This study analyzed social network data from students in a master's program over 10 years to investigate the relationship between social ties and academic performance. Descriptive statistics showed no missing data from the institutional records. Regression analysis found that higher closeness and eccentricity centrality were positively associated with GPA, supporting the hypothesis that greater social capital is linked to better performance. Further, students in the top two quartiles for social capital had significantly higher GPAs than those in the bottom two quartiles, indicating cross-class social ties benefit academic achievement.

1. LEARNING ANALYTICS IN EDUCATION
SOCIAL NETWORK ANALYSIS
YILDIZ UZUN

2. A SOCIAL NETWORK ANALYSIS ON ELEMENTARY STUDENT
ENGAGEMENT
IN THE NETWORKED CREATION COMMUNITY
CHEN-CHUNG LIU , YU-CHI CHEN , SHU-JU DIANA TAI (COMPUTERS AND EDUCATION, 2017)

3. CONTEXT OF THE STUDY
 The goals of the study is to understand the impact of the social networking activities on elementary
students' engagement in the networked creation community.
 Compares students' engagement in two settings;
 Students freely team up with peers (dynamic teaming)
 Students work with a fixed peer (fixed relation)

4. RESEARCH QUESTIONS
 Do the dynamic teaming activities impact the students' flow and motivation in the networked creation
activity?
 How do the students team and collaborate with peers to participate in the networked creation activity?
 Do the students' properties in the social network have an impact on their flow and motivation in the
networked creation activity

5. RESEARCH METHOD- PARTICIPANTS
 This study is part of a year-long program aiming at helping students develop their proficiency in English,
computer use, and collaboration through collaborative digital storytelling.
 26- third grade students in an elementary school in northern Taiwan, aged between 8 and 9

6. RESEARCH METHOD- INSTRUMENTS
 There are 3 instruments:
 Storytelling and social networking
 to create multimedia stories on the Internet, Story &
Painting House, social networking application was
developed to facilitate the process of the networked
creation activity
 students could invite peers to create a shared
multimedia story
 Once an invitation was sent, it would be instantly logged
by the system and a link from inviter to invitee would be
presented in the SNA tool for further analysis.

7. RESEARCH METHOD- INSTRUMENTS
 The flow perception survey
 this survey investigates student engagement in four basic flow components including the level of control,
attention, curiosity and intrinsic interest
 consists of only four 5-point Likert items and
 administrated right at the end of each activity section
 Motivated Strategies for Learning Questionnaire (MSLQ)
 While the flow survey was conducted to obtain students' short term engagement right after each activity section,
the Motivated Strategies for Learning Questionnaire (MSLQ) was conducted to uncover the long-term impact of
the networked creation activity on participants' motivation associated with the fixed relation and dynamic teaming
activities.
 The questionnaire includes 25 items asking students’ motivation in the dimensions of self-efficacy,
extrinsic/intrinsic goal orientation, task value and peer learning

8. RESEARCH METHOD- PROCEDURE
 The networked creation activity was implemented during a semester, for an 80-min session per week.
 Students
 First 5 weeks => fixed teams, from 6 to 13 week => dynamic teams (total 13 section)
 After each section, they answered flow perception survey. Motivated Strategies for Learning
Questionnaire (MSLQ) was administrated in week 5 and 13 to assess motivation
Read the story
Create picture
book
collaboratively
Publish the books

9. RESEARCH METHOD- DATA ANALYSIS
 Flow perception and motivation questionnaires were analyzed with dependent t-test. (RQ1)
 Participants’ invitation activities on the social network were analyzed with the UCINET SNA tool. (RQ2)
 Their perceptual engagement was also compared based on their degree centrality with t-test (RQ3)

10. RESULTS
 Perceptual engagement in fixed relation and
dynamic alignment activities
 participants perceived relatively lower level of
flow in the first 5 weeks when the fixed relation
activity was implemented and the flow
perception was at a higher level during week 6
to 13 when the dynamic teaming activity was
administered.

