學術產出-Theses

題名 以語意分析及Bloom理論為基礎之線上測驗輔助及智慧型評分系統
A Study on Computer Aided Testing and Intelligent Scoring: Based on Semantic Analysis and Bloom`s Taxonomy
作者 應鳴雄
Ying, Ming-Hsiung
貢獻者 楊亨利
Yang, Heng-Li
應鳴雄
Ying, Ming-Hsiung
關鍵詞 Bloom教育目標分類
線上測驗系統
本體論
電子化學習
填充題
Bloom’s Taxonomy
On-Line Testing
Ontology
E-Learning
Fill-in-Blank Items
日期 2005
上傳時間 18-Sep-2009 14:34:12 (UTC+8)
摘要 隨著電子化學習(E-Learning)環境技術的普及,線上學習與線上測驗已成為資訊教育的重要議題。但是因為填充題及問答題等測驗類型在線上測驗系統上實施有許多問題需克服,當線上測驗系統提供填充題及問答題等題型測驗時,將會產生嚴重的測驗評分等化(Equation)問題。目前線上測驗系統大多仍以是非題、單選題及複選題等題型為主,雖有少數線上測驗系統提供填充題及其他開放式填答的測驗類型,但仍未針對受測者填答之答案進行的語意自動評分。<br>另外,現有線上測驗系統未提供教師設定個人化的評分風格,對於多位教師共用測驗系統平台時所產生的評分規則認知衝突,系統也未提供支援與解決,為了解決上述問題,並使線上測驗能具備與傳統測驗相同的評量效力,本研究使用模糊理論、相似語意詞庫及人工智慧概念等,發展一個線上測驗及智慧評分子系統,此系統除了包括一般測驗系統所提供的是非、單選、複選等題型外,也包含採用智慧評分機制來評分的填充題,完成雛形系統的建置後,本研究再針對傳統紙筆測驗、一般型評分機制、及本研究的智慧型評分機制進行評分效力比較的實證研究。<br>此部分的實證研究結果顯示,在包含填充題型的測驗中,不同的評分機制在測驗成績的評分結果上會有顯著差異,而智慧型評分機制運作初期雖然可以減少與紙筆評分間的差異,並改善一般型評分機制的評分效力,但仍無法在統計上獲得具有相同評分效力的結果。但是智慧評分機制在擴充詞彙語意後,「已擴充語意後的智慧評分機制」與「紙筆評分」的評分結果並無顯著差異,其顯示出若在包括填充題型的線上測驗系統中加入具有擴充詞彙語意關係知識的功能,並提供多功能的智慧型模糊評分機制,允許教師輸入代表個人評分風格習慣的評分規則參數,則線上評分系統將有可能具有與紙筆評分相同的評分效力來處理具有填充題題型的測驗工作。<br>然而線上測驗並不只是在測驗後給予受測者一個分數而已,而應該讓學習者了解自己在知識向度及認知向度的學習結果,因此測驗系統的試題若能包含Bloom教育目標分類資訊,將促使測驗活動能給予學習者更大的幫助。為了降低教師製作試題的負擔,本研究也以本體論、詞彙網路、Bloom分類理論、中文語意庫、人工智慧為基礎,提出一個輔助教師產製題庫的系統架構,並使電腦所產製的試題能涵蓋新版Bloom認知領域教育目標分類中的知識向度及認知向度概念。本研究在電腦輔助教師產製題庫的成果上,不僅能減少教師人工出題的負擔,系統產製的試題也能評量事實、概念及程序等三種知識及記憶、了解、應用、分析及評鑑等五種認知向度能力。受限於線上測驗系統能自動評分的四種題型,本研究尚無法產製屬於創造認知層次的試題,但是卻已能產製出包含基本知識概念的試題,並能提供具有Bloom概念的測驗題庫來評量學習成效。此外,本研究亦針對電子化學習環境,提出適用於線上測驗系統的試題品質及評分等化能力評估概念模式,強調Bloom理論在線上測驗系統環境中的使用範圍與限制。此模式也透過測驗等化觀念,針對教育領域測驗理論在評估試題品質概念上提出新的觀點。
Since the rapid E-learning development, the online learning and testing have been important topics of information education. Currently teachers still need to spend much time on creating and maintaining on-line testing item banks. Some researches have applied the new Bloom`s taxonomy to design meaningful learning assessments. This research has applied ontology, Bloom`s taxonomy, Chinese semantic database, artificial intelligence, semantic web, to design an on-line course learning system to assist teachers in creating test items.