NEW ANALYSIS OF VISUALIZATION IN EDUINFORMATICS USING A NETWORK WITH PARAMETRIC AND NONPARAMETRIC CORRELATION COEFFICIENTS WITH THRESHOLD
DOI:
https://doi.org/10.20319/pijss.2018.41.619630Keywords:
Dimension Reduction, Correlation Coefficient, Visualization, EduinformaticsAbstract
Eduinformatics, a new term coined by us, is a field that combines education and informatics, and novel techniques will need to be developed for this field. Earlier, we developed a new visualization method to visualize the curriculum of Kobe Tokiwa University using multidimensional scaling (MDS) and a scatter plot. In this study, our focus is on methods to analyze the relationships between answers to questions in eduinformatics questionnaires. MDS methods are very useful, but have limitations in that their results are difficult to interpret. To facilitate the interpretation of these results, we develop a new visualization method using a network with both parametric and non-parametric correlation coefficients with a threshold (VNCC). VNCC has nine steps. We apply the VNCC method to research on nursing education, and provide an example of the visualization of the result. VNCC methods will be useful in dealing with qualitative research in eduinformatics.References
Bingham, E., Bingham, E., Mannila, H., & Mannila, H. (2001). Random projection in dimensionality reduction: applications to image and text data. International Conference on Knowledge Discovery and Data Mining (KDD), 245-250. http://doi.org/10.1145/502512.502546
Donoho, D. L., & Grimes, C. (2003). Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences, 100(10), 5591-5596. http://doi.org/10.1073/pnas.1031596100
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. http://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Hama, S., Takamatsu, K., Nakata, Y., & Adachi, R. (2018). Relationship between lip closing force and oral function in healthy women college students. The Journal of Educational Conference on All Japan Colleges of Dental Hygiene, 7, 21-28.
Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1-27. http://doi.org/10.1007/BF02289565
Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., … Zinzen, R. P. (2017). The Drosophila embryo at single-cell transcriptome resolution. Science, 358(6360), 194–199. http://doi.org/10.1126/science.aan3235
Kullback, S., & Leibler, R. A. (1951). JSTOR: The Annals of Mathematical Statistics, Vol. 22, No. 1 (Mar., 1951), pp. 79-86. The Annals of Mathematical Statistics. Retrieved from http://www.jstor.org/stable/10.2307/2236703%5Cnpapers2://publication/uuid/A65B3271-44DC-42DC-87B5-A0C3A91EBFA8
Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. Philosophical Magazine Series 6, 2(11), 559-572. http://doi.org/10.1080/14786440109462720
Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323-2326. http://doi.org/10.1126/science.290.5500.2323
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498-2504. http://doi.org/10.1101/gr.1239303
Shoji, Y., Ozaki, Y., Sasao, H., Takamatsu, K., & Nakata, Y. (2018). Significance of pediatric nursing practice in which graduates of our university as clinical nurses teach: From the results of questionnaire survey for students. Bulletin of Kobe Tokiwa University, 11, in press.
Takamatsu, K., Murakami, K., Kirimura, T., Bannaka, K., Noda, I., Yamasaki, M., Lim, R-J. W., Mitsunari, K., Tadashi, N., & Nakata, Y. (2017). A new way of visualizing curricula using competencies: Cosine similarity, multidimensional scaling methods, and scatter plotting. Advanced Applied Informatics (IIAI-AAI), 2017 6th IIAI International Congress On. IEEE, http://doi.ieeecomputersociety.org/10.1109/IIAI-AAI.2017.29.
Takamatsu, K., Murakami, K., Lim, R.-J. W., & Nakata, Y. (2017). Novel visualization for curriculum in silico using syllabus by a combination of cosine similarity, multidimensional scaling methods, and scatter plot: Dynamic curriculum mapping (DCM) for syllabus. Bulletin of Kobe Tokiwa University, 10, 99-106, http://doi.org/10.20608/00000396.
Takamatsu, K., Murakami, K., Kirimura, T., Bannaka, K., Noda, I., Lim, R-J. W., Mitsunari, K., Seki, M., Matsumoto, E., Bohgaki, M., Imanishi, A., Omori, M., Adachi, R., Yamasaki, M., Sakamoto, H., Takao, K., Asahi, J., Nakamura, T., & Nakata, Y. (2018). “Eduinformatics”: A new education field promotion. Bulletin of Kobe Tokiwa University, 11, 27-44.
Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323. http://doi.org/10.1126/science.290.5500.2319
Van Der Maaten, L. J. P., & Hinton, G. E. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605. http://doi.org/10.1007/s10479-011-0841-3
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442. http://doi.org/10.1038/30918
Zhang, Z., & Wang, J. (2006). MLLE: Modified locally linear embedding using multiple weights. Advances in Neural Information Processing Systems, 1593–1600. Retrieved from http://cognet.mit.edu/library/books/mitpress/0262195682/cache/chap200.pdf
Zhang, Z. Y., & Zha, H. Y. (2004). Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. Journal of Shanghai University, 8(4), 406–424. http://doi.org/10.1007/s11741-004-0051-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2018 Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.