Georgia A. Cobbs Trent Atkins The University of Montana
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Transcript of Georgia A. Cobbs Trent Atkins The University of Montana
How effective is our Elementary Teacher Education Program?
Two Projects: AGILE
Teacher Work Sample:
Georgia A. CobbsTrent Atkins
The University of Montana
Background
“The current political climate with its attention to teacher and student accountability and the shift in schooling from a norm-referenced, textbook driven system to a learner-centered, standards-based system has highlighted the need for a framework like work sampling that offers fodder for potential theoretical and empirical connections between preparation, teaching practices, and P-12 student learning”(Girod, Schalock & Cohen, 2006).
Teacher Education Needs
Teacher education under scrutinyHow to link teacher preparation to practice to K-
12 learning?Complex correlation (Berliner, 2002)Need for congruence between Higher Education
and PK-12Need to improve literacy instruction preparationHow to measure effectiveness?
Basis for AGILE & TWS
Need to blur the line between general education & special education
Many barriers in PK-12 are same issues in higher education
Ultimately, many of the issues in PK-12 are responsibility of higher education
AGILE Guiding Questions
Does training, engagement and ongoing support in RTI have a positive impact on:#1 Student-teacher view of self-efficacy?#2 Student-teacher view of measurement?#3 Student-teacher knowledge of the principles of Effective Instructional Practices?
AGILEGuiding Question cont#4 K-3 student reading skills?#5 K-3 student spelling skills?#6 K-3 student early numeracy skills?#7 K-3 student math computation skills?
AGILE Methods2 professors met w/ (3) student
teachers about 3-4/ monthCollected data about every 2
weeks on Elem studentsIf students were not improving
modify instructionNo specific strategies prescribed
(RTI)
Math Computation
Oral Counting
K-3 Student Data cont.
Implementation Highlights
School placements have been easierSchools appreciate the training we are
providing to studentsMore meaningful, transactional relationship
with schoolsStudent-teachers are receiving more focused
university supervision from UM faculty (Darrell and Trent)
AGILEEpiphanies
The complete lack of training in intervention strategies
Taking on assessment and evidence-based intervention strategies during student teaching is too much
Assessment and evidence-based intervention strategies must be integrated into the teaching preparation curriculum
Oregon Teacher Work Sample
Developed over the last 20 yearsStandardized a method for TWSOver 1,000 Student teachers10,000 K-12 students
Why TWS at UM?
To prepare teachers to make a difference in the learning of children
Teach Teacher Candidates to make data driven decisions.
Help to ensure teachers meet ethical obligations
To validate effectiveness of Teacher Ed programs
Purpose of TWS
Effort to move the Teacher Education Program into a new era
Experience of documenting teaching effectiveness
Measure knowledge & skill gains of PK-12 students
Assess using a pre/post action research design.
TWSResearch Questions
Q1 At what grade was the most impact made?
Q2 Was there an impact difference between male and female students?
Q3 Is there an impact difference among the types of students?
Q4 Is there an impact difference among the types of lessons?
Methodology
Senior level Math Methods 2011-2012Required assignment in methodsTeach a math lesson with a pre-post
assessmentEmail me the spreadsheet of dataAnswer assigned questions
Math Lesson (Researcher coded)
BookManipulativeTechnologyBook/ManipulativeBook/TechnologyTechnology/ManipulativeOther
Data Collection
Assignment to my UG/G Students (N=102)Part of their requirements for my courseReport back to meSome worked in pairsData incomplete or no clear pre-post
(narrative no test scores)Final Teacher Candidate Data N=76
Suggestions for Assessment
Same for both the pre & the post assessment.
Or two assessments should be very similar These could be chapter or unit test included
within a curriculum. In other cases you may be asked to use an assessment that is not part of a specific curriculum. In other situations, you may need to create your own assessment.
Data Reporting
Student Male/Female Ethnicity Learning Needs Post-Assessment Pre-Assessment % Change
Student #1
Student #2
• ANONYMOUS results to me
• Set up a spreadsheet like below
Demograhpics
Gender Male Female Unknown0
100
200
300
400
500
600
700
N=1169 Elementary Students
Gender Ethnicity Needs
Male 603 (51.6%) Native 13 NIN 937 (80.2%)
Female 528 (45.2%) Asian 7 G & T 62 (5.3%)
Unknown 38 (3.3%) Black 3 SpEd 94 (8%)
Total 1169 Latino 4 ELL 2 (.2%)
Caucasian 311 Unkn 74 (6.3%)
Unknown 831 Total 1169
Total 1169
Demographics
Mean Score Differences by Grade Level
Report
Change
Gradelevel Mean N Std. Deviation
Kinder11.26 137 28.325
1st35.44 32 38.210
1st/2nd Combo35.74 38 31.395
2nd14.78 187 24.700
3rd21.73 280 27.933
4th13.17 268 24.995
5th23.32 164 37.227
6th10.41 61 37.314
5th/6th Combo45.50 2 17.678
Total17.93 1169 29.957
Findings for GRADE LEVEL
To conduct analyses, grade were grouped into three categories: Early (K,1, and 2), Middle (3 and 4), and Late (5 and 6).
