Presentation for AERA 2018 - Motivation Profiles of Student Teachers: A Person-Centered Study to Guide Program Awareness and Improvement

1. Motivation Profiles of Student
Teachers: A Person-Centered Study to
Guide Program Awareness and
Improvement
Jessica Chittum
Christina Tschida
Kristen Cuthrell
East Carolina University
AERA 2018 - New York, NY
Non-Presenting Authors:
Joy Stapleton, Winthrop University
Elizabeth Fogarty, University of Minnesota
Brett Jones
Virginia Tech

2. https://bit.ly/2IUzpz7
Access the presentation at the site above

3. {
Context
ECU:
Averages 1,689 UG teacher
candidates annually
➢54% ELEM
➢220-250 ELEM grads annually
Produces the most educators in NC
Graduates have the highest
employment rate in NC

4. Method
Multivariate, Person-Centered Analyses: Cluster analysis to
better understand student motivation in our program
● RQ1: Can students’ perceptions of their internships and
teaching be used to categorize them into groups of student
teachers with similar motivation profiles?
● RQ2: Are the clusters related to theoretically-correlated
variables (engagement, effort, relatedness, grit)? (measure of
predictive validity)

5. Participants
● N = 254
● 244 female (96.1%), 10 male (3.9%)
● Age M = 25.58 (Min. = 21, Max. = 51, SD = 6.768)
● 214 identified as White (84.3%), 16 as Black or African American
(6.3%), 11 as Hispanic or Latino/a (4.3%), 6 as two or more races
(2.0%), 1 as Native American or Pacific Islander (0.4%), and 1 as
Asian (0.4%), and 6 students who did not report or selected “other”

6. 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)
● Indicating “responses” because the examples we’re talking about have to do
with measuring students’ motivation-related perceptions.

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

8. 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

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

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

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

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

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

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

15. ● 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

16. Measures
Scale Items α Example item
Cost 4 .824
“Because of other things that I do, I don’t have time to
put into my internship.”
Ability Perceptions 3 .747
“How have you been doing at teaching in your internship
this year?”
Interest Value 2 .756 “How much do you like your internship?”
Utility Value 2 .575
“Compared to your other courses, how useful is what
you learn in your internship?”
Attainment
Value/Identity
4 .841
“It matters to me how well I do in my teaching.”
Autonomy/
Empowerment
5 .921
“I have control over how I learn the course content.”
Preference for
autonomy
3 .937
“The amount of control I have over what I do in my
internship is:”

17. Cluster Analysis
● 15 outliers removed for cluster analysis
● N = 254
● 2-step cluster procedure: (1) hierarchical
agglomerative, (2) k-means

18. Variable 1 2 3 4 5
Cost 3.30 4.25 2.76 5.50 1.93
Ability Perceptions 4.47 5.02 4.82 5.18 5.12
Interest Value 3.89 5.21 5.12 5.81 5.90
Utility Value 4.58 5.45 5.58 5.63 5.84
Attainment Value (ID) 5.70 5.63 5.78 5.78 5.97
Autonomy/Empowerment 3.51 5.26 4.75 5.54 5.70
Preference for autonomy 3.00 5.70 3.91 4.13 4.13
Cluster N 19 21 67 26 121
Five-Cluster Solution (N = 254)

19. Five-Cluster Solution

20. Cluster Names
1. Lower motivation, too little autonomy
2. Somewhat high motivation, too much autonomy and high cost
3. Somewhat high motivation, high attainment and utility
4. High motivation and high cost
5. High motivation and low cost

21. Follow-up measures
Scale Items α Example item
Cognitive
engagement 3 .673
“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.”
Effort 3 .664
“I put a lot of effort into my internship.”
Relatedness with
CT
5 .763
“I get along with my clinical teacher.”
Grit 8 .717
“I am diligent.”
Career goals 2 .723
“My future career will involve teaching/education.”

22. One-Way ANOVAs With Post Hoc Tests
Variable SS df MS f
Cognitive
engagement
Between
Within
Total
14.321
50.880
65.200
4
249
253
3.580
0.204
17.521**
Effort Between
Within
Total
4.153
32.220
36.373
4
249
253
1.038
0.129
8.023**
Relatedness with
CT
Between
Within
Total
9.061
55.121
64.182
4
249
253
2.265
0.221
10.233**
Grit Between
Within
Total
6.044
57.044
63.229
4
249
253
1.511
0.230
6.579**
Career goals Between
Within
Total
125.757
121.506
247.263
4
249
253
31.439
0.488
64.428**
** p < .001

23. Note. Z scores used due to different scales.

24. 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 candidate
readiness and student outcome data?
● Can you affect teacher candidate perceptions of the cost of
the internship/teaching?
● What can we do about the students’ perceptions of having
too much or too little autonomy?
● What happens when we look at the motivation clusters by
internship type?

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

26. 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
DR. BRETT JONES BRETTJONES@VT.EDU