<br>Most of present on-line testing only has multiple choice items and true-false items. Though some provide fill-in-blank items, they can only recognize the answers either all right or all wrong through the simple computer binary pattern matching. In order to have the same evaluation effects as the traditional paper-and-pencil testing, this research will adopt the concepts of fuzzy theory, thesaurus, set, and artificial intelligence to develop the fuzzy scoring mechanism. The proposed on-line testing system will have true-false, multiple-choice, and fil-in-blank items. The latter will be graded through fuzzy judgment that is naturally endowed by the human teachers.<br>In addition, the past research indicated that e-learning students would learn more if provided appropriate feedback messages. In this research will add feedback messages to the proposed system according to different situations. The proposed on-line testing system will not only grade the test items, but also explain the answers and provide related materials to the testers.<br>The result of study are: (1) we could design the test items that would need a particular cognitive process to a particular type of knowledge, though we still could not have items to test “creative” level of cognitive process; (2) the test items could be used to assess the learning level meaningfully; (3) the computer would assist teachers to create a large number items, and save time of making item; (4) that different scoring mechanisms have a significant effect on test scores; (5) at the beginning, though our fuzzy on-line testing system is significantly better than the usual on-line testing system, it could not achieve the same effect as the paper-and-pencil testing; (6) after expanding semantic vocabularies from feedbacks, our fuzzy scoring mechanism is equivalent to paper-and-pencil.
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描述 博士
國立政治大學
資訊管理研究所
89356501
94
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0893565011
資料類型 thesis
dc.contributor.advisor 楊亨利zh_TW
dc.contributor.advisor Yang, Heng-Lien_US
dc.contributor.author (Authors) 應鳴雄zh_TW
dc.contributor.author (Authors) Ying, Ming-Hsiungen_US
dc.creator (作者) 應鳴雄zh_TW
dc.creator (作者) Ying, Ming-Hsiungen_US
dc.date (日期) 2005en_US
