Presentation for the 2018 AACTE Conference on Motivation factors and Co-Teaching

1. Guiding Innovations in Clinical
Practice by Investigating Motivation in
Co-Teaching vs. Traditional Student
Teaching
Jessica Chittum - ECU
Kristen Cuthrell - ECU
Christina Tschida - ECU
Elizabeth Fogarty - University of MN
Joy Stapleton – Winthrop University
AACTE 2018 – Baltimore, MD

2. Intro:
The Situation in NC
“I just can’t risk turning over
my classroom
to a novice any longer.”

3. Addressing the Concerns
 Due to increased teacher accountability, a model for student
teaching that allows clinical teachers to remain in their
classrooms is imperative.
 North Carolina Teacher
Evaluation: The Sixth Standard
 The first 5 measures on NC Teacher
Evaluation measure teacher performance.
 The sixth standard is based on individual
growth of a teacher’s students and the
school-wide growth value.

4. Non-cognitive factors have been linked to
effective performance of teachers in the
classroom.
 Does this hold true for teacher
candidates’ perceptions of their internships and
teaching?
 Up to this point we have focused strictly on
academic/cognitive measures and professional
dispositions.
 Looking for patterns in teacher candidates’ motivation-
related perceptions about their internships and teaching.
 Identify the motivation profiles that successful
Problem

5. {
Context
ECU
 averages 1,689 UG
teacher candidates
 54% ELEM
 220-250 ELEM grads
annually
 produces the most
educators in NC
 graduates have the
highest employment rate
in NC

6. ECU’s Models of
Student Teaching
Co-teaching is defined as two or
more teachers working together with
groups of students. They share
responsibility for planning, delivery,
and assessment of instruction, as
well as the organization of the
physical space.
…Or three
Traditional
co-teaching
1:1
co-teaching
2:1

7. 1:1 Co-Teaching Model
2:1 Co-Teaching Model

8. Method
Linear Analysis: t Tests
● RQ1: Do students in co-teaching internships perform differently on the
edTPA?
Multivariate, Person-Centered Analyses: Cluster analysis to better understand
student motivation in our program
● RQ2: Can students’ perceptions of their internships and teaching be used to
categorize them into groups of student teachers with similar motivation
profiles?
● RQ3: Are the clusters related to theoretically-correlated variables (edTPA,
engagement, effort, relatedness)? (measure of predictive validity)
● RQ4: Are students in co-teaching internships disproportionately assigned to
specific clusters?

9. Participants
● N = 192
● 187 female (97.4%), 5 male (2.6%)
● Age M = 24.84 (Min. = 21, Max. = 49, SD = 6.197)
● 162 identified as White (84.4%), 11 as Black or African American
(5.7%), 12 as Hispanic or Latino/a (6.3%), 5 as two or more races
(2.6%), 1 as Native American or Pacific Islander (0.5%), and 1 as
Asian (0.5%)
● Co-teaching:
○ 1:1 → 18 students (9.4%)
○ 2:1 → 11 (5.7%)
○ 1:2 → 3 (1.6%)
○ 2:2 → 2 (1%)

10. Comparing students in co-teaching internship with those placed in a traditional
internship
** p ≤ .01
RQ1: Independent Samples t Tests
Scale t df M diff.
Co-teach
M (SD)
Traditional
M (SD)
edTPA 2.692** 190 1.87 48.32 (4.67) 46.45 (3.44)
Program GPA 0.207 54.46 0.01 3.54 (0.30) 3.53 (0.35)

11. RQ1: Guiding questions
● How is the ECU model similar to where you teach?
● Your co-teaching model?
● What does the non-significant program GPA tell us?

12. RQ2 - RQ4: What is cluster analysis?
● Multivariate, exploratory method
● Individuals (i.e., “cases”) are grouped together based on similarities in the
pattern of their responses on several variables (forming into profiles with
similar characteristics)
● I’m indicating “responses” here because the examples we’re talking about
have to do with measuring students’ motivation-related perceptions.

13. Cluster Analysis in Concept
A
D
E
B
G
C
F
H

14. A student’s response on 4
dimensions (for example):
1. Expectancy for success
2. Utility value
3. Interest value
4. Attainment value
A
D
E
B
G
C
F
H
Cluster Analysis in Concept

15. ● Finds students with similar patterns in responses and groups them
together
A
D
E
B
G
C
F
H
Cluster Analysis in Concept

16. A D EB
G
C
F
H
Cluster Analysis in Concept
● Finds students with similar patterns in responses and groups them
together

17. A D E
B G
C
F
H
Cluster Analysis in Concept
● Finds students with similar patterns in responses and groups them
together

18. A D E
B G
C F H
Cluster Analysis in Concept
● Finds students with similar patterns in responses and groups them
together

19. ●Similarities?
A
D
E
B
G
C
F
H
Expect. Utility Interest Attain.
H H H H
Cluster Analysis in Concept

20. ●Similarities?
A
D
E
B
G
C
F
H
Expect. Utility Interest Attain.
H H H H
Expect. Utility Interest Attain.
M M M M
Cluster Analysis in Concept

