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Developing Measures of Mathematical Knowledge for
Teaching
Developing Measures of Mathematical Knowledge for
Teaching
Geoffrey Phelps, Heather Hill, Deborah Loewenberg Ball, Hyman Bass
Learning Mathematics for TeachingStudy of Instructional Improvement
Consortium for Policy Research in EducationUniversity of Michigan
MSP Regional ConferenceBoston, MA
March 30-31, 2006
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Overview of today’s session Overview of today’s session
1. LMT/SII Measures Development
2. Some Sample Results
3. LMT/SII Measures and Dissemination
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Subtract:
What is “Content Knowledge for Teaching”? An Example From
Subtraction
What is “Content Knowledge for Teaching”? An Example From
Subtraction
3002
783-
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Analyzing Student ErrorsAnalyzing Student Errors
3002
783-
2781
3002 - 783 = 4832
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Analyzing Unusual Student SolutionsAnalyzing Unusual Student Solutions
3002
783-
299
2219
12 3 0 0 2
7 8 3-
3-7-8-1
2 2 1 9
LMT/SII Measures Development
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Why Would We Want to “Measure” Teachers’ Content Knowledge for
Teaching?
Why Would We Want to “Measure” Teachers’ Content Knowledge for
Teaching?
• To understand “what” constitutes mathematical knowledge for teaching
• To understand the role of teachers’ content knowledge in students’ performance
• To study and compare outcomes of professional development and teacher education
• To inform design of teachers’ opportunities to learn content knowledge
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Measuring Teachers’ Mathematics Knowledge: Background and History
Measuring Teachers’ Mathematics Knowledge: Background and History
• Research on teacher behavior• Early research on student achievement
– Proxy measures for teacher knowledge– Tests of basic skills
• 1985 on: “the missing paradigm” pedagogical content knowledge or PCK
• 1990s: interview studies of teachers’ mathematics knowledge (MSU -- NCRTE)
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Study of Instructional Improvement
Study of Instructional Improvement
• Study of three Comprehensive School Reforms; teacher knowledge a key variable
• Instrument development goals:– Develop measures of content knowledge
teachers use in teaching • K-6 content for elementary school teachers• Not just what they teach - specialized knowledge
– Develop measures that discriminate among teachers (not criterion referenced)
– Non-ideological
• But we faced significant problems….
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Problems As We Began This Work
Problems As We Began This Work
• No way to measure teachers’ content knowledge for teaching on a large scale– Small number of items, many written by Ball,
Post, others appeared on every instrument– Nothing known about the statistical qualities
of those items (difficulty, reliability)– Studies relied on single items, yet single
items unlikely valid or reliable measures of teacher knowledge
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Early Decisions and ActivityEarly Decisions and Activity
• Survey-based measure of CKT-M– 3000 teachers participating in SII– Multiple choice
• Specified domain map
• 5 people + 5 lbs cheese + 5 weeks = 150 items (May 2001)
• Large-scale piloting, summer 2001
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Early Decisions and ActivityEarly Decisions and Activity
Types of knowledge
Math
em
atica
l co
nte
nt
Content knowledge
Knowledge of content
and students
Number
Operations
Patterns, functions, and algebra
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Early Analyses and Validity Checks
Early Analyses and Validity Checks
• Results from piloting – We can measure teachers’ CKT-M – Reliabilities of .70-.90– Factor analysis shows distinct types of
knowledge • Knowledge of content and students (KCS)
separate from CK• Specialized content knowledge (SCK) vs.
common content knowledge (CCK) Hill, H.C., Schilling, S.G., & Ball, D.L. (2004) Developing measures of
teachers’ mathematics knowledge for teaching. Elementary School Journal 105, 11-30.
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Reliabilities (1PL-IRT): Elementary
Reliabilities (1PL-IRT): Elementary
Knowledge of content
Knowledge of content and
students
Number and operations (K-6) .72-.81 .58-.67
Patterns, functions, and algebra (K-6) .70-.85
Geometry (3-8) .85-.86
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Reliabilities (1PL-IRT): Middle School
Reliabilities (1PL-IRT): Middle School
Knowledge of content
Knowledge of content and
students
Number and operations (5-9) .74-.75
Patterns, functions, and algebra (5-9) .86-.89
Geometry (3-8) .84-.86
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Content Knowledge :Number and Operations
Content Knowledge :Number and Operations
• Common knowledge– Number halfway between 1.1 and 1.11
• Specialized knowledge– Representing mathematical ideas and
operations – Providing explanations for mathematical ideas
and procedures– Appraising unusual student methods, claims,
or solutions
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Representing Number ConceptsRepresenting Number Concepts
Mrs. Johnson thinks it is important to vary the whole when she teaches fractions. For example, she might use five dollars to be the whole, or ten students, or a single rectangle. On one particular day, she uses as the whole a picture of two pizzas. What fraction of the two pizzas is she illustrating below? (Mark ONE answer.)a) 5/4 b) 5/3 c) 5/8 d) 1/4
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Providing Mathematical Explanations: Divisibility Rules
Providing Mathematical Explanations: Divisibility Rules
Ms. Harris was working with her class on divisibility rules. She told her class that a number is divisible by 4 if and only if the last two digits of the number are divisible by 4. One of her students asked her why the rule for 4 worked. She asked the other students if they could come up with a reason, and several possible reasons were proposed. Which of the following statements comes closest to explaining the reason for the divisibility rule for 4? (Mark ONE answer.)
a) Four is an even number, and odd numbers are not divisible by even numbers.
b) The number 100 is divisible by 4 (and also 1000, 10,000, etc.).
c) Every other even number is divisible by 4, for example, 24 and 28 but not 26.
d) It only works when the sum of the last two digits is an even number.
