- 期刊
教師在職進修研習課程大數據關聯規則研究初探：以2017年全國國中語文領域國文科教師為例
Study on the Big Data Association Rules of In-service Teacher Professional Curricula: Example As the National Middle School First-registered Chinese of Official Teachers
摘要
為了解教師在職進修研習課程的特徵，本研究針對全國正式在職首登專長國中國文科教師的在職進修研習課程進行關聯規則特徵分析，以2017年度上述教師登錄完成之研習進修課程為主，進行資料探勘，資料來源以教育部建置之「全國教師在職進修資訊網」資料庫為依據。2017年度全國正式在職首登專長國中國文科教師計7,817人，研習課程參加人次合計135,405人次；依任教學校區域進行教師分群：北部、中部、南部、東部及離島區域，以Apriori演算法探勘教師在職進修研習課程性質之間的關聯規則，利用支持度（Supp.）及相關性指標（lift）進行判斷挖掘關聯規則，以支持度和信賴度來嘗試找出有意義且正相關的規則，初步結果發現關聯規則過多，需要再進一步釐清。之後，運用翁政雄於2012年所提出之重要性指標（Imp.）計算出有意義關聯規則之重要性程度，可縮減關聯規則的數量，凸顯出重要的關聯規則；藉此分析了解國文科教師進修課程的各種特徵，做為持續改進辦理教師在職進修研習成效之參考。主要結論如下：（1）13-24班、30-34歲、課程時數1小時、課程時數2小時、課程範疇實用知能與生活素養為關聯規則及重要性指標；（2）整體與北、中部群以碩士學歷為關聯規則及重要性指標，南、東部群以大學學歷為關聯規則及重要性指標；（3）整體以男性為關聯規則及重要性指標，中、南、東部群以女性為關聯規則及重要性指標。
並列摘要
The purpose of this study was to analyze the characteristics of first-registered Chinese expertise of official teachers with the contents of their in-service teacher professional curricula (ISTPC). The way of data digging on the ISTPC focuses on the courses completely registered by first-registered Chinese expertise of official teachers in 2017 to search in the Database of Nationwide Teacher In-service Education Information Web of Ministry of Education by data mining and to proceed the analyses based on the files found in the database. In 2017, there were 7,817 first-registered Chinese expertise teachers who were officially in-service at the national junior high schools , and the total number of participants in the ISTPC was 135,405. Applying the algorithm of Apriori to proceed the data mining for exploring the association rules among the ISTPC from the Database relevant to junior high school teachers. The main conclusions were as follows: (1)The school size is 13-24 classes, the teacher's age is 30-34 years old, the course hours as 1 hour, 2 hours, and the curriculum category-practical knowledge and life literacy are the association rules and importance indicators; (2)As the whole and northern,、central group, master's degree is used as the association rule and importance indicator, and in the southern and eastern group, university degree is used as the association rule and importance indicator; (3)As the whole, male is used as the association rules and importance indicators, and the central, southern and eastern group take female as the association rules and importance indicators.