(Special Issue) Database Establishment in Institutional Research and Decision-Making Support Applications
Author: Yao-Ping Peng(Department of Business Administration,Hsuan Chuang University), Feng-Chi Liu (Department of Statistics, Feng Chia University), Sheng-Hua Tuan (Departmaent of Social Work,Hsuan Chuang University)
Vol.&No.：Vol. 62, No.4
Date：December 2017
Pages：27-51
DOI：10.6209/JORIES.2017.62(4).02
Abstract：
Institutional research (IR) is expected to undergo development to conform to the needs of Taiwan’s higher education sector by enabling each university to execute IR based on its own data to conduct analysis, transfers, and applications. Thus, no common and consistent standards and regulations will govern data collection and storage. Each university develops its own IR strategy based on data and information provided by the U.S. Association of Institutional Research. IR focuses on internal data collection, analysis, and interpretation from higher education institutions. Objects of study include students, faculty members, support staff, and the school environment. Institutional researchers analyze information of value to facilitate senior leaders’ decision-making and institutional development. However, the effectiveness of data analysis applications is based on data collection and storage; therefore, the completeness of the initial data and data system construction is the first step in IR development to determine whether subsequent data analysis and interpretation are valuable for decision-making. Therefore, this study investigated the data-processing elements of libraries and information science and the design and models of IR databases at a private university in Taiwan. The study objective was to ascertain the method of massive educational information use and storage in the modern higher education sector and to determine the optimal mode of analyzing concepts and references for institutional research professionals. The outcomes of this study could contribute to university decision-making processes.
Keywords：database management, decision-making support system, educational data, institutional research
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References：
- 玄奘大學(2015a)。「大學提升校務專業管理能力計畫」計畫書。新竹市：作者。【Hsuan Chuang University. (2015a). The project of improving professional institution administration capability. Hsinchu, Taiwan: Author.】
- 玄奘大學（2015b）。行政工作標準書。取自http://www.hcu.edu.tw/sec/sec/zh-tw/33FC58DF5D0 D41CF8BD542A63A051CCB/01B06861C0FC441E8503544166907584/xuan-1zang-da-xue-jiao-wu-hui-yi-gong-zuo-liu-cheng-biao-zhun-shu 【Hsuan Chuang University. (2015b). Administrative work procedure. Retrieved from http://www.hcu.edu.tw/sec/sec/ zh-tw/33FC58DF5D0D41CF8BD542A63A051CCB/01B06861C0FC441E8503544166907584/xuan-1zang-da-xue-jiao-wu-hui-yi-gong-zuo-liu-cheng-biao-zhun-shu】
- 李欣宜（2015）。美國Top 4技術長寶立明：大數據即將在五年內消失。數位時代。取自http://www.bnext.com.tw/article/35404/BN-ARTICLE-35404 【Lee, X.-Y. (2015). Top 4 American chief technology officer stephen brobst: Bigdata will dispear during next five years. BUSINESS NEXT. Retrieved from http://www.bnext.com.tw/article/35404/BN-ARTICLE-35404】
- 陳鏗任（2014）。大學院校應用學習分析之概況。教育資料與圖書館學，51（4），1-36。doi:10.6120/JoEMLS.2014.514/0612.RV.CM 【Chen, K.-Z. (2014). Review of the development of learning analytics applied in college-level institutes. Journal of Educational Media & Library Sciences, 51(4), 1-36. doi:10.6120/JoEMLS.2014.514/0612.RV.CM】
