臺灣博碩士論文加值系統

隨著數位學習(E-learning)的盛行，讓學習已經不再受時間跟地點的限制，只要能上網就能進行學習，但也因為其方便性，使得參與課程的人數也遠超以往，評定成績的方式也因此需做了不同的調整，對於某些科目來說，是非、選擇、填空等題型是不恰當的，所以同儕互評(Peer Assessment)變成大型課程評定作業成績的一種方式。本研究將探討在大型課程中，學習者在評定他人作業的態度對整體同儕互評系統的影響，並對研究之模型做適當的調整，從信效度的角度對模型的結果進行分析，再提出從HITS演算法修改來的同儕互評模型，試圖以HITS演算法的精神，找出好的評分者、與不好的評分者，從中找出學習者在評定他人作業成績時胡亂給分的特徵，利用協同過濾進行分析，使同儕互評系統不因這些使用者而導致結果與真實狀況有極大的誤差，以提升系統的可信度。

With the popularity of E-learning, learning itself was no longer restricted by time and location, but available just with an accessible internet. Yet, due to its convenience, the average number of learners participated in an online course has become more than ever, and therefore homework assessment becomes a great challenge for instructors and require being adjusted accordingly. Amongst these adjustments, peer assessment is one of potential approaches applied to homework assessment in a large-scaled course. This study aims to explore the impact of learner’s attitude on Peer Assessment System, and accordingly makes appropriate adjustment based on the research result from the perspective of validity. And we intend to find out the features of the learners do the assessment in a random manner and use collaborative filtering to identify them with these features. With the identification of these learners, it is expected the Peer Assessment System would produce the results similar to those made by human experts, and hence increase its assessment reliability.

中文摘要 I
ABSTRACT II
誌　謝 III
目錄 IV
表目錄 V
圖目錄 VI
第一章 緒論 1
1.1研究動機與背景 1
1.2研究目的與問題 2
1.2.1研究目的 2
1.2.1研究問題 2
第二章 文獻探討 3
2.1同儕互評(PEER ASSESSMENT) 3
2.2網路學習的同儕互評 4
2.3協同過濾互評（COLLABORATIVE FILTERING） 5
2.4超連結引發之主題網頁檢索名次排序法（HYPERLINK-INDUCED TOPIC SEARCH） 8
第三章 研究方法 10
3.1協同過濾同儕互評測試 10
3.2協同過濾互評調整 12
3.3 HITS同儕互評 14
第四章 實驗設計 18
4.1實驗方法 18
4.1.1學生成績 18
4.2實驗結果與分析 19
4.2.1協同過濾同儕互評 19
4.2.2HITS同儕互評 21
4.2.3HITS同儕互評與Outlier的比較 24
4.2.4協同過濾搭配HITS同儕互評 25
4.2.5限制批改份數的同儕互評 26
第五章 結論 30
參考文獻 32

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