Cynicism
At times I strike myself as naïve and without guile. At other
times I am clearly a cynic. To illustrate, when teaching
teachers I often encourage authentic assessment formats
and allow my students the opportunity to view the work of
others as models. Guileless optimism! Each year I am
disheartened by the number of models that are stolen. Our
future teachers are stealing from the secretary who is
monitoring the samples, their “peers” who prepared them,
the professor who arranged this, their classmates who would
benefit from viewing them, their future students who need
good role models, and on and on… These are the teachers
who will be teaching your children. Thus my cynicism; and I
wonder about this resident, this squatter in my being.
Cynicism in the classroom…
I’m not the only one who is cynical. I
have often noted cynicism on the part of
my students with respect to some things
I would do in the classroom. Some
things I thought they would like, they
hated. Or at least some of them hated
them. Over the years I have reflected
on this cynicism, monitored classroom
reactions, considered various
determinants of such cynicism, collected
data, and expanded my own cynicism
about cynicism, and optimism about
cynicism.
This Presentation
• Cynicism with respect to various teaching techniques
• I am interested in knowing how people respond to
various techniques I use in class—particularly the
degree of cynicism with respect to technique.
• I am interested in detecting some of the
determinants of such cynicism.
• I am interested in reflecting on the politics, the
philosophy and the psychology of cynicism.
The sample
• I teach about 750 students who have at
least the baccalaureate degree, and are in
the process of acquiring the B.Ed. degree
and their teaching certification.
• 506 of these individuals responded to my
request for feedback on various teaching
techniques.
The Instrument
• They were asked to rate 11 different
techniques.
• They were asked demographic questions.
• They were asked questions which related to
their preferential modes of information
intake.
• They were asked questions that could be
linked to information output styles.
Pedagogical Techniques
NANO
Lessons
Outline
Pedagogical Techniques
•
•
•
•
•
•
•
•
•
•
•
Pre-class --Music
Pre-class --PowerPoint
Sound Bites –Brief Story Experiences (self/others)
Sound Bites --Sample Projects
Sound Bites --Nano-lessons
Sound Bites --Gimmicks (The Pink Horn)
WEB --Outline
WEB --Tour
WEB --Resources
Traditional --“THE STORY”
Traditional --Research Studies
Demographics, Input and Output styles…
• Output…
• Demographics
–
–
–
–
Major (15)
Gender
Time-of-Day
Age
–
–
–
–
–
Business Types
Technical Types
Science Types
Psychology Types
Arts Types
• Input…
–
–
–
–
–
Literate Types
TV Types
Performance Types
News Types
Fiction Types
Question # 1
• What is the “Cynicism Rate” for the 11
pedagogical techniques?
Attitudinal Diversity
Detecting Pedagogical Cynicism
Rate
Of
Cynicism
70
60
%
50
40
30
20
10
0
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Pedagogical Activity
Figure 2.
Cynicism Rates
•
•
•
•
Cynicism rates are substantial (20% to >60%)
Lowest rate is for the Sound-Bite—Little Stories
Highest rate is for the Traditional—Big Story
WEB Techniques (28-42% cynicism rates).
Model…
Input…
Literate Types
TV Types
Performance Types
News Types
Fiction Types
• Demographics
–
–
–
–
Major (15)
Gender
Time-of-Day
Age
Output…
Business Types
Technical Types
Science Types
Psychology Types
Arts Types
Question # 2
• Does the model based on Input-Style,
Output-Style and Demographics predict
cynicism rates?
Logistic Regression
(11 Dependent Variables and 14 Predictor Variables)
11 Pedagogical Techniques
Input…
Literate Types
TV Types
Performance Types
News Types
Fiction Types
• Demographics
–
–
–
–
Major (15)
Gender
Time-of-Day
Age
Output…
Business Types
Technical Types
Science Types
Psychology Types
Arts Types
Question # 3
• Do specific variables related to Input-Style,
Output-Style and Demographics predict
cynicism rates?
Logistic Regression for Pre-Class “Music”
Variable
Wald/
Sig.
Odds Ratio
Nil
Nil
Χ2 (28) = 37.31, p = .11
The Model?
Logistic Regression for Pre-Class “Powerpoint”
Variable
Wald/Sig.
Odds Ratio
Gender
10.65/
< .01
3.88/
< .05
(Odds ratio = .368) (better rating
from males)
(Odds ratio = .757)
(poorer rating from the “Fiction
Types”)
Fiction Types
Χ2 (28) = 37.91, p = .10
The Model?
Logistic Regression for “Little Stories”
Variable Wald/Sig Odds Ratio
.
Gender
10.61/
<.001
(Odds ratio = .373
(better rating from females)
Χ2 (28) = 53.96, p < .01 (82.1% correctly classified)
(Wald = 154.15, p < .000; Odds Ratio = 4.34)
The Model?
