Social Networks, Pedagogy, and Weak Ties:The Impact of Collaborative Social Capital on Grades and Relevance for Political Science *

Stephen Bird, Boston University - Political Science – sbird@bu.edu

APSA Teaching and Learning ConferenceWashington, DC, February 19-21 2005

* Many thanks to Steve Borgatti (Boston College) for important comments in the development of this research.

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social capital and social network theory multi-disciplinary:

political science, organizational studies, sociology, economics, anthropology, psychology, etc.

general network theory: physics, biology social capital refers to those stocks of social trust, norms

and networks that people can draw upon to solve common problems.

networks of civic engagement, such as neighborhood associations, sports clubs, and cooperatives,

the denser these networks, the more likely that members of a community will cooperate for mutual benefit.

occurs even in the face of persistent problems of collective action (tragedy of the commons, prisoner's dilemma etc.)

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relevance to politics political participation and civic engagement – voting

and campaigns, understanding politics, parent-teacher assoc., religious groups, civic organizations

De Tocqueville considered US democracy a success because of extensive civic organizations role of black churches in civil rights movement

leadership issues (carter network and its implications) how the network is shaped can affect ability to lead, etc.

information diffusion Howard Dean’s grass roots internet campaign

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multiple channels information flows (e.g. learning about jobs, learning

about candidates running for office, exchanging ideas at college, etc.) depend on social capital

norms of reciprocity (mutual aid) dependent on social networks

bonding networks that connect folks who are similar sustain particularized (in-group) reciprocity

bridging networks that connect individuals who are diverse sustain generalized reciprocity

collective action depends upon social networks it also can foster new networks

broader identities, solidarity encouraged by social networks. translate mentality from “I” into a “we”

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four main points from this specific research

impact, collaboration,

difference between diffusion and collaboration

weak ties, why are they important why might they be less important in

collaborative environment (but turn out to be just as important!)

benefit limitations

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four models of learning

social network

structural holes (broker/bridge)

collaborative/interactive

information diffusion

1. high intensity (few

ties)

2. weak ties (more ties)

3. high intensity (few

ties)

4. weak ties (more ties)

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an experiment in PO 101

Survey “interactions” between students

• distinction between social and academic interactions

additional controls• political affiliation – dummy variable• motivation• year in school (freshman, senior)

• no effect

probable collaboration (proof later)

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Social Networks and Grades Questionnaire – Stephen Bird (sbird@bu.edu)(all information will be kept confidential, names will be changed in the finished analyses and reports)Your NameYear Circle one: Freshman Sophomore Junior Senior Graduate/otherParty affiliation?Circle one: None Republican Democrat Green Libertarian Communist

Other:_______________ How motivated are you to do well in this class?Circle one: (least motivated) 1 2 3 4 5 6 7 8 9 10 (very motivated) Academic Interaction:Write down the names (first and last –go find them and ask them their name if you don’t know it) of people in this class with whom you interact on an academic basis (this means study partner, someone that you have casual or intense conversations about politics or other aspects of class – in or outside of class, someone that you sit next to on purpose for academic reasons, etc.). Put each name in one box only, more than one name can go in each box: Minimal interactionMedium interactionIntense interactionYou can have academic and social interactions with the same person and the degree of interaction doesn’t have to be the same. E.g. you could have an intense academic interaction with “Morgan Gerr” and a minimal social interaction with “Morgan Gerr.”Social/Friendship Interaction:Write down the names (first and last –go find them and ask them their name if you don’t know it) of people in this class with whom you interact on a social basis (this means a friend, an acquaintance, someone you party with, a “partner” – i.e. boyfriend, girlfriend, etc.). Put each name in one box only, more than one name can go in each box: Minimal interactionMedium interactionIntense interaction

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impact

clear correlation between grades and network interaction

measurements centrality (freeman degree) structural holes

issues: causality other controls (address later in

presentation)

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01

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Ave

rag

e D

eg

ree

0 1 1.67 2 2.33 2.67 3 3.33 3.67 4

Entire class fully dichotomized

Average Degree per Grade Category

mean of degree

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01

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4G

rad

e

0 2 4 6 8 10Number of Interactions

95% CI Fitted valuesgrade

entire class fully dichotomized - quadratic regression

Degree Centrality and Grade

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legend for network map color of node = student grade

yellow = a green = b orange = c black = d or f

size of node = “degree” network rating smallest circle = 0 interactions largest = 9 interactions

line width = intensity of interaction thin line = low intensity medium = medium thick = high intensity

