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課程簡介 Course Introduction
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開課年度學期 Year / Term
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115 學年度 第 1 學期
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開課班級 Department
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教育學系教育數位評量與數據分析碩士班 教育數位評量測統碩博合選 Master Program of Online Educational Assessment and Data Analysis, Department of Education
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授課方式 Instructional Method
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課堂教學 、 中文
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課程電腦代號 Course Reference Number
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112034
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課程名稱(中文) Course Title(Chinese)
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數學評量專題研究
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課程名稱(英文) Course Title(English)
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Seminar on Mathematics Assessment
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學分數/時數 Credit Hours
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3 /
3
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必(選)修 Required / Elective Course
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選修 Elective
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授課老師 Instructor
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林素微
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助教 Teaching Assistant
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上課時間 Meeting Time
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星期三,節次C Wed, Period C、D、E
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上課教室 Classroom
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A304
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Office Hours
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| 獲獎及補助情形 Awards and Grants |
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| 聯合國永續發展目標 (SDGs跨域類別) Sustainable Development Goals, SDGs |
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| 屬於大學社會責任(USR)性質 Courses related to University Social Responsibility(USR) |
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否
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| 是否運用AI輔助教學 Using Artificial Intelligence (AI) to assist teaching |
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是
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課程目標 Learning Objectives
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1. 學生能覺察、省思當前國際與學校教學中數學評量的相關議題,針對該議題,進行文獻(含評量工具)閱覽。 2. 學生能依據上述文獻探討結果,針對數學領域進行革新的評量設計,並進行小樣本試用、結果分析和課堂的分享討論。 3.針對國際數學評比資料庫,依據測驗理論與技術進行分析,並討論其可能的意涵。
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先修 ( 前置 ) 課程 Prerequisite
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| 彈性教學規劃 Flexible Teaching/Planning Schedules |
| *本課程實施16+2週彈性教學方案,其中第17、18週之彈性規劃如下: |
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線上教學/討論
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校外參訪
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課程大綱 Course Syllabus
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| 週次 Week |
課程單元大綱 Unit |
教學方式 Instructional Method/Style/Teaching Style |
參考資料或相關作業 References or Related Materials |
評量方式 Grading |
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1
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課程簡介與數學評量趨勢
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講述與討論
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介紹課程大綱與評量要求;討論 PISA 等國際數學評量發展趨勢
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口頭討論
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2
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數學評量的理論基礎
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講述與討論
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探討心理測驗理論與古典測驗理論在數學評量中的應用
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口頭討論
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3
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數學評量議題與文獻探討(一)
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講述與討論
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討論素養導向評量與形成性評量的設計與實務挑戰
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口頭討論
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4
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數學評量議題與文獻探討(二)
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講述與討論
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分析生成式 AI 在教育評量命題與數學教學回饋中的應用與倫理
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口頭討論
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5
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數學評量工具發展與設計(一)
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講述與討論
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評量框架設計、雙向細目表編製與選擇題編製原則
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口頭討論&實作
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6
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數學評量工具發展與設計(二)
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講述與討論
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素養題、建構反應題 (非選擇題) 與實作表現任務的設計與評分規準
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口頭討論&實作
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7
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國際數學評比資料庫分析(一)
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講述與討論
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深入解析 PISA/TIMSS 數學評比資料庫結構介紹與資料清理處理
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口頭討論&實作
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8
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國際數學評比資料庫分析(二)
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講述與討論
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運用進階量化技術 (如 SEM、CFA、HLM) 進行次級資料分析與詮釋
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口頭討論&實作
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9
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期中進度報告
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口頭報告
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學生報告文獻探討成果或初步的數學評量工具設計構想
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10
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非認知因素與數學學習評量
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講述與討論
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探討學生恆毅力 (Grit)、韌性、自我效能等情意特質的測量工具設計
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口頭討論&實作
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11
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現代測驗理論與量化議題(一)
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講述與討論
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試題反應理論 (IRT) 的模型與其在數學評量題目分析中的應用
