Using Student Data Analysis Based on Eduinformatics for Criteria in Institutional Research
Abstract
“Eduinformatics” is a new field of education that combines both education and informatics. In this article, we propose new criteria by which to utilize university student data in Institutional Research (IR). We define “primary data,” which is uncombined data and “secondary data,” which is a combination of primary or secondary data. Moreover, we present examples in which primary data were used to detect elements that could not be founded through the analysis of secondary data and were pitfalls of comparative analysis performed by IR practitioners.
References
K. Takamatsu et al., “‘Eduinformatics’: A new education field promotion,” Bull. kobe Tokiwa Univ., vol. 11, pp. 27–44, 2018.
K. Takamatsu et al., “Review of Recent Eduinformatics Research,” in IEEE/IIAI International Congress on Applied Information Technology, 2019, pp. 27–32.
K. Murakami et al., “Predicting the Probability of Student Dropout through EMIR Using Data from Current and Graduate Students,” in Advanced Applied Informatics (IIAI-AAI), 2018 7th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2018, pp. 478–481.
K. Takamatsu et al., “A New Way of Visualizing Curricula Using Competencies : Cosine Similarity and t-SNE,” in Advanced Applied Informatics (IIAI-AAI), 2018 7th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2018, pp. 390–395.
MEXT, “Towards the qualitative transformation of university education in order to build a new future,” The Central Council for Education, 2012. [Online]. Available: http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo0/toushin/1325047.htm. [Accessed: 15-Mar-2018].
MEXT, “Construction of education for university,” The Central Council for Education, 2008. [Online]. Available: http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo4/houkoku/080410.htm. [Accessed: 15-Mar-2018].
K. Takamatsu et al., “SWOT analysis and complex network analysis to enhance governance in universities by collaboration between faculty and staff,” in Advanced Applied Informatics (IIAI-AAI), 2016 5th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2016, pp. 1188–1189.
K. Takamatsu et al., “A new way of visualizing curricula using competencies: Cosine similarity, multidimensional scaling methods, and scatter plotting,” in Advanced Applied Informatics (IIAI-AAI), 2017 6th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2017, pp. 192–197.
M. Seki et al., “Generic Support System Prototype in Eduinformatics for Students Selecting a Seminar,” in Advanced Applied Informatics (IIAI-AAI), 2018 7th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2018, pp. 470–473.
UNESCO, “Learning Analytics,” 2012.
D. Norris, L. Baer, J. Leonard, L. Pugliese, and P. Lefrere, “Action analytics: Measuring and improving performance that matters in higher education,” Educ. Rev., vol. 43, no. 1, p. 42, 2008.
“DQC: Data Quality Campaign.” [Online]. Available: https://dataqualitycampaign.org/. [Accessed: 01-Apr-2018].
J. P. Campbell, P. B. DeBlois, and D. G. Oblinger, “Academic analytics: A new tool for a new era,” Educ. Rev., vol. 42, no. 4, p. 40, 2007.
MEXT, “Toward the construction of undergraduate education (Japanese),” Central Council for Education, 2008. [Online]. Available: http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo4/houkoku/080410.htm. [Accessed: 15-Mar-2020].
Between Editorial Department, “Connection between three policies (Japanese),” between, p. 1, 2008.
T. Kirimura et al., “Innovate the management of teaching and learning at our own university through collaboration between academic faculty and administrative staff,” Bull. Kobe Tokiwa Univ., vol. 10, pp. 23–32, 2017.
T. Kirimura, K. Takamatsu, K. Bannaka, I. Noda, K. Mitsunari, and Y. Nakata, “Design the basic education courses as part of the innovation of management of learning and teaching at our own university through collaboration between academic and administrative faculty,” Bull. Kobe Tokiwa Univ., vol. 11, pp. 181–192, 2018.
“Syllabus of Kobe Tokiwa University,” Kobe Tokiwa University, 2017. [Online]. Available: available: http://www.kobe-tokiwa.ac.jp/univ/. [Accessed: 15-Mar-2020].
T. Kirimura et al., “Effectiveness of first year experience’s course ‘Manaburu’ at Kobe Tokiwa University for university students by using textual analysis,” Bull. Kobe Tokiwa Univ., vol. 11, pp. 193–208, 2018.
R Core Team, “R: A Language and Environment for Statistical Computing,” R Foundation for Statistical Computing, Vienna, Austria, vol. 0. p. {ISBN} 3-900051-7-0, 2017.
RStudio Team, “RStudio: Integrated Development for R. RStudio, Inc., Boston, MA. URL http://www.rstudio.com/.,” RStudio, Inc., 2015.
A. Eklund, “beeswarm: The Bee Swarm Plot, an Alternative to Stripchart.” 2016.
T. L. Weissgerber, N. M. Milic, S. J. Winham, and V. D. Garovic, “Beyond bar and line graphs: time for a new data presentation paradigm,” PLoS Biol, vol. 13, no. 4, p. e1002128, 2015.
Y. Nakata et al., “Ensuring Equal Evaluation among Teachers in First-Year Education Courses through Rubrics: A Multiple Comparison Analysis,” in International Conference on Education, Psychology, and Learning (ICEPL2018), 2018, pp. 40–46.
T. Kunihiko et al., “Web-based Support System for Students to Select Courses using Tokiwa Competencies,” in International Conference on Education, Psychology, and Learning (ICEPL2017), 2017, pp. 74–80.
K. Takamatsu et al., “A Novel Curriculum Visualization Method Using a Combination of Competencies, Cosine Similarity, Multidimensional Scaling Methods, and Scatter Plotting,” IEE Inf. Eng. Express, vol. 5, no. 1, pp. 127–143, 2019.
K. Takamatsu, K. Murakami, T. Oshiro, A. Sugiura, K. Bannaka, and Y. Nakata, “Predicting the Probability of Student’s Academic Abilities and Progress with EMIR and Data from Current and Graduated Students,” in Advanced Applied Informatics (IIAI-AAI), 2019 8th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 359–362.
Y. Nakata et al., “New Proposal to Compare Student Data in Institutional Research,” in Advanced Applied Informatics (IIAI-AAI), 2019 8th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 404–407.
K. Takamatsu et al., “Introducing new criteria for IR, using student data compared analysis based on Eduinformatics,” in Advanced Applied Informatics (IIAI-AAI), 2020 9th International Institute of Applied Informatics (IIAI) International Congress on. Institute of Electrical and Electronics Engineers (IEEE), 2020, pp. 378–384.
