Acis nlc2012

TeLLNet

Lehrstuhl Informatik 5(Information Systems)

Prof. Dr. M. JarkeI5-PCPK-0412-1 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

Learning Analytics in a Teachers’ Social Network

Manh Cuong Pham, Yiwei Cao, Zinayida Petrushyna, and Ralf Klamma

RWTH Aachen UniversityAdvanced Community Information Systems (ACIS)

[email protected]

http://creativecommons.org/licenses/by-sa/3.0/





http://dbis.rwth-aachen.de/cms/research/MiCIS


http://www.tellnet.eun.org/web/tellnet

TeLLNet


Prof. Dr. M. JarkeI5-PCPK-0412-2

Responsive Open

Community Information

Systems

Community Visualization

and Simulation

Community Analytics

Community Support

Web A

nalytics

Web

Eng

inee

ring

Advanced Community Information Systems (ACIS)

Requirements Engineering




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Agenda

Introduction to social capital Social network analysis for social capital Case study: social capital in eTwinning network Conclusions and future work




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Introduction Human capital vs. social capital [Burt, 1992]

– Human capital: the personal ability to perform tasks (e.g. talent, education, etc.)– Social capital: the social environment surrounding individuals

Social capital as a property of– Individuals: positions in social network that are more efficient in performing tasks

(i.e. local structure)– Groups: structure of members’ network that makes the group functions more

efficient (i.e. structure of a sub-network) In our research, we study social capital in teachers’ network

– By SNA metrics and a development model– The performance of teachers and projects: recognized by Quality Labels– Network structure of projects and position of teachers: identified via networks

created by several communication mechanisms (e.g. message, project collaboration, blog)




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Social Capital:Structural Hole vs. Closure

Structural holes [Burt, 1992]

- Nodes are positioned at the interface between groups (gatekeepers, e.g. node B)

- Informational advantages: access to information from different parts of networks

- Form novel ideas by combining information from different groups

- Control the communication between groups

Closure - Nodes are embedded in tightly-knit groups (e.g. node A)- More trust and security within coherent communities

Social capital [Coleman, 1990]

- Individuals and groups deriving benefits from social relationships- Network structural property: either structural hole or closure




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Identification of Individual Social Capital

Given the network G=(V,E), where V is the set of nodes and E is the set of edges

Structural holes: nodes with high betweenness

where: : number of shortest paths between nodes i and j that pass through node u

: total number of shortest paths between nodes i and j Closures: nodes with high local clustering coefficient

where: is the set of neighbors of node u

jiu

u

ji

jiuB

),(

),()(

),( jiu

),( ji

2/1)(N(u)

E w)(v, :N(u) wv,C(u)

uN

)(uN




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Identification of Group Social Capital

In which stage is the members’ network of a given group? How does it relate to the performance of the group?

A community development model [Pham et al., 2011]




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Qualify the Stage of Group Member Network

Density: fraction of actual edges in the network

, n is the number of nodes

Global clustering coefficient

Maximum betweenness: highest betweenness of nodes Largest connected component: fraction of nodes in largest connected

component For large member networks

- Diameter: the longest shortest path between any pair of nodes- Average shortest path length

triplesconnected ofnumber

trianglesofnumber 3D

n2

E w)(v, :V wv,D




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Case Study:eTwinning Community

Data #data entries DescriptionProject 23641 Schools from at least two schools from at least two different European countries create a

project and use ICT to carry out their work.

Contact 769578 Teachers are able to explore other teachers' profiles and add them into their own contact list. It is suggested to use forum and other media to contact the other teachers before taking them as a contact.

Project diary 20963 Blog for project reports Project diary post 49604 Each blog entry in project diaryProject diary comment

7184 Comments added to blog entries in project diary

My journal message

38496 Message posted on teachers' wall which is part of teachers' profile

Teacher 146105 Registered teachers working in European schools and, namely "eTwinner"Quality label 8042 Awarded first to projects. Then the project-involved schools and teachers are awarded

accordingly. They are assigned by each country or on the European level: National Quality Label and European Quality Label

Prize 1384 eTwinning Prizes are awarded to schools. They are of European level and are called European eTwinning Prizes

Institution 91077 Various European schools: pre-school, primary, secondary and upper schools

Statistics on eTwinning data (as of 11.11.2011)




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eTwinning NetworkNetwork #nodes #edges DescriptionProject 37907

(26%)804856(0.11%)

Nodes are teachers (eTwinners) and there is a connection (edge) between two teachers if they collaborated in at least one project. Edges in the network are undirected and weighted by the number of projects in which the two teachers collaborate.

