Abstract
The assessment of students’ learning ability for career guidance in the future is a huge challenge. The development stage of students’ learning ability is considered from the sixth grade to the ninth grade. Student’s transcripts from grade 6 to grade 9 are used to assess students’ learning abilities. A transcript comparison of grades 6 through 9 is essential for each parent and analyst from there they can guide their children to comprehensive development of knowledge. The objective of this paper is to visually analyze student data using visual analysis approach, proposes a visual analysis system for data discovery with many variables (VAS), a visual data analysis model, visual data analysis criteria, visual data variables, multidimensional cube representing student data, and some visual data analysis questions based on visual graphs related to Junior High School students (JHSSs). Visual analysis of student data helps parents or analysts observe and extract useful information that they interact visual on visual graphs by asking themselves or answering the visual data analysis questions themselves when observing visual graphs by the retina to guide their children to choose the right knowledge chain and future jobs. Visual graphs represent the correlation between subjects and especially the comparison of a subject in the academic years together to help parents and analysts see clearly the trend of the development of students’ learning abilities by visual data analysis model.
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References
Stuart, C.K., Jock, M., Ben, S.: Readings in Information Visualization: Using Vision to Think. Academic Press, Norwell (1999)
Card, S.T., Mackinlay, J.D., Scheiderman, B.: Readings in Information Visualization, Using Vision to Thinks. Graphic Press, Cheshire (1990)
Stevens, S.S.: On the theory of scales of measurement. Science 103, 677–680 (1946)
Bertin, J.: General theory, from semiology of graphics. In: Dodge, M., Kitchin, R., Perkins, C. (eds.) The Map Reader. Theories of Mapping Practice and Cartographic Representation. Wiley, pp. 8–16 (2011)
Nguyen, H.T., Tran, A.V.T., Nguyen, T.A.T., Vo, L.T., Tran, P.V.: Multivariate cube integrated retinal variable to visually represent multivariable data. EAI Endorsed Trans. Context.-Aware Syst. Appl. 4, 1–8 (2017). https://doi.org/10.4108/eai.6-7-2017.152757
Thi Nguyen, H., Thi Pham, T.M., Thi Nguyen, T.A., Thi Tran, A.V., Vinh Tran, P., Van Pham, D.: Two-stage approach to classifying multidimensional cubes for visualization of multivariate data. In: Cong Vinh, P., Alagar, V. (eds.) ICCASA/ICTCC -2018. LNICST, vol. 266, pp. 70–80. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-06152-4_7
Thomas, J.J., Cook, K.A.: Illuminating the path the research and development agenda for visual analytics. National Visualization and Analytics Center – NVAC (2005)
Sacha, D., Stoffel, A., Stoffel, F., Kwon, B.C., Ellis, G., Keim, D.A.: Knowledge generation model for visual analytics. IEEE Trans. Vis. Comput. Graph. 20(12), 1604–1613 (2014). https://doi.org/10.1109/tvcg.2014.2346481
Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the Information Age: Solving Problems with Visual Analytics. Eurographics Association, Goslar (2010). ISBN 978-3-905673-77-7
Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, John T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70956-5_7
Wijk, J.J.: The value of visualization. In: IEEE Visualization, p. 11 (2005)
Card, S.T., Mackinlay, J.D., Scheiderman, B.: Readings in Information Visualization, Using Vision to Think. Academic Press, Norwell (1999)
Bertini, E., Tatu, A., Keim, D.: Quality metrics in high-dimensional data visualization: an overview and systematization. IEEE Trans. Vis. Comput. Graph. 17(12), 2203–2212 (2011). https://doi.org/10.1109/TVCG.2011.229
von Szent-Gyorgyi, A.: American (Hungarian-born) Biochemist (Nobel Laureate, 1937) who said: “Discovery consists of seeing what everybody has seen and thinking what nobody has thought” ed. by Irving Good, The Scientist Speculates (1962)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)
Ministry of Education and Training (MET), Abstract: “Released circular regulations in assessing and ranking of Secondary School Students and High School Students” in menu of legal documents at page 20, Effect: in force. https://en.moet.gov.vn/document/legal-documents/Pages/detail.aspx?ItemID=744. Published date: 12/15/2011, Date to: 01/26/2012, Official number: 58/2011/TT-BGDĐT. Accessed Apr 2019
Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data – A Systematic Approach, p. 2006. Springer, Heidelberg (2006)
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Van Pham, D., Vinh Tran, P. (2019). A System and Model of Visual Data Analytics Related to Junior High School Students. In: Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICCASA ICTCC 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-030-34365-1_9
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