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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 葉丙成 | |
dc.contributor.author | Da-Min Huang | en |
dc.contributor.author | 黃大珉 | zh_TW |
dc.date.accessioned | 2021-06-17T02:14:14Z | - |
dc.date.available | 2027-12-31 | |
dc.date.copyright | 2018-01-04 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-11-15 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68184 | - |
dc.description.abstract | 在現今的教育中,為學生的作業、考試評分已經不是專屬於老師或是助教的工作。除了由專業人士評分之外,同儕互評也是近年來逐漸興起的學生評量方式之一。從傳統的紙本互評,到現在也逐漸有一些線上同儕回饋系統,讓學生可以依照自己的時間規劃去完成同儕互評的流程。而同儕互評的趨勢,也促使許多學者進而投入同儕互評的品質計算方式與互評效果之研究。
在進行同儕互評時,如果想要提升同儕互評帶來的學習成效,那麼互評系統內的同儕互評回饋品質就是一項重要的指標。但在目前為止,計算回饋品質的方式仍舊處在人為評分的階段,並且沒有一套明確的標準與規範。此外,在面對大量的同儕互評回饋時,評斷回饋品質往往變成一項耗時費工的任務,是目前同儕互評回饋評量的瓶頸所在。 在本研究中,將使用機器學習的技術開發出一套可以幫助降低在計算回饋品質時所花費時間的演算法,稱為「隨機森林自動評斷回饋等第演算法」。此演算法會自動評斷互評回饋相對於其它互評回饋的等第,也就是互評回饋依照其回饋品質在整體互評回饋中的等級。本研究利用在課程中蒐集到的同儕互評回饋數據,並且為現行的評量方式加上新的規定來解決評定標準不夠明確的狀況,進而給出較為客觀準確的同儕互評回饋品質量化分數。將這些分析後的數據加以處理,用來作為產生演算法預測模型的訓練資料,進而發展出一套可以自動為同儕回饋評論預測相對等第的演算法。並進一步透過實驗數據證明,此演算法可以應用在網路開放式評論中,用來預測使用者給出評論的相對等第。 | zh_TW |
dc.description.abstract | Peer assessment has been gradually implemented in education nowadays. The role of evaluating students’ performance can be not only done by teachers, but also by students themselves. With the support of modern Internet, the process of peer assessment can be done online. Students do not have to evaluate others' work in class, and they can easily provide their feedbacks with a simple connection to an online peer assessment system. The trend has attracted many researchers' attention and put effort in studying the effect of peer assessment on students.
To improve learning performance of students in peer assessment, the quality of peer feedback is an important factor. Although there has been many methods for evaluating the peer feedback quality, the process is still being done manually, which is time consuming. There is no clear rule and standard for feedback quality so far. This research is trying to establish an algorithm called “Random Forest Automatically Ranking Algorithm” with techniques used in machine learning to help reduce time spent on calculating quality of peer feedback. This algorithm automatically determines rank of some peer feedback relative to other peed feedback, which means it grades every peer feedback with its quality relative to others’ quality. This research uses peer feedback data collected from course to get quantified quality of peer feedback and add new rules to method for evaluating the peer feedback quality to get more objective quality of peer feedback. We use processed peer feedback data as train data for generating predict model of this algorithm to automatically rank peer feedback. Furthermore, it is confirmed by experimental data that Random Forest Automatically Ranking Algorithm can also be applied to open comments on the internet to predict rank of comments given by online users. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:14:14Z (GMT). No. of bitstreams: 1 ntu-106-R04942045-1.pdf: 2648813 bytes, checksum: e9edaf1e12f5e85d6fcb304fe049c53f (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 目錄 v 圖目錄 viii 表目錄 x 第一章 緒論 1 1.1 前言 1 1.2 研究動機 1 1.3 研究背景 2 1.4 研究問題 3 1.5 研究概述 4 第二章 回饋品質計算與自動化方法之文獻探討 6 2.1 回饋相關理論 6 2.1.1 收到的回饋品質影響學生表現 7 2.1.2 回饋品質計算方法 11 2.2 動機相關理論 14 2.2.1 自我決定理論 14 2.2.2 學習動機對於回饋品質之影響 16 2.3 數位學習相關理論 17 2.4 回饋品質自動化計算相關理論 19 2.4.1 文字斷詞演算法 19 2.4.2 隨機森林演算法 20 第三章 教學環境與線上同儕回饋系統KaiGon 22 3.1 簡報製作與表達課程 23 3.1.1 課程概述 23 3.1.2 課程目標 23 3.2 系統概述 23 3.3 系統架構與功能 25 3.3.1 前端技術 26 3.3.2 後端 30 3.3.3 資料庫 30 3.4 系統特色功能 31 3.4.1 系統登入 31 3.4.2 首頁 31 3.4.3 助教端設定作業功能 32 3.4.4 作業上傳與額外提問功能 34 3.4.5 互評、影片按讚與留言功能 35 3.4.6 作業成績結算 36 3.4.7 排行榜 39 3.4.8 簡報學習歷程檔案 40 3.5 課程作業形式 41 第四章 隨機森林自動評斷回饋等第演算法 44 4.1 資料來源 44 4.2 分析方法 45 4.2.1 人工評量互評回饋品質計算方法 45 4.2.2 資料分析方法 48 第五章 研究結果 52 5.1 資料描述 52 5.2 分析結果 56 5.3 實例操作 60 5.4 網路開放式評論之自動評斷相對等第 62 5.4.1 開放式評論的選取 63 5.4.2 預測結果 65 第六章 結論與未來展望 70 6.1 研究討論 70 6.1.1 課堂同儕互評回饋 70 6.1.2 網路開放式評論 72 6.1.3 實務應用 72 6.2 研究結論 72 6.3 未來展望 73 參考文獻 76 | |
dc.language.iso | zh-TW | |
dc.title | 自動化評斷同儕回饋品質相對等第演算法:自動化方法與結果分析 | zh_TW |
dc.title | Automatically Ranking Algorithm of Peer Feedback: the Method and Result Analysis | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林明仁,賴以威 | |
dc.subject.keyword | 線上互評系統,同儕互評,同儕回饋,回饋品質,機器學習, | zh_TW |
dc.subject.keyword | Online peer assessment system,peer assessment,peer grading,peer feedback,feedback quality,machine learning, | en |
dc.relation.page | 80 | |
dc.identifier.doi | 10.6342/NTU201704378 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-11-15 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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