應用大數據於教學與學習之研究

近幾年來，巨量資料正在進入教育的各個層面中，運用教育資料探勘將凌亂的資料變成有用的資訊，使得教育不在只是你聽我講的互動，巨量資料將顛覆傳統教學模式。學校已經擁有足夠且大量的學生資料，而且學校也是重視學生的學習成效，在巨量分析前，都需要有一個完善能夠儲存這些龐大資料的資料庫，但學校因為資料分散儲存、資料格式不一致和各單位對資料定義上的不同，導致資料蒐集與整合上出現困難，如何解決資料上取得不易及資料品質低落的問題成了一大挑戰。本研究設計一個如何在不影響個案學校業務執行的情況下，解決學校資料分散儲存、資料格式不一致、各單位資料定義上的差異和資料品質低落問題的資料倉儲建置流程。本研究將針對這個問題，重新設計一個學生資料倉儲流程，此流程分為基於學生觀點為基礎的學生資料倉儲、探索資料源且建立同步機制和依照分析需求建置資料超市三個階段。此流程將會實作於個案學校，透過實作的方式來驗證此流程的可行性，且整理在每個階段過程中所遇到的困難和應該注意的事項，來作為學生資料整合的一個參考依據。

關鍵字

商業智慧與分析；商業智慧與教育；巨量資料；資料倉儲

並列摘要

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.

並列關鍵字

Business intelligence and Analytics ； Business intelligence and Education ； Big data ； Data warehouse

參考文獻

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.

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).

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.

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.

Bakshi, K. (2012). Considerations for big data: Architecture and approach. 2012 IEEE Aerospace Conference, 1–7.

國際替代計量

應用大數據於教學與學習之研究

全文下載

主題瀏覽