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Bridging Theory and Practice in the Classroom:
A Reappraisal Intervention for Test Anxiety
Bridgette Martin HardStanford University
PEDAGOGYPSYCHOLOGY
NeuroscienceCognitive
Developmental Affective Science
Clinical
Social
Content
Assessment
Presentation
Engagement
Technology
Strengthen teaching practices
Sharpen knowledge and theory
PEDAGOGYPSYCHOLOGY
PEDAGOGYPSYCHOLOGY
AssessmentAffective Science
Assessment
Affective Science
For students, tests:• Determine grade• Gateways to other
opportunities (e.g., jobs, education)
People often respond to situations like these with feelings of anxiety.
Anxiety: A heightened state of physiological arousal accompanied by future-oriented feelings of distress.
0%
10%
20%
30%
40%
50%
60%
Calm down Ignore your anxiety
Tell yourself your anxiety could help
you
Distract yourself
Get excited
% Endorsed as “Most Likely” for an instructor to suggest
Mohler, Hard, Lam, & Brady (in prep)
Current college students at public
universityN = 389
Important test Anxiety
Bandura (1997); Beilock (2011; 2008); Cassady & Johnson (2002); Wine (1980)
Worry
Performance?
Important test Anxiety
Bandura (1997); Beilock (2011; 2008); Cassady & Johnson (2002); Wine (1980)
Worry
Performance+
-?
Important test Anxiety Performance+
Bandura (1997); Beilock (2011; 2008); Cassady & Johnson (2002); Wine (1980)
Worry
-?
-Worry
Important test Anxiety Performance+
Bandura (1997); Beilock (2011; 2008); Cassady & Johnson (2002); Wine (1980)
?
Worry
Important test Anxiety Performance
COGNITIVE REAPPRAISAL
Change the meaning
+
Gross (2002; 2014)
-?
Can reappraisal help?
Jamieson, Mendes, Blackstock, & Shmader (2010)
“…recent research suggests that arousal doesn’t hurt performance on these tests and can even help performance... If you find yourself feeling anxious, simply remind yourself that your arousal could be helping you do well”
• Students planning to take the GRE.
• Half randomly assigned to receive reappraisal message:
500
550
600
650
700
750
800
GRE$Math$) Practice GRE$Math$) Actual
GRE
Sco
reControl Reappraisal
Practice GRE MathN = 60
Actual GRE MathN = 28
Error Bars: +/- 1 SEM Jamieson, Mendes, Blackstock, & Shmader (2010)
500
550
600
650
700
750
800
GRE$Math$) Practice GRE$Math$) Actual
GRE
Sco
reControl Reappraisal
Practice GRE MathN = 60
Actual GRE MathN = 28
Error Bars: +/- 1 SEM Jamieson, Mendes, Blackstock, & Shmader (2010)
Motivating questions
• Moderation?
First-year students
When you think about taking the exam this Thursday, to what extent do you:
3
3.5
4
4.5
5
feel anxious? feel worried?
Ratin
g (7
-Poi
nt S
cale
)
First-year (N = 130) Upper-year (N = 115)
*d = .45
*d = .37
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
Motivating questions
• Moderation?
• Durability?
Method• 431 introductory psychology students,
fall and winter quarter– 55% first-year, 58% female, 64% White
Brady, Hard, & Gross (invited revision)
Method• 431 introductory psychology students,
fall and winter quarter– 55% first-year, 58% female, 64% White
• Random assignment to receive reappraisal message in email from instructor the night before first exam.
Brady, Hard, & Gross (invited revision)
Standard condition
Brady, Hard, & Gross (under review, R&R)
Brady, Hard, & Gross (invited revision)
Reappraisal condition
Brady, Hard, & Gross (invited revision)
Reappraisal conditionPeople think that feeling anxious while taking a test will make them do poorly on the test. However, recent research suggests that arousal doesn’t generally hurt performance on tests and can even help performance. People who feel anxious during a test might actually do better. This means that you shouldn’t feel concerned if you do feel anxious while studying for or taking tomorrow’s exam. If you find yourself feeling anxious, simply remind yourself that your arousal could be helping you do well.
Method• 431 introductory psychology students, fall
and winter quarter– 55% first-year, 58% female, 64% identified as
White• Random assignment to receive
reappraisal message in email from instructor the night before first exam
• DVs: – Anxiety– Worry– Exam performance
Brady, Hard, & Gross (invited revision)
Hypotheses
1. Moderation: – First-years, but perhaps not upper-years,
will benefit: showing reduced worry and enhanced performance on first exam.
2. Durability:– Benefits of reappraisal may extend
beyond first exam to overall course performance.
