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 115 學年度 第 1 學期 教育學系教育數位評量與數據分析碩士班 林素微教師 數學評量專題研究 課程大綱
課程簡介   Course Introduction
開課年度學期
Year / Term
115 學年度 第 1 學期
開課班級
Department
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
授課方式
Instructional Method
課堂教學 、 中文
課程電腦代號
Course Reference Number
112034
課程名稱(中文)
Course Title(Chinese)
數學評量專題研究
課程名稱(英文)
Course Title(English)
Seminar on Mathematics Assessment
學分數/時數
Credit Hours
3 / 3
必(選)修
Required / Elective Course
選修 Elective
授課老師
Instructor
林素微
助教
Teaching Assistant
謝程宇
上課時間
Meeting Time
星期三,節次C
Wed, Period C、D、E
上課教室
Classroom
A312
Office Hours
林素微:2444/BBCD

獲獎及補助情形   Awards and Grants

聯合國永續發展目標 (SDGs跨域類別)   Sustainable Development Goals, SDGs
SDGs 04. 優質教育:確保有教無類、公平以及高品質的教育,及提倡終身學習
Quality Education:Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

屬於大學社會責任(USR)性質   Courses related to University Social Responsibility(USR)
否

是否運用AI輔助教學   Using Artificial Intelligence (AI) to assist teaching
是

課程目標   Learning Objectives
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。
 

先修 ( 前置 ) 課程   Prerequisite
 

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:
線上教學/討論
校外參訪

課程大綱   Course Syllabus
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
1 課程簡介與數學評量趨勢 講述與討論  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  口頭討論 
2 數學評量的理論基礎 講述與討論  探討心理測驗理論與古典測驗理論在數學評量中的應用  口頭討論 
3 數學評量議題與文獻探討(一) 講述與討論  討論素養導向評量與形成性評量的設計與實務挑戰  口頭討論 
4 數學評量議題與文獻探討(二) 講述與討論  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  口頭討論 
5 數學評量工具發展與設計(一) 講述與討論  評量框架設計、雙向細目表編製與選擇題編製原則  口頭討論&實作 
6 數學評量工具發展與設計(二) 講述與討論  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  口頭討論&實作 
7 國際數學評比資料庫分析(一) 講述與討論  深入解析 PISA/TIMSS 數學評比資料庫結構介紹與資料清理處理  口頭討論&實作 
8 國際數學評比資料庫分析(二) 講述與討論  運用進階量化技術 (如 SEM、CFA、HLM) 進行次級資料分析與詮釋  口頭討論&實作 
9 期中進度報告 口頭報告  學生報告文獻探討成果或初步的數學評量工具設計構想   
10 非認知因素與數學學習評量 講述與討論  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  口頭討論&實作 
11 現代測驗理論與量化議題(一) 講述與討論  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  口頭討論&實作 
12 現代測驗理論與量化議題(二) 講述與討論  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  口頭討論&實作 
13 數學評量工具之實作與試用(一) 講述與討論  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  口頭討論&實作 
14 數學評量工具之實作與試用(二) 講述與討論  試測資料收集完成,進行初步數據檢核與資料整併  口頭討論&實作 
15 測驗結果分析與解釋(一) 講述與討論  運用統計軟體 (如 Mplus) 分析試測結果,進行信度與效度檢驗  口頭討論&實作 
16 測驗結果分析與解釋(二) 講述與討論  運用統計軟體 (如 Mplus) 分析試測結果,進行信度與效度檢驗  口頭討論&實作 
17 書面論文初稿問題討論 線上學習  書面論文初稿問題討論  口頭討論&實作(10%) 
18 綜合討論與反思 線上學習  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  重點摘要及心得(10%) 


單一課程對應校能力指標程度   The Degree to Which Single Course Corresponds to School Competence
編號
No.
校核心能力
School Core Competencies
符合程度
Degree of conformity
1 公民力 (Citizen) 5
2 自學力 (Self-learning) 5
3 資訊力 (Information) 5
4 創造力 (Creativity) 5
5 溝通力 (Communication) 5
6 就業力(Employability) 5

單一課程對應系能力指標程度   The Degree to Which Single Course Corresponds to Department Competence
編號
No.
類別
Category
系核心能力
Department Core Competencies
符合程度
Degree of conformity
01 系所 能分析與解釋量化與類別資料 5
02 系所 能批判量化研究設計 5
03 系所 能創新評量工具(碩) 5
04 系所 能整合科技進行測驗創新議題探討 5
05 系所 能發表測驗統計議題的論文 5
06 系所 能提供基礎水準測驗與統計問題的諮詢服務(碩) 5

單一課程對應院能力指標程度   The Degree to Which Single Course Corresponds to College Competence
編號
No.
院核心能力
College Core Competencies
符合程度
Degree of conformity
1 探究能力 5
2 語文與溝通能力 5
3 創新與實踐能力 5
4 專業知能 5


教科書或參考用書   Textbooks or Reference Books
館藏書名   Library Books
備註   Remarks
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)


※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.  ※

教學方法   Teaching Method
教學方法
Teaching Method
百分比
Percentage
講述 35 %
問題導向學習 35 %
專題實作 30 %
總和  Total 100 %

成績評量方式   Grading
評量方式
Grading
百分比
Percentage
課堂參與 40 %
個人書面報告 40 %
第17週自主學習 10 %
第18週自主學習 10 %
總和  Total 100 %

成績評量方式補充說明   
 

課程大綱補充資料   Supplementary Material of Course Syllabus