Contact 109321(75%)

573602(0.01%)

Nodes are teachers and there is an edge between two teachers if at least one teacher is in the contact list of the other. Edges are undirected and unweighted.

Project diary 3264(2.2%)

3436(0.06%)

Nodes are teachers and there is an edge between two teachers if one teacher has commented on at least one blog post created by the other. Edges are directed and weighted by the number of comments.

My journal 23919(16%)

30048(0.01%)

Nodes are teachers and there is an edge between two teachers if one teacher has posted or commented on the wall of the other. Edges are directed and weighted by the number of messages.

Teacher networks statistics (as of 11.11.2011) Data is processed, transformed and loaded into Oracle data warehouse Networks are aged for time series analysis Network parameters are computed using Oracle store procedures Projects are considered as groups to study group social capital




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Properties of Teacher Networks:The Power Law Degree Distribution

0 2 4 6 8 10

8

6

4

2

0

2

4

Cum

ulat

ive

Freq

uenc

y

Degree

Project network degree distribution

Raw data

y=28.209x 1.455

0 2 4 6 8 10 10

8

6

4

2

0

2

4

6

Cum

ulat

ive

Freq

uenc

y

Degree

Contact network degree distribution

Raw data

y=327.630x 1.933

0 1 2 3 4 5 6 8

6

4

2

0

2

4

6

Cum

ulat

ive

Freq

uenc

y

Degree

Project diary network degree distribution

Raw data

y=14.904x 1.625

0 1 2 3 4 5 6 7 8

6

4

2

0

2

4

6

Cum

ulat

ive

Freq

uenc

y

Degree

My journal network degree distribution

Raw data

y=38.875x 1.750

Degree distribution of eTwinning networks follow the power law with the formula axy




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Social Capital of Teachers

0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Freq

uenc

y

Number of quality labels

(a) Quality labels and number of projects/posts/contacts/wall posts

Project diaryContactProjectMy journal

0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Degr

ee


(b) Quality labels and degree


0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Betw

eenn

ess


(c) Quality labels and betweenness


0 10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Clus

terin

g


(d) Quality labels and clustering


Structural hole as a form of social capital in eTwinning networks




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Projects Achievement and Non-structural Properties

0 5 10 15 20 25 30 35 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Fr

actio

n of

rece

ived

qua

lity

labe

l pro

ject

s

Number of countries involved, languages used and subjects

Quality label of projects and their properties

0 10 20 30 40 50 60 70 800

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Frac

tion

of re

ceiv

ed q

ualit

y la

bel p

roje

cts

Number of teachers and institution involved

Quality label of projects and their properties

CountryLanguageSubject

TeacherInstitution

Number of countries and languages used somehow correlate to the quality Number of teachers and institutions: effect on small projects (less than 30 members) Subject has no effect




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Projects Achievement and Structural Properties

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Frac

tion

of re

ceiv

ed q

ualit

y la

bel p

roje

cts

Network parameters

Quality label of projects and their members network parameters

DensityClustering coefficientMaximum betweennessLargest component

Project member networks: created using the previous project collaboration and wall messaging, reflect the early communication of project members

High quality projects prefer the Bonding stage: consists of seperated densely connected groups

Form of social capital: structural hole




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Conclusions and Future Work Social capital in eTwinning Network

– Both teachers and projects follow structural hole– The informational diversity is the key success factor

Applications: recommendation tools– Help teachers find projects, contacts, etc.– Help project organizers find, select and invite project partners

Future works– Tracking the development pattern of teacher networks– Tracking the development pattern of teachers for competence management– Developing tools

– Recommendation tools– Dynamic visualization of local and global teacher networks as well as network parameters




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