dc.date.accessioned 18-Sep-2009 14:34:12 (UTC+8)-
dc.date.available 18-Sep-2009 14:34:12 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 14:34:12 (UTC+8)-
dc.identifier (Other Identifiers) G0893565011en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35257-
dc.description (描述) 博士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 89356501zh_TW
dc.description (描述) 94zh_TW
dc.description.abstract (摘要) 隨著電子化學習(E-Learning)環境技術的普及,線上學習與線上測驗已成為資訊教育的重要議題。但是因為填充題及問答題等測驗類型在線上測驗系統上實施有許多問題需克服,當線上測驗系統提供填充題及問答題等題型測驗時,將會產生嚴重的測驗評分等化(Equation)問題。目前線上測驗系統大多仍以是非題、單選題及複選題等題型為主,雖有少數線上測驗系統提供填充題及其他開放式填答的測驗類型,但仍未針對受測者填答之答案進行的語意自動評分。<br>另外,現有線上測驗系統未提供教師設定個人化的評分風格,對於多位教師共用測驗系統平台時所產生的評分規則認知衝突,系統也未提供支援與解決,為了解決上述問題,並使線上測驗能具備與傳統測驗相同的評量效力,本研究使用模糊理論、相似語意詞庫及人工智慧概念等,發展一個線上測驗及智慧評分子系統,此系統除了包括一般測驗系統所提供的是非、單選、複選等題型外,也包含採用智慧評分機制來評分的填充題,完成雛形系統的建置後,本研究再針對傳統紙筆測驗、一般型評分機制、及本研究的智慧型評分機制進行評分效力比較的實證研究。<br>此部分的實證研究結果顯示,在包含填充題型的測驗中,不同的評分機制在測驗成績的評分結果上會有顯著差異,而智慧型評分機制運作初期雖然可以減少與紙筆評分間的差異,並改善一般型評分機制的評分效力,但仍無法在統計上獲得具有相同評分效力的結果。但是智慧評分機制在擴充詞彙語意後,「已擴充語意後的智慧評分機制」與「紙筆評分」的評分結果並無顯著差異,其顯示出若在包括填充題型的線上測驗系統中加入具有擴充詞彙語意關係知識的功能,並提供多功能的智慧型模糊評分機制,允許教師輸入代表個人評分風格習慣的評分規則參數,則線上評分系統將有可能具有與紙筆評分相同的評分效力來處理具有填充題題型的測驗工作。<br>然而線上測驗並不只是在測驗後給予受測者一個分數而已,而應該讓學習者了解自己在知識向度及認知向度的學習結果,因此測驗系統的試題若能包含Bloom教育目標分類資訊,將促使測驗活動能給予學習者更大的幫助。為了降低教師製作試題的負擔,本研究也以本體論、詞彙網路、Bloom分類理論、中文語意庫、人工智慧為基礎,提出一個輔助教師產製題庫的系統架構,並使電腦所產製的試題能涵蓋新版Bloom認知領域教育目標分類中的知識向度及認知向度概念。本研究在電腦輔助教師產製題庫的成果上,不僅能減少教師人工出題的負擔,系統產製的試題也能評量事實、概念及程序等三種知識及記憶、了解、應用、分析及評鑑等五種認知向度能力。受限於線上測驗系統能自動評分的四種題型,本研究尚無法產製屬於創造認知層次的試題,但是卻已能產製出包含基本知識概念的試題,並能提供具有Bloom概念的測驗題庫來評量學習成效。此外,本研究亦針對電子化學習環境,提出適用於線上測驗系統的試題品質及評分等化能力評估概念模式,強調Bloom理論在線上測驗系統環境中的使用範圍與限制。此模式也透過測驗等化觀念,針對教育領域測驗理論在評估試題品質概念上提出新的觀點。zh_TW
dc.description.abstract (摘要) Since the rapid E-learning development, the online learning and testing have been important topics of information education. Currently teachers still need to spend much time on creating and maintaining on-line testing item banks. Some researches have applied the new Bloom`s taxonomy to design meaningful learning assessments. This research has applied ontology, Bloom`s taxonomy, Chinese semantic database, artificial intelligence, semantic web, to design an on-line course learning system to assist teachers in creating test items.