使用結構化學習特徵進行學習歷程探勘

Learning Profile Mining Using Structuralized Learning Feature

林義朝(Yi-Chao Lin)

指導教授 ： 林聰武 ； 鍾斌賢

中原大學/工學院/資訊工程研究所/碩士(2004年)

https://doi.org/10.6840/cycu200400211

摘要

近年來，遠距教學盛行，但因無法真實了解學生的上課情形，此時學習歷程(learning profile)就顯得重要，學習歷程可以代表一個學生的學習情況，有許多的專家研究學生的學習歷程來對教材內容或學習成效做改善。
本研究提出結構化學習特徵(Structuralized Learning Feature，SLF)的方法去開發一個學習歷程分析系統，讓使用者即使不懂得SQL語法，但還是能夠輕鬆的訂定條件，精確的搜尋出所要的資料。這樣的設計，對一般不具資訊專業能力的人而言，是非常方便且人性化的。提供了資料探勘工具，可以針對所搜尋出來的資料做進一步的分析。另外系統提供警訊機制，讓使用者分析出有用的規則後，可以套用此機制，有用的規則可以被有效的利用。
本系統可以透過所提出的資料庫介接層去連接各個不同的歷程資料庫，只要系統管理員事先完成設定，並不侷限只能分析某個特定的歷程資料庫。

關鍵字

遠距教學 ； 資料探勘 ； 學習歷程

並列摘要

In recent years, distance learning prevails, but because teacher can’t understand students’ situation during or after online learning activities.
So the learning profiles seem very important, learning profiles can represent students’ learning situation. There are many experts and researchers engaged in the research of student’s learning profile for improving teaching materials or learning performance.
On this thesis，we proposed the “Structuralized Learning Feature(SLF)” mechanism to develop a learning profile analysis system，users can search information in the profile database easily by setting query condition，even though she or he doesn’t know any about SQL language. It is friendly for those not have professional ability like information searching. The system also offered the materials like data mining to help teachers to analyze data even further. Besides，the system provides an warning strategy (ex. Sending an Email to teacher) which when mining results show something unreasonable or abnormal.
We can even connect to any profile data base with conditions which have been set. That means it not only can analyze certain profile data base, but other online activities like e-commerce or e-communication as long as the database stores everything.

並列關鍵字

distance learning ； data mining ； learning profile

參考文獻

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