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口頭討論&實作
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12
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現代測驗理論與量化議題(二)
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講述與討論
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潛在類別分析 (LCA) 或認知診斷模型 (CDM) 等進階量化技術之應用
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口頭討論&實作
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13
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數學評量工具之實作與試用(一)
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講述與討論
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學生針對其設計的創新評量工具,進行小樣本試測設計與實施
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口頭討論&實作
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14
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數學評量工具之實作與試用(二)
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講述與討論
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試測資料收集完成,進行初步數據檢核與資料整併
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口頭討論&實作
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15
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測驗結果分析與解釋(一)
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講述與討論
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運用統計軟體 (如 Mplus) 分析試測結果,進行信度與效度檢驗
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口頭討論&實作
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16
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測驗結果分析與解釋(二)
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講述與討論
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運用統計軟體 (如 Mplus) 分析試測結果,進行信度與效度檢驗
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口頭討論&實作
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17
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書面論文初稿問題討論
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線上學習
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書面論文初稿問題討論
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口頭討論&實作(10%)
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18
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綜合討論與反思
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線上學習
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分享改良後的評量工具,針對數學評量實務與量化分析進行深度反思
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重點摘要及心得(10%)
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單一課程對應校能力指標程度 The Degree to Which Single Course Corresponds to School Competence
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| 編號 No. |
校核心能力 School Core Competencies |
符合程度 Degree of conformity |
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1
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公民力 (Citizen)
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5
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2
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自學力 (Self-learning)
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5
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3
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資訊力 (Information)
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5
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4
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創造力 (Creativity)
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5
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5
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溝通力 (Communication)
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5
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6
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就業力(Employability)
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5
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單一課程對應系能力指標程度 The Degree to Which Single Course Corresponds to Department Competence
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| 編號 No. |
類別 Category |
系核心能力 Department Core Competencies |
符合程度 Degree of conformity |
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01
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系所
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能分析與解釋量化與類別資料
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5
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02
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系所
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能批判量化研究設計
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5
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03
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系所
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能創新評量工具(碩)
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5
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04
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系所
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能整合科技進行測驗創新議題探討
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5
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05
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系所
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能發表測驗統計議題的論文
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5
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06
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系所
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能提供基礎水準測驗與統計問題的諮詢服務(碩)
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5
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單一課程對應院能力指標程度 The Degree to Which Single Course Corresponds to College Competence
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| 編號 No. |
院核心能力 College Core Competencies |
符合程度 Degree of conformity |
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1
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探究能力
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5
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2
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語文與溝通能力
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5
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3
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創新與實踐能力
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5
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4
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專業知能
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5
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教科書或參考用書 Textbooks or Reference Books
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館藏書名 Library Books
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備註 Remarks
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Key Journals:
