Translated Titles

The Application of Big Data for Teaching and Learning





Key Words

商業智慧與分析 ; 商業智慧與教育 ; 巨量資料 ; 資料倉儲 ; Business intelligence and Analytics ; Business intelligence and Education ; Big data ; Data warehouse



Volume or Term/Year and Month of Publication


Academic Degree Category




Content Language


Chinese Abstract


English Abstract

Within big data analysis growing, using educational data mining will cluster unstructured data into useful information. Also, it influences the traditional mode of teaching and has a disruptive innovation of education. Because of data source distributing and formatting inconsistencies, it’s becoming difficulties of data collection and integration for educational data mining. However, we need a solution to resolve simple data obtained problem and low quality data issue has become a challenge. In this study, we proposed student data warehouse processes which solved school data issue of dispersed storage, data format inconsistent, different definitions of the data in each department and low quality data without affecting school business execution. This model has three stages for solving above disadvantages. First, construct data warehouse based on student view. Second, explore the data sources and build synchronization mechanisms. Finally, according to analysis demands building data mart. Those processes will implement in the school for case. Through those processes to verify this model’s feasibility, we supposed to aggregate difficulties and precautions at every stage of the process encountered, as an integrated reference for student data.

Topic Category 商學院 > 資訊管理研究所
社會科學 > 管理學
  1. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734–749.
  2. Aghabozorgi, S., Mahroeian, H., Dutt, A., Wah, T. Y., & Herawan, T. (2014). An Approachable Analytical Study on Big Educational Data Mining. In Computational Science and Its Applications–ICCSA 2014, Springer, (pp. 721–737).
  3. Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), 3–17.
  4. Bakshi, K. (2012). Considerations for big data: Architecture and approach. 2012 IEEE Aerospace Conference, 1–7.
  5. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98.
  6. Chen, H. (2009). AI, E-government, and Politics 2.0. Intelligent Systems, IEEE, 24(5), 64–86.
  7. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165–1188.
  8. Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683–695.
  9. Cuzzocrea, A., Song, I.-Y., & Davis, K. C. (2011). Analytics over large-scale multidimensional data: the big data revolution! Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP, 101–104 .
  10. Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the world-wide web. Communications of the ACM, 54(4), 86–96.
  11. Easton, G. (2010). Critical realism in case study research. Industrial Marketing Management, 39(1), 118–128.
  12. Eisenhardt, K. M. (1989). Building theories from case study research. Academy of management review, 14(4), 532–550.
  13. Fathi, R. (2014). The Effect of Entrepreneurship Education on Business Intelligence of Management Students of Islamic Azad University of Elam. International Letters of Social and Humanistic Sciences, (19), 24–34.
  14. Hajlaoui, J. E., & Hamdani, N. (2014). Active data warehouse: Review, challenges and issues. 2014 World Symposium on Computer Applications & Research (WSCAR), 1–6.
  15. Kurniawan, Y., & Halim, E. (2013). Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits). 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 98–103.
  16. Manjunath, T. N., Hegadi, R. S., Umesh, I. M., & Ravikumar, G. K. (2012). Realistic Analysis of Data ware housing and Datamining Application in Education Domain. International Journal of Machine learning and computing, 2(4), 419-422.
  17. Masters, A., & Michael, K. (2007). Lend me your arms: The use and implications of humancentric RFID. Electronic Commerce Research and Applications, 6(1), 29–39.
  18. Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research in Open and Distance Learning, 15(4), 306-331.
  19. Sun, L., Hu, M., Ren, K., & Ren, M. (2013). Present Situation and Prospect of Data Warehouse Architecture under the Background of Big Data. IEEE 2013 International Conference on Information Science and Cloud Computing Companion (ISCC-C), 529–535.
  20. Wang, F., Lee, N., Hu, J., Sun, J., Ebadollahi, S., & Laine, A. F. (2013). A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(2), 272–285.
  21. Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96–99.
  22. Zhang, Z., Li, W., Wu, Z., & Tan, W. (2012). Towards an Automata-Based Semantic Web Services Composition Method in Context-Aware Environment. In International Conference on Services Computing (SCC), 2012 IEEE Ninth (pp. 320–327).
  23. 英文部分:
  24. Berta, D.-A. (2012). Business Intelligence in education. In Conference proceedings of「 eLearning and Software for Education」(eLSE), 62–66.
  25. Cortez, P., & Silva, A. M. G. (2008). Using data mining to predict secondary school student. In the Proceedings of 5th Annual Future Business Technology Conference, 5-12.
  26. Ferguson, M. (2012). Architecting a big data platform for analytics. A Whitepaper Prepared for IBM.
  27. Lee, Y., Madnick, S., Wang, R., Wang, F., & Zhang, H. (2014). A Cubic Framework for the Chief Data Officer: Succeeding in a World of Big Data. MIS Quarterly Executive, 13(1), 1-13.
  28. Manjunath, T. N., Hegadi, R. S., & Ravikumar, G. K. (2010). Analysis of Data Quality Aspects in DataWarehouse Systems. IJCSIT) International Journal of Computer Science and Information Technologies, 2(1), 477–485.
  29. Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal (KM&EL), 4(3), 236–247.
  30. Mcafee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big Data. The management revolution. Harvard Bus Rev, 90(10), 61–67.
  31. Patterson, D. A. (2008). Technical Perspective: The Data Center is the Computer. Communications of the ACM, 51(1), 105.
  32. Ray, S. (2013). Big Data it Education. In GRAVITY Issue. Retrieved From http://www.glgravity.org/pdf/issue20v2.pdf
  33. Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. IEEE 2013 International Conference on Collaboration Technologies and Systems (CTS), 42–47.
  34. The Economist . (2010). “The Data Deluge,” Special Report on Managing Information, Technology Section, February 25.
  35. Thusoo, A., Sarma, J. S., Jain, N., Shao, Z., Chakka, P., Zhang, N., … Murthy, R. (2010). Hive-a petabyte scale data warehouse using hadoop. 2010 IEEE 26th International Conference on Data Engineering (ICDE), 996–1005.
  36. Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., … others. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.
  37. Yin, R. K.(2013). Case study research: Design and methods. Sage publications.
  38. Zaslavsky, A., Perera, C., & Georgakopoulos, D. (2013). Sensing as a service and big data. In International Conference on Advances in Cloud Computing (ACC- 2012), 21–29.
  39. 中文部分:
  40. 林秀姿 (2014)。教部投入Big data 6大學明年試辦。聯合新聞網。取自 http://mag.udn.com/mag/edu/storypage.jsp?f_ART_ID=519175。
  41. 林俊宏(譯) (2013)。大數據(原作者:Viktor Mayer-Schonberger & Kenneth Cukier)。台北市: 遠見天下文化。
  42. 林俊宏(譯) (2014)。大數據-教育篇(原作者:Viktor Mayer-Schonberger & Kenneth Cukier)。台北市: 遠見天下文化。
  43. 胡世忠 (2013)。雲端時代的殺手級應用。台北市:天下雜誌。