Logistic Regression for “Sample Projects”
Variable
Wald/Sig. Odds Ratio
Kinesiology
Majors
5.16/
< .05
Gender
6.21/
< .01
Time
4.65/
< .05
(Odds Ratio = .17)
(poorer rating from Kinesiology
Majors)
(Odds ratio = .467)
(better rating from females)
(Odds ratio = .793)
(better rating early in day)
Χ2 (28) = 48.49, p < .01 (80.6% correctly classified)
(Wald = 137.48 , p < .000; Odds Ratio = 3.83)
The Model?
Logistic Regression for “Nano-Lessons”
Variable
Science
Types
Arts Types
Time
Wald/
Sig.
4.88/
< .05
5.74/
< .05
3.89/
< .05
Odds Ratio
(Odds Ratio = 1.297)
(better rating from the “Science Types”)
(Odds Ratio = 1.33)
(better rating from the “Arts Types.”)
(Odds ratio = .842)
(better rating early in the day)
Χ2 (28) = 49.07, p < .01 (67.6% correctly classified)
(Wald = 28.0, p < .000; Odds Ratio = 1.67)
The Model?
Logistic Regression for “Gimmicks”
Variable
Wald/
Sig.
Odds Ratio
Science-Types
4.37/
<.05
(Odds Ratio = 1.276)
(better rating from Science-Types)
Χ2 (27) = 29.41, p = .39 The Model?
Logistic Regression for WEB “Outline”
Variable
Wald/ Sig. Odds Ratio
Technical
Types
3.70/
< .05
(Odds Ratio = 1.27)
(better rating from “Tech-Types”)
TV Types
15.23/
<.001
4.43/
<.05
(Odds Ratio = 1.60)
(better rating from TV-Types)
(Odds Ratio = 1.22)
(better rating early in the day
Time
Χ2 (28) = 43.35, p < .025 (72.0% correctly classified)
(Wald = 85.7, p < .000; Odds Ratio = 2.6)
The Model?
Logistic Regression for “WEB” Tour
Variable
Wald/Sig.
Odds Ratio
Psychology
Types
News-Types
10.33/
< .001
7.93/
<.005
(Odds Ratio = 1.63)
(better rating from Psych-Types)
(Odds Ratio = 1.39)
(better rating from News-Types)
Χ2 (28) = 47.79, p < .01 (75% correctly classified)
(Wald = 86.96, p < .000; Odds Ratio = 2.67)
The Model?
Logistic Regression for WEB “Resources”
Variable
News-Types
Wald/ Sig. Odds Ratio
4.28/ <.05
(Odds Ratio = 1.24)
(better rating from “News-Types”)
Performing Arts
Major
5.84/ <.05
(Odds Ratio = 4.36)
(better rating from “Performing Arts Majors”)
History Major
7.38/ <.05
(Odds Ratio = 5.71)
(better rating from “History Majors”)
Criminology
Major
6.15/ <.01
(Odds Ratio = 18.34)
(better rating from “Criminology Majors”)
Science Major
5.06/ <.05
(Odds Ratio = 3.64)
(better rating from “Science Majors”)
Χ2 (28) = 43.35, p < .025 (64.0% correctly classified)
(Wald = 17.73, p < .000; Odds Ratio = 1.49)
The Model?
Logistic Regression for Traditional-- “Research”
Variable
Wald/
Sig.
Odds Ratio
History
Majors
Science
Majors
4.25/
<.05
5.32/
< .025
(Odds ratio = 4.42)
(better rating from History Majors)
(Odds ratio = 4.32)
(better rating from Science Majors)
Χ2 (28) = 34.13, p = .16 The Model?
Logistic Regression for Traditional-- “The Big Story”
Variable
Wald/
Sig.
Odds Ratio
Literate
Types
4.45/
< .05
(Odds ratio = 1.25)
(better rating from Literate-Types)
Fiction
Types
4.55/
< .05
(Odds ratio = 1.27)
(better rating from Fiction-Types)
Χ2 (28) = 29.44, p = .34 The Model?
Effects
• Sometimes the model was significant.
• Sometimes the model was not significant but there were
individual variables which were significant predictors.
• A picture begins to emerge about the type of variables
that relate to a cynical attitude:
– Gender
– Time of Day
– Major
– Different techniques generate different responses
– Individual Information Input Styles
– Individual Information Output Styles
Implications?
• Can’t please everyone all the time.
• There is a substantial resident cynicism rate.
• Techniques you like may not be liked by your
students, or at least some of your students.
• Determinants of cynical attitudes are somewhat
complex:
–
–
–
–
–
–
Related to differing techniques
Related to input style
Related to output style
Related to background (Major)
Related to Gender
Related to Time-of-Day