1. Selected Topics in Data Networking Explore Social Networks: Cliques and Sub-group (Example Application)

2. 2 Applying “Clique” to Analyse Network Social Network Analysis (SNA) Applications in Evaluating MPA Classes  evaluate instruction and teaching methods in an MPA program at the University of Central Florida (UCF) Identify patterns of interaction among students, Identify characteristics of student friendships and advice networks, Design teaching methods and interventions to facilitate student interaction Develop students’ interpersonal skills so they can learn in collaborative environments.

3. Social Network Analysis Enables the analysis of relationships between individuals, groups, teams, cliques, agencies, and organizations.  A network analyst would be concerned with how an actor is located in the network how that very structure is created by the relationships among those actors 3

4. Applying SNA in Education Field Measure student interaction, Identify patterns of interaction in student groups, Provide instructive guidance accordingly, Evaluate the impact of instruction that aims to develop students’ interpersonal skills for working collaboratively in groups. 4

5. Centrality Measures Degree centrality refers to the total number of ties that each organization has with others. Closeness centrality is the communication distance between each network actor.  It is useful for analyzing the flow of communication, based on the premise that the shorter the actors’ paths are to others, the quicker their communication will be. Betweenness centrality identifies key organizations that facilitate relationships between other actors.  An actor has betweenness power when providing communication linkage between two other actors or subgroups in such a way that the nonexistence of that actor could cause a serious communication breakdown for those two actors or subgroups that it is facilitating. 5

6. Goal of Study To observe and test increased student interaction over the term of a class—and thus analyze which course methods are better for that goal. 6

7. Sample Data Nearly 200 active students  Twenty-nine students took this elective course in the spring 2008 term  The surveys, which were distributed in class, aimed to discover friendship and work relationships among students. They asked each student to list their friends in the classroom and the people they worked and studied with. The listed findings are a result of conducting network analysis on the survey data that measured changes in student interactions and social capital. 7

8. Collaborative Assignment for the Class 8

9. Class Friendship Network Beginning of the Term Student 14, Student 17, and Student 27 form a friendship clique that is connected to the network via Student 27. End of the Term Look like a strongly centralized network—meaning that everyone has connections with everyone else. 9

10. Centrality Measures for Class Friendship Network 10

11. Statistics for Each Measure of the Class Friendship Network Students in the class expanded his or her friendship network, which also shows how the course contributed to expanded student interaction and friendship ties. 11

12. Class Work Network Beginning of the Term End of the Term 12

13. Statistics for Each Measure of the Class Work Network Students in the class expanded his or her work network 13

14. Number of Cliques Found In the friendship network, both the numbers and the sizes of cliques grew The work-advice network, although the total number of cliques declined, the number of larger cliques increased. 14

15. Collaborative Learning Makes a Difference in Student Interactions 15

16. References Naim Kapucu, Farhod Yuldashev, Fatih Demiroz and Tolga Arslan, Social Network Analysis (SNA) Applications in Evaluating MPA Classes, JPAE, 16(4), 541–563 16