<br>Most of present on-line testing only has multiple choice items and true-false items. Though some provide fill-in-blank items, they can only recognize the answers either all right or all wrong through the simple computer binary pattern matching. In order to have the same evaluation effects as the traditional paper-and-pencil testing, this research will adopt the concepts of fuzzy theory, thesaurus, set, and artificial intelligence to develop the fuzzy scoring mechanism. The proposed on-line testing system will have true-false, multiple-choice, and fil-in-blank items. The latter will be graded through fuzzy judgment that is naturally endowed by the human teachers.<br>In addition, the past research indicated that e-learning students would learn more if provided appropriate feedback messages. In this research will add feedback messages to the proposed system according to different situations. The proposed on-line testing system will not only grade the test items, but also explain the answers and provide related materials to the testers.<br>The result of study are: (1) we could design the test items that would need a particular cognitive process to a particular type of knowledge, though we still could not have items to test “creative” level of cognitive process; (2) the test items could be used to assess the learning level meaningfully; (3) the computer would assist teachers to create a large number items, and save time of making item; (4) that different scoring mechanisms have a significant effect on test scores; (5) at the beginning, though our fuzzy on-line testing system is significantly better than the usual on-line testing system, it could not achieve the same effect as the paper-and-pencil testing; (6) after expanding semantic vocabularies from feedbacks, our fuzzy scoring mechanism is equivalent to paper-and-pencil.en_US
dc.description.tableofcontents 中文摘要 I
英文摘要 II
目錄 Ⅲ
圖目錄 Ⅵ
表目錄 Ⅷ
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第三節 研究程序 3
第四節 基本假設 4
第五節 研究範圍 4
第六節 研究限制 5
第七節 論文結構 5
第二章 文獻探討 7
第一節 網路教學 7
第二節 建構主義 9
第三節 本體論 9
第四節 中文自動斷詞與中文結構 11
第五節 語意網與超文字文件結構 13
第六節 詞彙網路 14
第七節 測驗形式之演化 15
一、傳統測驗 15
二、電腦輔助測驗 15
三、網路測驗 16
四、人工智慧與模糊理論在測驗系統的應用 17
五、線上測驗系統及智慧型測驗系統發展 18