Brady, Hard, & Gross (invited revision)
First-year students: Emotional experience
2
2.5
3
3.5
4
4.5
5
Anxiety Worry
Ratin
g (7
-poi
nt s
cale
)
Standard Reappraisal
*d = .32
n.s.d = .11
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
2
2.5
3
3.5
4
4.5
5
Anxiety Worry
Ratin
g (7
-poi
nt s
cale
)
Standard Reappraisal
*d = .32
n.s.d = .11
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
First-year students: Emotional experience
First-year students: Performance
85
86
87
88
89
90
91
Exam 1 Final Grade
Scor
e (o
ur o
f 100
)
Standard Reappraisal
*d = .32
*d = .29
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
First-year students: Performance
85
86
87
88
89
90
91
Exam 1 Final Grade
Scor
e (o
ur o
f 100
)
Standard Reappraisal
*d = .32
*d = .29
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
Upper-year students: Emotional experience
2
2.5
3
3.5
4
4.5
5
Anxiety Worry
Ratin
g (7
-poi
nt s
cale
)
Standard Reappraisal
*d = .30
n.s.d = .13
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
Upper-year students: Emotional experience
2
2.5
3
3.5
4
4.5
5
Anxiety Worry
Ratin
g (7
-poi
nt s
cale
)
Standard Reappraisal
*d = .30
n.s.d = .13
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
85.5
86
86.5
87
87.5
88
88.5
89
89.5
Exam 1 Final Grade
Scor
e (o
ur o
f 100
)
Standard Reappraisal
n.s.d = .07
n.s.d = .06
Upper-year students: Performance
Brady, Hard, & Gross (invited revision)Error Bars: +/- 1 SEM
Ongoing research
• Replicating at other institutions, exploring other potential moderators, boundary conditions.
• Exploring longitudinal effects from original sample, now several years later.
Strengthen teaching practices
Sharpen knowledge and theory
PEDAGOGYPSYCHOLOGY
• Shannon Brady• James Gross• Cayce Hook• Amy Lam• Angela Lee• Marleyna Mohler• Jeanne Tsai• Greg Walton• Elizabeth Wong• Yun Lucy Zhang
Research Collaborators:
Teaching Demonstration:Correlation for Introductory Psych
Bridgette Martin HardStanford University
Is it a good idea to multitask on a laptop during class?
Correlational method
1. What is it?
2. Correlation in action: multitasking
3. Interpreting correlational data
4. The limits of correlation
Correlational method
1. What is it?
2. Correlation in action: multitasking
3. Interpreting correlational data
4. The limits of correlation
What is the correlational method?
• Scientific approach that examines how variables are related to each other.
?
• Correlation: The tendency of two variables to change together.
What is the correlational method?
What is the correlational method?
Positive correlation
What is the correlational method?
Height (cm)
Wei
ght (
kg)
scatterplot
Heinz, Peterson, Johnson, & Kerk (2003)
140
150
160
170
180
190
200
30 50 70 90 110 130
What is the correlational method?
?
Negative correlation
What is the correlational method?
Mean Outdoor Temp (ºF)
Ga
s Use
/Da
y (T
herm
s)
scatterplot
Carver (1998)
0
2
4
6
8
10
12
14
0 20 40 60 80 100
How many real-world examples can you think of in 30 s?
Positivecorrelation
Negative correlation
Left side Right side
Correlational method
1. What is it?
2. Correlation in action: multitasking
3. Interpreting correlational findings
4. The limits of correlation
Is it a good idea to multitask on a laptop during class?
Is it a good idea to multitask on a laptop during class?
• How is multitasking on laptops related to performance in the class?
?
Is it a good idea to multitask on a laptop during class?
• How is nonacademic multitasking on laptops related to performance in the class?
?
Positive or Negative?
?
• How is nonacademic multitasking on laptops related to performance in the class?
What is your hypothesis?
Negative
• How is nonacademic multitasking on laptops related to performance in the class?
• We first need to define, or operationalizeour variables.
• Operationalize/operational definition:– Translate the variable we want to assess into a
specific procedure or measurement.
How could you operationalize:“nonacademic multitasking”?
• We first need to define, or operationalizeour variables.
• Operationalize/operational definition:– Translate the variable we want to assess into a
specific procedure or measurement.
How could you operationalize:“performance in the class”?
• Nonacademic multitasking: Proxy server logs http requests – Sum & categorize http requests
to estimate “nonacademic” minutes online (e.g., Twitter, Gmail).
• Performance: Cumulative final exam score
Ravissa, Uitvlugt, & Fenn (2016)
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
Cumulative final exam score
scatterplot
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
Cumulative final exam score
Ravissa, Uitvlugt, & Fenn (2016)
N = 84
scatterplot
Correlational method
1. What is it?
2. Correlation in action: multitasking
3. Interpreting correlational data
4. The limits of correlation
Interpreting correlational data
• What is the direction and strength of the relationship?
