British Journal of Educational Technology, 43(3), 489–504.
Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218.
Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Experimental Social Psychology, 19, 123–205.
Prudy, N., & Morrison, H. (2009). Cognitive neuroscience and education: Unravelling the confusion. Oxford Review of Education, 35(1), 99–109.
Shubbar, K. E. (1990). Learning the visualization of rotations in diagrams of three-dimensional structural formulas. Research in Science and Technological Education, 8(2), 145–153.
Tsaparlis, G., Kolioulis, D., & Pappa, E. (2010). Lower-secondary introductory chemistry course: A novel approach based on science-education theories, with emphasis on the macroscopic approach, and the delayed meaningful teaching of the concepts of molecule and atom. Chemistry Education Research and Practice, 11, 107–117.
Willingham, D. T. (2009). Why don’t students like school: A cognitive scientist answers questions about how the mind works and what it means for the classroom. San Francisco: Jossey-Bass Press.
Wu, H. K., Krajcik, J. S., & Soloway, E. (2001). Promoting conceptual understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821–842.
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Liu, CJ., Huang, CF. (2016). Innovative Science Educational Neuroscience: Strategies for Engaging Brain Waves in Science Education Research. In: Chiu, MH. (eds) Science Education Research and Practices in Taiwan. Springer, Singapore. https://doi.org/10.1007/978-981-287-472-6_12
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DOI: https://doi.org/10.1007/978-981-287-472-6_12
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