- 曾元顯（2014）。自動化資訊組織與主題分析近二十年來的研究與發展［特刊］。教育資料與圖書館學，51，3-26。doi:10.6120/JoEMLS.2014.51S/0652.RV.AM 【Tseng, Y.-H. (2014). Research and development on automatic information organization and subject analysis in recent decades [Special issue]. Journal of Educational Media & Library Sciences, 51, 3-26. doi:10.6120/ JoEMLS.2014.51S/0652.RV.AM】
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- 玄奘大學(2015a)。「大學提升校務專業管理能力計畫」計畫書。新竹市：作者。【Hsuan Chuang University. (2015a). The project of improving professional institution administration capability. Hsinchu, Taiwan: Author.】
- 玄奘大學（2015b）。行政工作標準書。取自http://www.hcu.edu.tw/sec/sec/zh-tw/33FC58DF5D0 D41CF8BD542A63A051CCB/01B06861C0FC441E8503544166907584/xuan-1zang-da-xue-jiao-wu-hui-yi-gong-zuo-liu-cheng-biao-zhun-shu 【Hsuan Chuang University. (2015b). Administrative work procedure. Retrieved from http://www.hcu.edu.tw/sec/sec/ zh-tw/33FC58DF5D0D41CF8BD542A63A051CCB/01B06861C0FC441E8503544166907584/xuan-1zang-da-xue-jiao-wu-hui-yi-gong-zuo-liu-cheng-biao-zhun-shu】
- 李欣宜（2015）。美國Top 4技術長寶立明：大數據即將在五年內消失。數位時代。取自http://www.bnext.com.tw/article/35404/BN-ARTICLE-35404 【Lee, X.-Y. (2015). Top 4 American chief technology officer stephen brobst: Bigdata will dispear during next five years. BUSINESS NEXT. Retrieved from http://www.bnext.com.tw/article/35404/BN-ARTICLE-35404】
- 陳鏗任（2014）。大學院校應用學習分析之概況。教育資料與圖書館學，51（4），1-36。doi:10.6120/JoEMLS.2014.514/0612.RV.CM 【Chen, K.-Z. (2014). Review of the development of learning analytics applied in college-level institutes. Journal of Educational Media & Library Sciences, 51(4), 1-36. doi:10.6120/JoEMLS.2014.514/0612.RV.CM】
- 曾元顯（2014）。自動化資訊組織與主題分析近二十年來的研究與發展［特刊］。教育資料與圖書館學，51，3-26。doi:10.6120/JoEMLS.2014.51S/0652.RV.AM 【Tseng, Y.-H. (2014). Research and development on automatic information organization and subject analysis in recent decades [Special issue]. Journal of Educational Media & Library Sciences, 51, 3-26. doi:10.6120/ JoEMLS.2014.51S/0652.RV.AM】
- 曾元顯（2016）。校務研究資料庫的建構與分析應用。當代教育研究季刊，24（1），107-134。doi:10.6151/CERQ.2016.2401.04 【Tseng, Y.-H. (2016). Development and application of databases for institutional research and analysis. Contemporary Education Research Quarterly, 24(1), 107-134. doi:10.6151/CERQ.2016.2401.04】
- 鍾沛原、曾賢寶、楊嘉麗、李柏毅、蔡一郎（2014）。電腦機房異地備援機制參考指引。取自http://download.icst.org.tw/attachfilecomm/我國電腦機房異地備援機制參考指引.pdf 【Chung, P.-Y., Tseng, H.-B., Yang, C.-L., Lee, P.-I, & Tsai, I-L. (2014). Guideline for computer lab mechanism. Retrieved from http://download.icst.org.tw/attachfilecomm/我國電腦機房異地備援機制參考指引.pdf】
- Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2013). Factors influencing beliefs for adoption of a learning analytics tool: An empirical study. Computers & Education, 62, 130-148. doi:10.1016/j.compedu.2012.10.023
- Baradwaj, B. K., & Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2(6), 63-69. doi:10. 14569/IJACSA.2011.020609
- Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. doi:10.1111/bjet.12230
- Duan, Y., Cao, G., One, V., & Woolley, M. (2013, November). Big data in higher education: An action research on managing student engagement with business intelligence. Paper presented at the Second International Conference on Emerging Research Paradigm in Business and Social Science, Middlesex University, Dubai.
- Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. doi:10.1504/IJTEL.2012.051816
- Hagerty, J. (2006). Expanding BI beyond the power user. Boston, MA: AMR Research Alert.
- Han, J., & Kamber, M. (2002). Data mining, concepts and techniques. San Francisco, CA: Morgan Kaufmann. doi:10.1145/565117.565130
- Howard, R. D., McLaughlin, G. W., & Knight, W. E. (2012). The handbook of institutional research. San Francisco, CA: John Wiley & Sons.
- Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38(5), 758-773. doi:10.1080/03075079.2011.598505
- King, B. R. (2016). Making the connections across institutional types and academic programs: Recommendations for institutional research practice and future research. New Directions for Institutional Research, 168, 101-105. doi:10.1002/ir.20164
- Kirby, Y. K., & Floyd, N. D. (2016). Maximizing institutional research impact through building relationships and collaborating within the institution. New Directions for Institutional Research, 166, 47-59. doi:10.1002/ir.20130
- Knight, W. E., & Leimer, C. L. (2010). Will ir staff stick? An exploration of institutional researchers’ intention to remain in or leave their jobs. Research in Higher Education, 51(2), 109-131. doi:10. 1007/s11162-009-9152-9
- Lane, J. E., & Johnstone, D. B. (Eds.). (2013). Higher education systems 3.0: Harnessing systemness, delivering performance. Albany, NY: SUNY Press.
- Li, J., & Xu, B. (2010, August). ETL tool research and implementation based on drilling data warehouse. Paper presented at the 7th International Conference On Fuzzy Systems and Knowledge Discovery (FSKD), Yantai, China.
- Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford, NY: Oxford University Press. doi:10.1016/S0040- 1625(96)00091-1
- Park, B. K., Han, H., & Song, I. Y. (2005, August). XML-OLAP: A multidimensional analysis framework for XML warehouses. Paper presented at the 6th International Conference on Data Warehousing and Knowledge Discovery, Copenhagen, Denmark.
- Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9-20. doi:10.24059/0lj.v16i3. 267
- Powell, B. A., Gilleland, D. S., & Pearson, L. C. (2012). Expenditures, efficiency, and effectiveness in US undergraduate higher education: A national benchmark model. The Journal of Higher Education, 83(1), 102-127. doi:10.1353/jhe.2012.0005
- Pritchard, R. M. O., Klumpp, M., & Teichler, U. (2015). Diversity and excellence in higher education: Can the challenges be reconciled? Rotterdam, The Netherlands: Sense. doi:10.1007/ 978-94-6300-172-4
- Reddy, G. S., Srinivasu, R., Rao, M. P. C., & Rikkula, S. R. (2010). Data warehousing, data mining, OLAP and OLTP technologies are essential elements to support decision-making process in industries. International Journal on Computer Science and Engineering, 2(9), 2865-2873.
- Rios-Aguilar, C. (2015). Using big (and critical) data to unmask inequities in community colleges. New Directions for Institutional Research, 163, 43-57. doi:10.1002/ir.20085
- Satkaur, A. M. (2013). A review paper on scope of ETL in retail domain. International Journal of Advanced Research in Computer Science and Software Engineering, 3(5), 1209-1213.
- Serban, A. M., & Luan, J. (2002). Management: Building a competitive advantage in higher education. San Francisco, CA: Jossey-Bass.
- Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector: Policy and strategy for systems-level deployment of learning analytics. Sydney, Australian: Office for Learning and Teaching.
- Sweatman, R. A. (2015). IR’s role in piloting an assessment model: Coordination, consultation and compromise. New Directions for Institutional Research, 165, 43-59. doi:10.1002/ir.20123
- Terenzini, P. T. (2013). “On the nature of institutional research” revisited: Plus ça change…? Research in Higher Education, 54(2), 137-148. doi:10.1007/s11162-012-9274-3
- Tulasi, B. (2013). Significance of big data and analytics in higher education. International Journal of Computer Applications, 68(14), 21-23. doi:10.5120/11648-7142
- Volkwein, J. F. (1999). The four faces of institutional research. New Directions for Institutional Research, 104, 9-19. doi:10.1002/ir.10401
- Zhang, Y., Li, J., Lu, W., & Shao, H. (2017). Migrating dashboards from SAS® BI dashboard to SAS® Visual Analytics. Cary, NC: SAS Institute.
- Zhao, C.-M., & Luan, J. (2006). Data mining: Going beyond traditional statistics. New Directions for Institutional Research, 131, 7-16. doi:10.1002/ir.184