1.Journal of Educational Measurement (JEM): Flagship applied-theoretical measurement journal: test theory, scaling, equating, standard setting, validity 2.Psychometrika: The premier quantitative-theory journal: IRT, factor analysis, SEM, classification, latent-variable theory 3.Applied Psychological Measurement (APM): Applied/methodological studies on IRT, CTT, equating, CAT Educational and Psychological Measurement (EPM): Scale development, validity, reliability, quantitative methods 4.Journal of Educational and Behavioral Statistics (JEBS): Statistical methodology for educational/behavioral data — multilevel models, psychometric statistics Large-scale Assessments in Education (LSAE): Open-access journal dedicated to PISA/TIMSS/PIAAC/NAEP-type methodology and findings International Journal of Testing (IJT) Taylor & Francis / ITC Cross-cultural test development, adaptation, fairness, testing policy Journal for Research in Mathematics Education (JRME) NCTM Leading U.S. journal for empirical/theoretical research on math teaching and learning Educational Studies in Mathematics (ESM) Springer International theoretical/empirical mathematics education research ZDM – Mathematics Education Springer International math education research; strong overlap with assessment and comparative studies
Classic Textbooks (The Core Dissertation Toolkit) Textbook Author(s), Year Covers Statistical Theories of Mental Test Scores Lord & Novick, 1968 CTT foundations + early IRT (Birnbaum chapters) Standards for Educational and Psychological Testing AERA/APA/NCME, 2014 Authoritative U.S. professional testing standards Fundamentals of Item Response Theory Hambleton, Swaminathan & Rogers, 1991 Standard first IRT read: 1PL/2PL/3PL, invariance, DIF, CAT The Theory and Practice of Item Response Theory (2nd ed.) de Ayala, 2022 Comprehensive current IRT text: polytomous models, MIRT, DIF, software Item Response Theory: Foundations for Psychologists and Social Scientists Embretson & Reise, 2000/2013 Applied IRT with strong polytomous-model coverage Multidimensional Item Response Theory Reckase, 2009 Definitive MIRT text: model forms, dimensionality, estimation, adaptive testing Diagnostic Measurement: Theory, Methods, and Applications Rupp, Templin & Henson, 2010 Standard CDM/DCM reference; 2012 AERA Division D award winner Test Equating, Scaling, and Linking: Methods and Practices (3rd ed.) Kolen & Brennan, 2014 Equating/scaling essential for PISA/TIMSS/NAEP work
Seminal Papers Every Student Should Master Paper Author(s), Year Why It's Essential "Some Latent Trait Models and Their Use in Inferring an Examinee's Ability" Birnbaum, 1968 Introduced 2PL/3PL models — the operational basis of nearly all large-scale math testing "Cognitive Assessment Models With Few Assumptions..." Junker & Sijtsma, 2001 Canonical formalization of the DINA model "DINA Model and Parameter Estimation: A Didactic" de la Torre, 2009 The go-to worked example for CDM estimation using math data "Rule Space: An Approach for Dealing With Misconceptions..." Tatsuoka, 1983 First operational cognitive-diagnostic model; origin of the field's benchmark math dataset "The Generalized DINA Model Framework" de la Torre, 2011 Unifying CDM framework (G-DINA) used across most current applications Explanatory Item Response Models De Boeck & Wilson, 2004 Recasts IRT as GLMM/NLMM — essential for covariate-driven item/person modeling "The Use of Test Scores From Large-Scale Assessment Surveys" Braun & von Davier, 2017 Demystifies plausible-value methodology underlying PISA/TIMSS/NAEP scores PISA 2022 Technical Report OECD, 2024 Primary source for PISA's actual sampling/scaling/QA procedures "Automatic Item Generation: Theory and Practice" Gierl & Haladyna (Eds.), 2013 Establishes AIG as a field — required before engaging any AI-item-generation literature "The Rise of Artificial Intelligence in Educational Measurement" Bulut et al., 2024 Current field-defining synthesis of AI's role in assessment, scoring, and item generation
Key Professional Organizations • NCME (National Council on Measurement in Education) — ncme.org • Psychometric Society (publishes Psychometrika; hosts IMPS annual meeting) — psychometricsociety.org • AERA Division D (Measurement and Research Methodology) — aera.net/Division-D • OECD PISA Programme — oecd.org/PISA • IEA / TIMSS & PIRLS International Study Center — iea.nl/studies/iea/timss • NCES NAEP ("The Nation's Report Card") — nces.ed.gov/nationsreportcard/tdw Recommended Reading Sequence 1. Foundations first: Lord & Novick (1968) → Hambleton, Swaminathan & Rogers (1991) → de Ayala (2022) 2. Advance to multidimensionality and diagnosis: Reckase (2009) → Junker & Sijtsma (2001) → de la Torre (2009, 2011) → Rupp, Templin & Henson (2010) 3. Ground in mathematics-specific applications: Tatsuoka (1983, 1990) → Bradshaw et al. (2014) → Templin & Bradshaw (2014) 4. Move to large-scale operational context: OECD PISA Technical Report → TIMSS Technical Report → Braun & von Davier (2017) → Chmielewski et al. (2018) 5. Finish at the research frontier: Gierl & Haladyna (2013) → Bulut et al. (2024) → Leng, Bezirhan & von Davier (2026)
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※請尊重智慧財產權,不得非法影印教科書※
※ Please respect intellectual property rights and do not illegally photocopy textbooks. ※
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教學方法 Teaching Method
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教學方法 Teaching Method
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百分比 Percentage
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講述
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35 %
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問題導向學習
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35 %
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專題實作
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30 %
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| 總和 Total |
100 % |
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成績評量方式 Grading
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| 評量方式 Grading |
百分比 Percentage |
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課堂參與
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40 %
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個人書面報告
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40 %
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第17週自主學習
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10 %
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第18週自主學習
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10 %
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| 總和 Total |
100 % |
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課程大綱補充資料 Supplementary Material of Course Syllabus
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