Development and application of databases for institutional research and analysis

Yuen Hsien Tseng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This article elaborates on the possible best practice of developing databases for institutional research and analysis, based on the knowledge of Educational Science, Library Science, and Information Engineering, years of experience in developing educational databases, and a recent survey of related technology and products. Several developing options are compared to show their benefits and disadvantages under different conditions. Three representative analysis tasks are reported to verify and show the synergy of the mentioned ideas and experience. In particular, this article proposes a sustainable workflow: (1) data collection and aggregation, (2) cataloguing, (3) regulation, (4) archiving, and (5) usage, and describes their must-known caveats. The application situations of data normalization and de-normalization are described. Capability of domestic vendors of related products is briefly mentioned based on a proof-ofconcept testing. And finally, real-world institutional analyses are conducted to share our experience. Overall, the first four processes in the above workflow are most timeconsuming and costly. Once data have been well prepared, recent visualization analysis tools allow users to easily discover meaningful patterns and inspire hypotheses, and allow them to explore the database to find evidence to support their hypotheses and decisions. In the future, we expect that event evolution simulation techniques, which allow users to foresee the results given various input scenarios, could play an important role in educational data analysis, in addition to the maturing data visualization tools.

Original languageEnglish
Pages (from-to)107-134
Number of pages28
JournalContemporary Educational Research Quarterly
Issue number1
Publication statusPublished - 2016 Mar 30


  • Data consistency
  • Data literacy
  • Data normalization
  • Data warehouse
  • Visualization analysis

ASJC Scopus subject areas

  • Education


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