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Student A Student B Student C
x32
55 x
32
55 x
3255
+17
25
5+1770
50 1
2550
875+
16
0000
875
875
Which of these students is using a method thatcould be used to multiply any two whole numbers?
Appraising Unusual Student Solutions
Appraising Unusual Student Solutions
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Common vs. Specialized CKCommon vs. Specialized CK
• Appears in exploratory factor analyses on 2/7 forms; confirmatory on 3/7
• Individuals can be strong in common but not specialized; vice versa
• Support from cognitive interviews of mathematicians
• Suggests there is professional knowledge for teaching
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Ongoing WorkOngoing Work
• Item and measures development – Middle school national probability study– Develop new measurement modules for
data analysis and for probability• Validation efforts
– “Videotape” study– Cognitive tracing studies– Content validity checks
Some Sample Results
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An Example: Establishing a Relationship to
Student Growth
An Example: Establishing a Relationship to
Student Growth
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Links to Study of Instructional Improvement Student Achievement
Analysis
Links to Study of Instructional Improvement Student Achievement
Analysis• SII CKT-M measure – 38 items
– SII: .89 IRT reliability• Model: Student Terra Nova gains
predicted by:– Student descriptors (family SES, absence rate)– Teacher characteristics (math
methods/content, content knowledge) • Teacher content knowledge significant
– Small effect (LT 1/10 standard deviation)– But student SES is also on same order of
magnitude Hill, H.C., Rowan, B., & Ball, D.L. (2005) Effects of teachers'
mathematical knowledge for teaching on student achievement. American Educational Research Journal 42, 371-406.
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A Second Example: Evaluating Teacher Professional
Development
A Second Example: Evaluating Teacher Professional
Development
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Tracking Teacher GrowthTracking Teacher Growth
• Items piloted in California’s Mathematics Professional Development Institutes (MPDI)– Instructors: Mathematicians and mathematics
educators – 40-120 hours of professional development– Focus is squarely on mathematics content– Summer 2001– Pre/post assessment format (parallel forms)
Hill, H. C. & Ball, D. L. (2004) Learning mathematics for teaching: Results from California’s Mathematics Professional Development Institutes. Journal of Research in Mathematics Education 35, 330-351.
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MPDI Teacher Growth (Year 1)
MPDI Teacher Growth (Year 1)
• For all institutes for which we have data, teachers gained .48 logits, or roughly ½ standard deviation
• Translates to 2-3 item increase on assessment
• Considered substantial gain
0
0.2
0.4
0.6
0.8
1
1.2
All institutes
Pre-test
Post-test
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Results from Sample Institutes
Results from Sample Institutes
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
MPDI I MPDI II MPDI III MPDI IV MPDI V
Pre
Post
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MPDI Evaluation: Other Findings
MPDI Evaluation: Other Findings
• Length of institute predicts teacher gains– 120-hour institutes most effective, on average– But some 40-hour institutes very effective
• Focus on mathematical analysis, proof, and communication leads to higher gains
• Many questions remain– Effects of content (e.g., mathematics vs. student
thinking)– Treatment of content: common vs. specialized– Effects of teacher motivation– Long term learning from colleagues, curriculum,
practice
LMT/SII Measures and Dissemination
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Current Item PoolCurrent Item Pool
• Equated forms for elementary school: – Number & operations / Content
knowledge (K-6) – Number & operations/ Knowledge of
content and students (K-6)– Patterns Functions & Algebra/ Content
knowledge (K-6)– Geometry (3-8)
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Current Item PoolCurrent Item Pool
• Equated forms for middle school:– Number & operations / Content
knowledge (5-9)– Patterns Functions & Algebra/ Content
Knowledge (5-9)– Geometry (3-8)
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Item Workshops and Dissemination
Item Workshops and Dissemination
• Interested users attend a one-day workshop in Ann Arbor
• We cover – History of item development – Analytic methods and validation studies – How to use technical materials
• Users get– Access to measures – Support materials
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Dates and Contact Information
Dates and Contact Information
• Learning Mathematics for Teaching– http://sitemaker.umich.edu/lmt
• Dates for LMT Workshops – May 19, 2006– August 10, 2006 – Brenda Ely ([email protected])
• Geoffrey Phelps – [email protected]– 734-615-6076