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regression results – effect on grade

* = Significant at 90%**= Significant at 95%***=Significant at 99%

Variable Descriptors: Coefficient(standard error)standardized beta coefficient

All Models above use dichotomized results for interactions (i.e. all interaction “values” have been reduced to 1)

Variables Model 1 Model 2 Model 3 Model 4

Degree centrality 0.06 **(0.027)0.15

0.07 **(0.026)0.17

0.14 ***(0.024)0.32

Structural Holes 0.07 **(0.030)0.17

Student Motivation 0.14 ***(0.032)0.31

0.14 ***(0.032)0.30

0.14 ***(0.032)0.31

Student year -0.04(0.058)-0.05

Left Affiliation (dummy variable)

0.12(0.095)0.08

N 197(resp. only)

197(resp. only)

197(resp. only)

294(entire class)

R Square 0.15 0.14 0.14 0.10

F Value 8.66 16.40 16.23 33.97

Constant 1.69 1.69 1.68 2.63

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collaboration or diffusion? does the difference matter? central clique versus entire class

(known as “component”) if diffusion process then the central

clique component will have a greater effect on grades than small components or “single” components.

if collaboration then there should be no effect from the component.

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regression results – diffusion or collaboration

* = Significant at 90%**= Significant at 95%***=Significant at 99%

Variable Descriptors: Coefficient(standard error)standardized beta coefficient

All Models above use dichotomized results for interactions (i.e. all interaction “values” have been reduced to 1)

Variables Model 1 Model 2

Structural Holes 0.15 ***(0.028)0.30

0.14 ***(0.035)0.27

Component(dummy variable)

0.087(0.111)0.05

N 294(entire class)

294(entire class)

R Square 0.10 0.097

F Value 30.74 15.66

Constant 2.65 2.63

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intensity versus “weak ties” counter-intuitive result

normally weak ties are reasonable result in context of information diffusion • (per Granovetter “weak ties”, and Burt

“structural holes”) hypothesis: intensity of ties should have

an effect in a collaborative interaction research actually demonstrates reduced

effect from more intense ties

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weighted versus dichotomized models (intensity versus “weak ties”)- “dichotomization”of ties means “value” of a weighted tie is reduced to “1”Variables Model 1

Weighted tiesModel 2‘1’ ties purged‘2&3’ ties dichotomized

Model 3all ties dichotomized

Model 4Weighted ties

Model 5‘1’ ties purged‘2&3’ ties dichotomized

Model 6all ties dichotomized

Degree centrality

0.263*** 0.251*** 0.282*** 0.312*** 0.286*** 0.323***

N 197(resp. only)

197(resp. only)

197(resp. only)

294(entire class)

294(entire class)

294(entire class)

R Square 0.069 0.063 0.080 0.097 0.082 0.104

F Value 14.590 13.233 16.942 31.497 26.063 33.971

Constant 1.69 1.69 1.68 2.63 2.65 2.63

* = Significant at 90%** = Significant at 95%*** = Significant at 99%

Model 6 demonstrates strongest r2 and best coefficient: all ties dichotomized

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issues Performance (i.e. grades) as learning lack of controls:

previous GPA study time (solo study time) attendance TF influence/grading variability

• Although grading consistency between TFs was checked and corrected for in two circumstances…

can groups be “forced” within teaching process?

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implications – teaching teaching political science

(or anything else really…) foster collaborative learning

• Learning activities that increase interactions and comfort with other students

not necessary to increase intensity of ties• classroom processes that are brief but expose

students to a variety of collaborative learning sources

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implications – research research in political science

(and other fields affected by social networks) implications for entrepreneurial responses of nations to

globalization pressures• Castells (Network Society), Hall & Soskice (Varieties of

Capitalism) implications for civic engagement, public participation, and

social capital• ground-up and top-down models• Putnam, Nin, Bo Rothstein etc.

implications for dispute resolution and conflict literature both in American and IR fields

Castells, Manuel. 2000. The Rise of the Network Society. Second ed, The Information Age: Economy, Society and Culture. Malden, MA: Blackwell Publishing.

Hall, Peter A., and David Soskice, eds. 2001. Varieties of Capitalism: The Institutional Foundations of Comparative Advantage. New York: Oxford University Press.