Cumulative final exam score
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
Ravissa, Uitvlugt, & Fenn (2016)
What is the direction and strength of this relationship?
Correlation coefficient (r): A measure of the strength and direction of the linear relationship between two variables.
0.0-1.0 1.0Zero (null)correlation
Negativecorrelation
Positivecorrelation
strong strongweakweak
Positive
02468
10121416
0 5 10 15
r = 1.00Negative
0
10
20
30
40
50
0 5 10 15
r = -1.00
Correlation coefficient (r): A measure of the strength and direction of the linear relationship between two variables.
Variable X Variable X
Var
iabl
e Y
Var
iabl
e Y
PositiveNegativer = .80
0
15
30
45
60
75
90
2 4 6 8 10 12 14 16 18 200
r = .54
2 4 6 8 10 12 14 16 18 2000
15
30
45
60
75
90
r = -.33
2 4 6 8 10 12 14 16 18 2000
15
30
45
60
75
90
r = -.85
2 4 6 8 10 12 14 16 18 2000
15
30
45
60
75
90
Null
2 4 6 8 10 12 14 16 18 200
r = 0
0
15
30
45
60
75
90
Cumulative final exam score
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
Ravissa, Uitvlugt, & Fenn (2016)
What is the direction and strength of this relationship?
Cumulative final exam score
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
r = - .25
Ravissa, Uitvlugt, & Fenn (2016)
What is the direction and strength of this relationship?
Correlations with final exam score:
Interest in the class
r = - .25
r = .33
r = .26
Ravissa, Uitvlugt, & Fenn (2016)
Academic multitasking
r = .36
r = ?
Correlations with final exam score:
Interest in the class
r = - .25
Ravissa, Uitvlugt, & Fenn (2016)
Academic multitasking
r = .36
r = .09r = .33
r = .26
Interpreting correlational data
• What is the direction and strength of the relationship?
• Does the relationship found in this sample reflect the larger population?
Cumulative final exam score
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
r = - .25
Ravissa, Uitvlugt, & Fenn (2016)
Does the relationship found in this sample reflect the larger population?
N = 84
Variable Y
140
160
180
200
220
240
260
15 35 55 75 95Variable X
Ravissa, Uitvlugt, & Fenn (2016)
Imagine a population of 500 people, measured on variables X and Y:
r = 0
Variable Y
140
160
180
200
220
240
260
15 35 55 75 95Variable X
Ravissa, Uitvlugt, & Fenn (2016)
Say I randomly sample 10 people:
r = 0
140
160
180
200
220
240
260
15 35 55 75 95Variable X
Variable Y
Ravissa, Uitvlugt, & Fenn (2016)
And I get this:
Variable Y
140
160
180
200
220
240
260
15 35 55 75 95Variable X
Ravissa, Uitvlugt, & Fenn (2016)
r = -.34
With this correlation:
Variable Y
140
160
180
200
220
240
260
15 35 55 75 95Variable X
Ravissa, Uitvlugt, & Fenn (2016)
r = -.34
When you sample from a large population, you can get a correlation in your sample, just by chance.
Cumulative final exam score
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
Ravissa, Uitvlugt, & Fenn (2016)
r = - .25
N = 84
Does the relationship found in this sample reflect the larger population?
p-value(p): The probability that we would get this result, given this sample size, if the correlation in the population was actually “0”.
Cumulative final exam score
40
50
60
70
80
90
100
0 200 400 600 800Non-academic internet use
p = .02
Ravissa, Uitvlugt, & Fenn (2016)
If p < 0.05,“Statistically significant”
r = - .25
N = 84
• How is nonacademic multitasking related to performance in the class?
Negative, r = -.25, p < .05
• Does this mean that nonacademic multitasking causes worse performance?
Negative, r = -.25, p < .05
Correlational method
1. What is it?
2. Correlation in action: multitasking
3. Interpreting correlational data
4. The limits of correlation
Reasons for correlation
Reasons for correlation
The directionality problem
Reasons for correlation
The third variable problem
Name the third variable!
Ice cream sales Drowning deaths
Name the third variable!
Temperature/Season
Name the third variable!
Use of contraception Number of electrical appliances owned
Name the third variable!
Socioeconomic Status (SES)
Reasons for correlation
The third variable problem
You can measure potential third variables and use statistical techniques to test their importance.
Reasons for correlation
The third variable problem
Interest in the class
Ravissa, Uitvlugt, & Fenn (2016)
Correlation ≠ Causation
Does not necessarily =Correlation Causation
A
B
Is it a good idea to multitask on a laptop during class?
• Adam Anderson• Shannon Brady• Stephen Chew• Sandy Goss Lucas• James Gross• Bob Henderson• Beth Morling• Toni Schmader• Greg Walton• Elizabeth Wong• Jamil Zaki
• The Psych One Teaching Fellows!
Partners in Pedagogy: