臺灣博碩士論文加值系統

隨著工程教育認證的推廣，學生核心能力越來越受到重視。工程教育認證為國際上重要的教育認證機制，接受認證的學系，系上老師必須參考該系的教育目標來擬定課程所對應之學生核心能力。學生核心能力代表學生在畢業時透過學校系上課程所習得的能力，老師透過訂定學生核心能力以及各課程相關之學生核心能力來規劃系上課程關聯。然而目前缺乏工具或方法來呈現出群體學生實際修課所習得的學生核心能力狀況。本研究主旨為開發群體學生核心能力雷達圖，運用資訊視覺化來呈現群體學生核心能力之群體學生模型。本研究研發之群體學生核心能力雷達圖，以雷達圖的方式來呈現所計算出的群體學生核心能力數據，可以協助老師了解系所學生的核心能力發展狀況，作為老師調整系上課程規劃之參考。本研究進而分析學生核心能力之表現，用來建立學生是否四年準時畢業之預警機制。本研究將學生核心能力雷達圖資料及學生是否準時畢業之資料建立關聯，透過邏輯斯迴歸分析之結果，分析學生核心能力雷達圖是否能夠用來預測學生是否準時畢業。本研究同時也以學生核心能力雷達圖數據以及相關修課數量為基準，透過資料探勘之K-means分群法研發分群之畢業預警機制。運用分群機制能分出準時畢業機會高低有所差異之學生群體，達到提前預警的效果。運用分群之畢業預警機制可提供老師觀察不同群體的學生核心能力發展狀況，並分析出特定學生學習狀況之學生群體，幫助老師依據各群體狀況給予學生不同的建議，進而做出輔導。評估結果顯示本研究之群體學生核心能力雷達圖能夠呈現系所群體學生核心能力的發展狀況，協助老師檢視和調整系上的課程規劃。透過邏輯斯迴歸分析結果顯示學生核心能力之表現對於預測學生四年準時畢業確實具有一定的預測效力。以學生核心能力之表現資料經由K-means分群法能夠分出四年準時畢業風險不同的學生群體。學生核心能力之表現能夠能發展為一套畢業預警機制，協助老師了解學生的狀況，提早對需要輔導的學生進行輔導。

The student core competencies attracts much attention because of the accreditation of the Institute of Engineering Education. The accreditation of the Institute of Engineering Education is an important international authentication of the education. Teachers of the department refer educational goals and student core competencies to plan curriculum and the connection between classes and student core competencies. Student core competencies mean the abilities which students learned from the education of the department. However, there are less tools or mechanisms to reveal students’ learned core competencies through curriculum. This research develops radar charts to show group student model through information visualization. Through radar charts as group student model of student core competencies through curriculum, teachers observe status and distribution of students’ core competencies for planning and adjusting curriculum. The application results reveal that radar charts of core competencies as group student model can present different core competencies status of students.
The research also applies students’ core competencies for developing an early-warning mechanism to predict whether students can graduate within four years or not. The early warning mechanism utilizes logistic regression analysis to analyze the relation between student core competencies and graduation status. The results of logistic regression analysis reveal that core competencies can be a prediction factor whether a student can graduate within four year.
This research also develops another early warning mechanism by utilizing K-means clustering method for clustering students based on student core competencies. The clustering-based early warning mechanism enables teachers to observe student core competencies of different groups and to tutor students. The results reveal that the K-means clustering can cluster different groups with different core competencies and graduation possibilities as an early-warning mechanism.

目錄
摘要 I
Abstract III
誌謝 V
目錄 VI
圖目錄 VIII
表目錄 X
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 論文架構 3
第二章 文獻探討 4
2.1 核心能力 4
2.2 群體學生模型 7
2.3 資訊視覺化 9
2.4 資料探勘之分群法 11
第三章 系統 15
3.1 群體學生核心能力雷達圖 15
第四章 評估 21
4.1 系所之群體學生核心能力雷達圖之資料分析 21
4.2 運用邏輯斯迴歸分析之學生核心能力表現是否四年準時畢業之預警機制成效分析 31
4.3 運用K-means分群法之學生核心能力表現是否四年準時畢業之預警機制成效分析 37
第五章 結論與建議 47
5.1 結論 47
5.2 建議 49
英文參考文獻 50
中文參考文獻 54
附錄一、每項學生核心能力「修課品質」及「修課數量」為基準之分群結果(二&三) 55
附錄二、每項學生核心能力「修課品質」及「修課數量」為基準之分群結果(五&六) 56
附錄三、每項學生核心能力「修課品質」及「修課數量」為基準之分群結果(七&八) 57

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