When grouped this way, there were slight differences in mean scores Early (17.25), Middle (17.54), and Late (20.05).
No statistically significant differences found.
Mean Differences by GENDER
Report
Change
Gender Mean N Std. Deviation
Female
18.65 528 31.203
Male
16.63 603 28.799
Total
17.57 1131 29.949
Findings for Gender
Females (18.65) scored slightly higher than male (16.63) students.
Differences were not statistically significant.
Mean Differences by ETHNICITY
Report
Change
ETHNICITY2 Mean N Std. Deviation
Diverse18.41 27 21.548
Non-Diverse20.78 311 29.373
Total20.59 338 28.808
Findings for ETHNICITY
To conduct analyses, the ethnicity variable was dichotomized.
“Non-diverse” students (20.78) scored higher than diverse students (18.41).
Differences were not statistically significant.
LEARNING NEEDS
Needs
Frequency Percent Valid Percent Cumulative Percent
Valid
NIN 937 80.2 85.6 85.6
G&T 62 5.3 5.7 91.2
SpEd 94 8.0 8.6 99.8
ELL 2 .2 .2 100.0
Total 1095 93.7 100.0
Missing Unknown 74 6.3
Total 1169 100.0
Percent by LEARNING NEEDS
Mean Differences by LEARNING NEEDS
Report
Change
Needs Mean N Std. Deviation
NIN18.19 937 29.880
G&T11.56 62 20.394
SpEd22.57 94 30.791
ELL40.50 2 21.920
Total18.23 1095 29.549
Findings for LEARNING NEEDS
To conduct analyses, due to the small number, ELL (n = 2 ) students were removed.
Students in special education scored highest (22.57), students with no identified needs scored in the middle (18.19), and students who are identified as gifted and talented made the smallest gains (11.56)
Differences were not statistically significant.
LESSON TYPELessonType
Frequency Percent Valid Percent Cumulative Percent
Valid
Book348 29.8 29.8 29.8
Book/Tech90 7.7 7.7 37.5
Tech60 5.1 5.1 42.6
Tech/Manip201 17.2 17.2 59.8
Manipulatives413 35.3 35.3 95.1
Book/Manip57 4.9 4.9 100.0
Total1169 100.0 100.0
Percent by LESSON TYPE
Mean Differences by LESSON TYPE
Report
Change
LessonType Mean N Std. Deviation
Book19.25 348 32.484
Book/Tech6.47 90 14.342
Tech7.80 60 23.415
Tech/Manip23.93 201 23.868
Manipulatives14.79 413 31.336
Book/Manip 40.26 57 31.227
Total17.93 1169 29.957
Findings for LESSON TYPE
Statistically significant differences among the lesson types!!!!
Book w/manipulatives had the most impact (statistical significance compared to all others).
Followed by Technology combined with manipulatives (statistical significance compared to all other but the book only).
Findings for LESSON TYPE
Statistically significant differences among lesson types
Book w/ manipulatives most impact (statistical significance compared to all others).
Technology w/ manipulatives (statistical significance compared to all other but the book only).
Findings continued
Book by itself was third most impactful (statistical differences when compared to the book and technology, the book and manipulatives)
Least impactful lesson types were manipulatives (14.49)technology (7.80) book w/ with technology (6.47)
Limitations
Not a standard assessmentTeacher Candidates not versed in Research
DesignCoding of lesson type Incomplete data: unknown demographicsError in recording/analysis by teacher
candidate or researcher
Next Steps
Make a Google Form!Standardize data collectionTeach Research Design to Teacher CandidatesOther ideas?
Conclusions
Book/manipulative lesson may be most effective for teacher candidates Need to research this more
Trend to have teachers make more data driven decisions
UM Teacher Candidates are teaching with data-driven decisions, perhaps more teachers will carry on as well
Questions?
Comments?
Thank you!
References
Berliner, D. (2002). Educational research: The hardest science of all. Educational Researcher, 31(8), 18-20.
Girod, M., Schalock, M. & Cohen, N. (2006). The Teacher Work Sample as a Context for Research. Paper presented at the annual meeting for AACTE, San Diego, CA.