11. RESULTS
 Perceptual engagement in fixed relation and
dynamic alignment activities
 in the dynamic teaming activity, participants’ average
flow perception was 4.52, which is significantly higher
than that in fixed relation activity (table 1)
 Participants perceived a significantly higher level of
motivation in the dynamic teaming activity than they
did in the fixed relation activity in two dimensions:
intrinsic motivation and task value

12. RESULTS
 Social Network Analysis
 elementary students tended to team up with
students of the same gender when participating
in the networked creation activity.
 Figure shows in-degree centrality (who recieved
more invitation), there are 12 student in the circle
6 boys and 6 girls. High popularity not depends
on gender on elementary students.
 Also, their content knowledge varies among 12.
content knowledge not effect popularity. They
were not invited because of their knowledge.
Girls BoysDensity=0.0
7

13. RESULTS
 Social Network Analysis
 Figure shows out-degree centrality.
There are 9 participant in the circle 5 boy,
4 girl.
 Their english profeciency levels are mid
and low.
 Results show that the lower-proficiency
students are more active in teaming up
and invitations. They seek for help.
Girls BoysDensity=0.07

14. RESULTS
 Impact of social network on perceptual
engagement
 The 12 students who showed higher in-
degree centrality (centrality 2)
perceived a significantly lower
perceptual engagement.
 The 9 students who showed higher out-
degree centrality (centrality 2)
perceived higher level of perceptual
engagement.

15. “CHOOSE YOUR CLASSMATES, YOUR GPA IS AT STAKE!”
THE ASSOCIATION OF CROSS-CLASS SOCIAL TIES AND
ACADEMIC PERFORMANCE
DRAGAN GASEVIC, AMAL ZOUAQ, AND ROBERT JANZEN (2013)

16. CONTEXT OF THE STUDY
 Aim of the study is to investigate the relationship between student academic performance and social
ties.
 The term (generally used in the article) Social capital can be defined as-» investment in social relations
with expected returns in the marketplace »
 Position in social networks is an important predictor of individual and group performance

17. RESEARCH QUESTIONS
 Hypothesis 1: Students’ social capital accumulated through their enrollment in courses while pursuing a
degree program is positively associated with their academic performance.
 Hypothesis 2: Students with more social capital in cross-class networks have a significantly higher
academic performance.

18. RESEARCH METHOD- SAMPLE
 10 years (between 2001 and 2011)of student course enrolment records (N=505) in a master’s degree
program offered through distance education at a Canadian university.
 The program is completely delivered through an online, distance education model with courses enrolling
up to 30-40 students per session. Course content and group learning is delivered by using the learning
management system Moodle.

19. RESEARCH METHOD- DATA COLLECTION
 For the extraction of data from institutional archival data,
 A link was created between all students who enrolled into same section of a course.
 This helped to create undirected social graphs.
 Weight of link between two students depends on the number of course they took together.

20. RESEARCH METHOD- VARIABLES AND MEASUREMENT
 The Gephi open source software used for social network analysis to compute below social networking
variables
 Degree centrality ( the number of social ties a node has in a social network)
 Closeness centrality (the distance of a node to all other nodes in a social network)
 Betweenness centrality (the number of shortest paths between any two nodes that go through a given node)
 Eccentricity (the distance between a node and its farthest node in a social network)

21. RESEARCH METHOD- ANALYSIS
 Distribution of variables was explored by using Kolmogorov-Smirnov and Shapiro-Walk tests.
 For Hypothesis 1;
 Linear regression was used (GPA was the outcome variable)
 The regression models were adjusted for confounding variables like days in program, course withdrawals, course
cedits.
 For Hypothesis II;
 Sample was split into groups based on students’ amount of social capital which was defined by centrality
measures.

22. RESULTS
 Table shows the descriptive statistics of each variable.
 Since data was extracted from institutional archival data, there is no missing value.

23. RESULTS
 Hypothesis 1
 Tested to measure if there is a positive association between the students’ academic performance and
students’ social capital
 The results show significant associations between closeness centrality and GPA, and eccentricity and
GPA.

24. RESULTS
 Hypothesis 2
 Participants were devided into groups according to amount of their social capital.
 Q1 is highest social capital, Q4 is lowest social capital.
 Results showed that Q1 and Q2 have significantly higher GPA than Q3 and Q4.
 Based on the results, higher levels of social capital have significantly higher GPA values than those with
lower levels of social capital in cross-class social networks.

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