Studying the Relationships Between and Among Teacher Education, Teacher Quality, and Pupil Learning: Strategies, Challenges, and Rewards

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Studying the Relationships Between and Among
Teacher Education, Teacher Quality, and Pupil
Learning Strategies, Challenges, and Rewards

• Robert Tobias, Director
• Center for Research on Teaching and Learning
• NYUs Steinhardt School of Culture, Education,
  and Human Development

AACTE Annual Meeting February 25, 2007
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STRATEGIES
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Conceptual Framework for CRTL Research on the
Steinhardt Schools Teacher Education Programs
Causal Factors
Steinhardt Courses MAP Courses Field
Observations Student Teaching Extra-Curricular
Experiences Advisement Independent Study
Post-Graduate Study In-Service Prof. Dev. Prof.
Experiences School Context
Demographics Individuality Family Society Peers Sc
hool
Professional Educator
Steinhardt Graduate
Entering Steinhardt Student
Birth 12 Students

• Claims
• Content Knowledge
• Pedagogical
• Knowledge
• Teaching Skill
• Teacher Caring/
• Efficacy

Continued Professional Growth Through Reflection
in Practice
Outcomes
Beliefs about Teaching Efficacy Teacher
Caring Self-Efficacy Content Knowledge
Learning
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CRTL is Building a Comprehensive Database of
Pre-Professional, Induction, and Follow-Up
Measures

• Teacher Education Student Demographics and
  Pre-Program achievement data
• Grade Point Averages
• Content Area
• Pedagogical Knowledge
• Teaching Skill
• Domain-Referenced Student Teacher Observation
  Scale (DRSTO-R)
• Educational Beliefs Questionnaire (EBQ)
• NYS Teacher Certification Exam Scores (NYSTCE)
• Student Course Reaction Forms (SCRF)

• End-of-Term Student Teacher Feedback
  Questionnaires (ETFQ)
• Fast Track End-of-Program Questionnaire (FTEPQ)
• Undergraduate End-of-Program Questionnaire (UEPQ)
• State Information System Follow-Up Tracking Data
• One-Year Self-Report Follow-Up Survey
• One-Year Employer Follow-Up Survey
• Pupil Achievement Data
• Pupil Work Samples

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CRTL is Beginning the Fourth Year of the Ongoing
Study of Steinhardts Teacher Education Programs

• Extracting data from extant databases
• Designing and validating instruments for
  measuring the developing expertise and
  dispositions of pre-service and in-service
  teachers.
• Building a relational database (Phoenix)
• Developing processes and methods for tracking and
  collecting data on graduates
• Analyzing and reporting the data to inform
  continuous program improvement.

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Follow Up Study

• Phase 1
• Use state- and local-education agency (SEA and
  LEA) teacher databases to locate Steinhardt
  graduates.
• Link SEA and LEA data with pre-service data in
  Phoenix and school characteristics data from LEA
  databases.
• Phase 2
• After obtaining approval from school
  administrators and graduates, administer a
  self-report survey of teaching experiences to
  graduates and a survey of graduates teaching
  expertise to principals.
• Obtain standardized achievement test scores and
  work samples for graduates pupils.
• Use HLM analytic techniques to assess the causal
  relationships among the variables for pre-service
  program experiences, teacher quality and
  disposition, and pupil achievement. The analysis
  will explore the interactive effects of school
  environment and pupil and teacher demographics.

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Phase 1 Results

• Identified 879 Steinhardt graduates from the
  Classes of 2001-2004 who were teaching in New
  York State public schools in October 2004
  (BS205 MA674). Graduates were teaching in 546
  public schools in 19 NYS counties largest number
  in NYC (81.6), with most in Manhattan (N308),
  followed by Brooklyn (N180), and the Bronx
  (N105).
• Large numbers of graduates were teaching in inner
  city schools serving mostly poor African American
  and Latino students, some with large numbers of
  English language learners and recent immigrants.
• Most BS graduates were inducted into teaching
  directly after graduation and were continuously
  employed in the same schools and districts.
• Science education majors had the highest
  employment rate (71) followed by Literacy, Math,
  and Social Studies (over 50)
• Some schools had high concentrations of graduates
  with as many as 10 members of the cohort among
  the faculty.
• Additional graduates were identified in
  Connecticut and Florida, as well as through a
  match to the October 2003 NYS database.

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Phase 2 Status

• Developed survey instruments.
• Adapted standards-based pupil work assignments,
  grades 3-12.
• Obtained IRB approvals.
• Identified preliminary target sample of
  graduates.
• Collaborating with the NYCDOE to update
  information on the teaching assignments of an
  augmented file of graduates.
• Collaborating with the NYCDOE to obtain
  standardized test scores for graduates teaching
  in grades 4 8.
• Adjusting the analytic growth model to the
  exigencies of the available data.

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Challenges

• Generalizability Currently, tracking graduates
  is limited to public schools in NYS and
  Connecticut and, of course, participation in the
  study is voluntary.
• Validity of the dependent measure State and
  local testing programs were not designed for
  value-added research.
• Validity and reliability of measures of teacher
  quality License exam scores, GPAs, teacher
  observation scales, and self reports are all
  imperfect measures.
• Specification of the model Teaching and
  learning involves a complex interplay of a wide
  array of variables, many of which defy accurate
  measurement.
• Limitations of data systems Institutional data
  systems were designed for school management and
  not research.
• Inter-organizational tension The agendas of
  public school systems and IHEs may not be
  aligned. Collaborative partnerships are integral
  to reducing this tension.

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Early Rewards

• Accreditation
• Identification of opportunities for school-IHE
  partnerships.
• Leverage for the development of an evidence base
  for the evaluation of teacher education programs.
• Support for a culture of data-based decision
  making.
• Faculty discussions about the characteristics of
  high quality teachers and high quality teaching
  and learning show promise for increasing program
  coherence.