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
Transcript
CynicismAt 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.
• 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…
• Demographics– Major (15)– Gender– Time-of-Day– Age
• Output…– Business Types– Technical Types– Science Types– Psychology Types– Arts Types
• 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…
• Demographics– Major (15)– Gender– Time-of-Day– Age
Logistic Regression (11 Dependent Variables and 14 Predictor Variables)
11 Pedagogical Techniques
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
(Odds ratio = .368) (better rating from males)
Fiction Types 3.88/< .05
(Odds ratio = .757)(poorer rating from the “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
(Odds Ratio = .17)(poorer rating from Kinesiology Majors)
Gender 6.21/< .01
(Odds ratio = .467)(better rating from females)
Time 4.65/< .05
(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 Wald/ Sig.
Odds Ratio
Science Types
4.88/< .05
(Odds Ratio = 1.297)(better rating from the “Science Types”)
Arts Types 5.74/< .05
(Odds Ratio = 1.33)(better rating from the “Arts Types.”)
Time 3.89/< .05
(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
(Odds Ratio = 1.60)(better rating from TV-Types)
Time 4.43/<.05
(Odds Ratio = 1.22)(better rating early in the day
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
10.33/< .001
(Odds Ratio = 1.63)(better rating from Psych-Types)
News-Types 7.93/<.005
(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 Wald/ Sig. Odds RatioNews-Types 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
4.25/<.05
(Odds ratio = 4.42)(better rating from History Majors)
Science Majors
5.32/< .025
(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