六、國內知名線上測驗系統功能與本研究系統預期功能之比較 18
第八節 BLOOM認知分類理論 20
第九節 測驗題型與試題發展原則 24
第十節 測驗回饋 28
第三章 雛型系統設計 30
第一節 評分效力問題來源先導研究階段 30
一、研究設計 30
二、先導實驗結果簡述 30
第二節 教師發展測驗題庫的能力及問題先導研究階段 31
一、研究設計 31
二、先導實驗分析結果 32
第三節 線上教材與試題結構知識本體概念設計階段 39
一、講義教材結構知識本體 40
二、教材知識本體與BLOOM知識向度對應 43
三、試題結構知識本體及所涵蓋的認知歷程向度 44
第四節 線上課程知識學習系統架構 46
第五節 輔助教師產製測驗題庫子系統之系統架構 49
第六節 線上測驗與智慧評分子系統之系統架構 49
第七節 本研究之知識種類 51
第八節 知識內容解析、詞義關係與文法結構規則之對應 52
第九節 電腦產製試題方法 58
第十節 智慧型線上評分機制設計 68
一、智慧型線上評分機制模組 68
二、智慧型評分機制運作流程 69
三、系統內建評分環境參數及相似語意詞庫模糊相似值之建構方法與步驟 72
第四章 雛形系統建置 76
第一節 雛形系統環境 76
第二節 使用者觀點之系統整體功能說明 76
第三節 系統功能說明 77
一、教師與管理者情境 77
二、學生情境 99
第五章 雛形系統成效初步評估 103
第一節 輔助教師產製測驗題庫子系統之系統成效評估 103
一、系統產製試題成效實證研究之研究設計 103
二、系統產製試題成效實證研究之資料分析 105
三、輔助教師產製測驗題庫子系統與教師出題及現有系統之功能比較 110
第二節 線上測驗與智慧評分子系統之系統成效評估 113
一、智慧評分機制評分效力實證研究之研究設計 113
二、智慧評分機制評分效力實證研究之資料分析 116
三、線上測驗與智慧評分子系統與現有測驗系統之功能比較 120
第六章 結論與建議 122
第一節 結論 122
第二節 本研究對於教育領域的影響語意涵 123
第三節 適用於線上測驗環境的試題品質及評分等化能力評估概念模式 126
第四節 未來研究方向與建議 130
第五節 研究貢獻 131
參考文獻 133
附錄一 教師發展測驗題庫的能力及問題先導研究第一階段問卷及資料 140
附錄二 教師發展測驗題庫的能力及問題先導研究第二階段問卷及資料 146
附錄三 電腦產製試題之試題合適性及採用意願問卷 153
附錄四 電腦產製試題子系統的系統成效問卷 156
附錄五 本研究已發表之研究成果 158

圖目錄

圖1  BLOOM教育目標分類系統新舊版本對照圖 20
圖2  線上講義教材結構的知識本體 40
圖3  線上試題結構的知識本體 44
圖4  本研究之線上課程知識學習系統架構 48
圖5  輔助教師產製測驗題庫子系統之系統架構圖 48
圖6  線上測驗與智慧評分子系統之系統架構圖 50
圖7  各工作處理階段所需之知識類型內容 52
圖8  試題產製流程圖 59
圖9  原型試題產製方法說明 60
圖10  反義試題產製方法說明 60
圖11  倒裝試題產製方法說明 61
圖12  相同知識主題內容的同型知識相關整併試題產製原理範例說明1 62
圖13  相同知識主題內容的同型知識相關整併試題產製原理範例說明2 63
圖14  相同知識主題內容的非同型知識相關整併試題產製原理範例說明1 63
圖15  相同知識主題內容的非同型知識相關整併試題產製原理範例說明2 63
圖16  相同知識主題內容的非同型知識相關整併試題產製原理範例說明 64
圖17  不同知識主題內容的非同型知識相關整併試題產製原理範例說明 65
圖18  對照詞彙型是非題試題產製原理範例說明 66
圖19  產製完成之各類試題範例歸納簡表 66
圖20  智慧型評分機制運作流程 70
圖21  集合比對SCM()副程式演算法 71
圖22  詞彙間語意相似程度之輸入歸屬函數 73
圖23  兩詞彙間語意相似程度之輸出歸屬函數 74
圖24  智慧型的線上課程知識學習系統平台環境架構圖 76
圖25  學員之系統註冊畫面 78
圖26  開啟來源文章檔案及直接輸入處理畫面 78
圖27  領域詞彙主題管理畫面 79
圖28  CKIP系統斷詞處理畫面 80
圖29  線上斷詞功能畫面 81
圖30  線上斷詞功能內容輸入畫面 81
圖31  線上斷詞功能之結果回傳畫面 81
圖32  通用斷詞準知識庫現有文章表列管理畫面 82
圖33  通用斷詞準知識庫之文章內容畫面 82
圖34  專用斷詞準知識庫現有文章表列管理畫面 83
圖35  專用斷詞準知識庫之文章內容畫面 83
圖36  規則類別管理畫面 84
圖37  現有知識樣式管理畫面 84
圖38  詞性意涵管理畫面 85
圖39  維護儲存於知識結構中的知識內容畫面 85
圖40  維護儲存於知識結構中的知識內容畫面1 86
圖41  維護儲存於知識結構中的知識內容畫面2 86
圖42  進行題庫產製的選擇畫面 87
圖43  電腦產製題庫的完成畫面 87
圖44  電腦輔助編製試卷以「題型」單一概念自動選題設定畫面 88