21. ●Similarities?
A
D
E
B
G
C
F
H
Expect. Utility Interest Attain.
H H H H
Expect. Utility Interest Attain.
M M M M
Expect. Utility Interest Attain.
L L L L
Cluster Analysis in Concept

22. ● Interesting patterns
A
D
E
B
G
C
F
H
Expect. Utility Interest Attain.
H L M H
Expect. Utility Interest Attain.
M M M M
Expect. Utility Interest Attain.
L VL VL L
Cluster Analysis in Concept

23. RQ2: Measures
Scale Items α Example item
Cost 4 .828
“Because of other things that I do, I don’t have time to put
into my internship.”
Ability Perceptions 3 .748
“How have you been doing at teaching in your internship
this year?”
Utility Value 2 .698
“Compared to your other courses, how useful is what you
learn in your internship?”
Interest Value 2 .581 “How much do you like your internship?”
Attainment
Value/Identity
4 .785
“It matters to me how well I do in my teaching.”
Autonomy/
Empowerment
5 .938
“I have control over how I learn the course content.”
Preference for
autonomy
3 .936
“The amount of control I have over what I do in my
internship is:”

24. RQ2: Cluster Analysis
● 10 outliers removed for cluster analysis
● Cluster analysis n = 181
● 2-step cluster procedure: (1) hierarchical agglomerative, (2) k-means

25. Variable 1 2 3 4
Cost 3.89 2.89 5.14 1.87
Ability Perceptions 4.39 4.81 5.16 5.12
Utility Value 4.67 5.68 5.63 5.83
Interest Value 4.00 5.15 5.56 5.87
Attainment Value (ID) 5.50 5.78 5.75 5.95
Autonomy/Empowerment 3.32 4.87 5.39 5.72
Preference for autonomy 3.03 4.00 4.68 4.15
Cluster N 12 54 34 81
RQ2: Four Cluster Solution (N = 181)

26. RQ2: Four-Cluster Solution

27. 4: Very high motivation
3: High motivation, high cost, too much autonomy
2: Very high utility, high motivation, just right autonomy, low cost
1: Moderate motivation and cost, high interest, too little autonomy
RQ2: The Clusters

28. RQ2: Guiding questions
● What might these clusters mean for you?
● Do they remind you of your students? Would this be what you would expect in
your context?

29. RQ3: Follow-up analyses measures
Scale Items α Example item
Effort 3 .719
“I put a lot of effort into my internship.”
Cognitive
engagement 3 .660
“In my internship, I keep track of how much I
understand what I'm doing, not just if I am doing what
I'm supposed to do.”
Relatedness with
CT
5 .738
“I get along with my clinical teacher.”

30. RQ3: One-Way ANOVAs With Post Hoc Tests
Variable SS df MS f
edTPA Between
Within
Total
135.96
2438.07
2574.02
3
177
180
45.319
13.774
3.29*
Effort Between
Within
Total
3.37
23.63
27.00
3
177
180
1.124
0.134
8.42**
Cognitive
Engagement
Between
Within
Total
11.74
38.86
50.59
3
177
180
3.912
0.220
17.82**
Relatedness
with CT
Between
Within
Total
73.27
87.65
160.93
3
177
180
24.425
0.495
49.32**
* p < .05
** p < .001

31. RQ3: Guiding questions
● What patterns do you see? Why do you think they might be occurring?
● Which clusters have similar values for the variables? Why?

32. RQ4: Are co-teaching interns in any particular
clusters?
Cluster Number Traditional Co-Teach Total
Count 10 2
12
Expected Count 9.8 2.2
Count 45 9
54
Expected Count 44.2 9.8
Count 29 5
34
Expected Count 27.8 6.2
Count 64 17
81
Expected Count 66.2 14.8
Chi-square test, χ2 (3, n = 181) = .805, p = .848

33. RQ4: Guiding questions
● Although the chi-square test wasn’t significant, we only tested a small sample
of co-teaching interns. What might happen when our sample is increased? (We
are collecting more data right now.)
● Should any of these results affect how we place students in their internships?
● Do these results affect how well we understand our students?
● Should this affect our program decisions? If so, how?
● What should the next steps be?

34. Implications?
● If these are malleable factors, then are there ways to move teacher candidate
values higher on some of these factors to increase motivation, thereby
potentially increasing edTPA scores?
● Can you affect teacher candidate perceptions of the cost of the
internship/teaching? If these are malleable, how can we change them?
● Cross-state/cross-university collaboration to increase the numbers. By creating
cross-state and cross-university collaboration we can increase the numbers
and see how his research plays out on a larger scale.

35. ● Collect qualitative data on student teacher
perceptions
● Adding data this fall and spring to increase the N to
see if trends continue.
● Continue research to determine other noncognitive
factors that contribute to teacher effectiveness and
retention.
Next steps

36. CONTACT US WITH QUESTIONS
DR. JESSICA CHITTUM CHITTUMJ15@ECU.EDU
DR. KRISTEN CUTHRELL CUTHRELLMA@ECU.EDU
DR. CHRISTINA TSCHIDA TSCHIDAC@ECU.EDU
DR. ELIZABETH FOGARTY FOGA0017@UMN.EDU
DR. JOY STAPLETON STAPLETONJ@WINTHROP.EDU