圖45  電腦輔助編製試卷以「題型」單一概念自動選題結果畫面 88
圖46  電腦輔助編製試卷以「知識類型」單一概念自動選題設定畫面 89
圖47  電腦輔助編製試卷以「認知層次」單一概念自動選題設定畫面 90
圖48  整合三種概念進行電腦輔助交叉選題的設定畫面 90
圖49  整合三種概念進行電腦輔助交叉選題的結果畫面 91
圖50  教師設定試卷需求過於嚴苛的範例設定畫面 91
圖51  教師設定試卷需求過於嚴苛的範例結果畫面 92
圖52  主功能選單及試題編輯管理-是非題畫面 92
圖53  試題編輯管理-填充題畫面 94
圖54  填充題語意專用設定畫面1 94
圖55  填充題語意專用設定畫面2 94
圖56  修改試題及考試時間畫面 96
圖57  專業領域相似語意詞彙建立畫面 96
圖58  智慧評分機制參數管理畫面 97
圖59  相似語意詞彙管理畫面 97
圖60  同音異字資料管理 97
圖61  學生測驗成績管理畫面 98
圖62  申訴案件警示與申訴管理畫面 98
圖63  線上測驗進行中之測驗部分畫面 100
圖64  調閱成績、答案及回饋之部分畫面 101
圖65  試卷批改申訴畫面 101
圖66  學習成效能力指標圖 105
圖67  評分效力研究之實驗流程圖 114
圖68  評分效力研究之研究架構圖 116
圖69  適用於線上測驗環境的試題品質及評分等化能力評估概念模式 128
圖70  本研究之系統對於線上測驗環境試題品質及評分等化能力的成效輔助 129


表目錄
表1  國內知名線上測驗系統功能比較表 19
表2  新版BLOOM認知領域教育目標分類表 20
表3  民國90年~93年四技二專商業類計算機概論試題分佈 24
表4  15位教師人工出題之題型分佈結果 33
表5  15位教師人工出題之原始試題BLOOM分類向度分佈結果 33
表6  15位教師人工出題且刪除重複試題後的BLOOM分類向度分佈結果 33
表7  參與實驗教師之試題發展認知結果彙整表 37
表8  參與實驗教師產製試題時運用BLOOM教育目標分類之程度結果彙整表 40
表9  本研究所使用的詞類標記符號表 53
表10  CKIP中文斷詞系統輸出之文章範例斷詞結果 53
表11  經領域斷詞修補模組處理後的斷詞結果 54
表12  解意知識結構規則內容 55
表13  特性知識結構規則內容 55
表14  因果知識結構規則內容 56
表15  次序知識結構規則內容 56
表16  詞義知識結構規則內容 56
表17  比較知識結構規則內容 57
表18  部分產製完成之是非題試題範例歸納簡表 67
表19  產製完成之各類試題範例歸納簡表 68
表20  模糊推論規則 74
表21  電腦產製試題之題型分佈結果 104
表22  電腦產製試題之BLOOM分類向度分佈結果 105
表23  參與教師對於電腦產製試題的是非題採用意願結果 108
表24  參與教師對於電腦產製試題的單選題採用意願結果 108
表25  參與教師對於電腦產製試題的複選題採用意願結果 108
表26  參與教師對於電腦產製試題的填充題採用意願結果 108
表27  參與教師對於電腦產製試題子系統具備的系統能力評估結果 109
表28  電腦產製試題與教師人工出題的效益比較表 111
表29  受測樣本之測驗結果 116
表30  評分工具假設之變異數分析表 117
表31  評分工具間之評分成績差異檢定分析表 117
表32  包含擴充詞彙語意後之智慧評分機制的變異數分析表 118
表33  評分工具間之評分成績差異檢定分析表 119
表34  線上測驗與智慧評分子系統相較於現有系統的功能特性分析表 120
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0893565011en_US
dc.subject (關鍵詞) Bloom教育目標分類zh_TW
dc.subject (關鍵詞) 線上測驗系統zh_TW
dc.subject (關鍵詞) 本體論zh_TW
dc.subject (關鍵詞) 電子化學習zh_TW
dc.subject (關鍵詞) 填充題zh_TW
dc.subject (關鍵詞) Bloom’s Taxonomyen_US
dc.subject (關鍵詞) On-Line Testingen_US
dc.subject (關鍵詞) Ontologyen_US
dc.subject (關鍵詞) E-Learningen_US
dc.subject (關鍵詞) Fill-in-Blank Itemsen_US
dc.title (題名) 以語意分析及Bloom理論為基礎之線上測驗輔助及智慧型評分系統zh_TW
dc.title (題名) A Study on Computer Aided Testing and Intelligent Scoring: Based on Semantic Analysis and Bloom`s Taxonomyen_US
dc.type (資料類型) thesisen
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