Lak13 koulocheri considering formal assessment in learning analytics within a ple the hou2 learn case

CONSIDERING FORMAL ASSESSMENT

IN LEARNING ANALYTICS WITHIN A

PLE: THE HOU2LEARN CASE

Eleni Koulocheri, Ph.D. Candidate

Michalis Xenos, Associate Professor

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Hellenic Open University

School of Sciences and Technology

Leuven, Belgium, 12/04/2013

LAK13

SITUATING THE WORK:

The application of Learning Analytics in an

environment of Hellenic Open University

Looking for methods to “exploit” students with

high impact.

The environment: HOU2LEARN

The techniques: Learning analytics with Social

Network Analysis (SNA) and activity metrics

The “formal” parameter: Formal grades

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LEARNING ANALYTICS

Siemens & Long five axes:

1. Course level: social network analysis (SNA),

discourse analysis, learning trails.

2. Educational data mining: pattern recognition

and predictive modeling.

3. Intelligent curriculum: development of

semantically defined curriculum resources

4. Adaptive content: provision of adaptive content

using recommendation procedures, based on

learner behaviour

5. Adaptive learning: social interaction and

learner support as an adaptive learner process.

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SNA IN A NUTSHELL

A methodological analysis of social networks.

Social network analysis views social relationships in terms of network theory

nodes (representing individual actors within the network)

edges (which represent relationships between the individuals, such as friendship, organizational position, etc.)

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SNA metrics

indegree/outdegree/betweeness centrality

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Users:

M.Sc. Students of HOU (N=76) Main supportive environment for PLH42 Course, ’11-’12

Open to all who are interested in

Objectives:

Openess promotion (content creation and sharing in a stressless way)

Communication enhancement in a less formal way (sharing common interests)

Socialization promotion among members endorsing ideas and experiences exchange

Formalities:

6 assignments

1 final exams

HOU2LEARN:

OPEN EDUCATIONAL PLATFORM

http://hou2learn.eap.gr/

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HOU2LEARN:


6

Set by Hellenic Open

University,

since September 2010.

For research and

educational purposes.

Supports:

•Informal Environment.

•Social Connections

•Material creation &

sharing.

Based on Elgg Platform.


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Social

networking

Learning 2.0

Informal

Learning

HOU2LEARN:


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Networking

Networking

Learning 2.0

Learning 2.0

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It covers all

three aspects of

a social

network:

Personal

Profile

Networking

Content

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HOU2LEARN:

PROFILING FEATURES


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It covers all

three aspects of

a social

network:

Personal

Profile

Networking

Content

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HOU2LEARN:

NETWORKING FEATURES


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It covers all

three aspects of

a social

network:

Personal

Profile

Networking

Content

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HOU2LEARN: CONTENT

SHARING/CREATING


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OUR RESEARCH…

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Α. Social Network Analysis Software

B. HOU2LEARN database schema (PLH42).

http://hou2learn.eap.gr

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Ele

ni K

ou

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eri - H

OU

METRICS DESIGN

topics that each user has uploaded on Group Discussion.

comments on topics of Group Discussion

new blogposts in Group Blog

comments on blogposts in Group Blog

comments on wireposts of other Group Members

uploads of new files on Group Files page

comments on files uploaded by other Group members

new bookmarks in Group

comments on bookmarks uploaded by other Group members

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C. Metrics Design according the course needs and set up

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SOCIAL NETWORK ANALYSIS

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D. Social Network Analysis Diagram – Shot 1

15/12/2011 - 22 Connections

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18/03/2012 - 73 Connections

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16/05/2012 - 404 Connections

Groups

Revealing Algorithms

(i.e. Wakita – Tsurumi, etc.)

Node Arrangement Algorithms

(i.e. Fruchterman Reingold, Force Atlas, etc.)

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GRADES VS SNA BASIC METRICS (1/4)

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Nodes’ size

proportional to the

(normalised) final

grades.

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Nodes’ size

proportional to the

Indegree centrality

i.e. the number of the total

number of connections

linked to a node.

It presents the number of

co-members a member is

followed by.

This metric, in fact,

measures the popularity of

the node.


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Nodes’ size

proportional to the

Outdegree centrality

i.e the number of edges

(arrows) that point

outward to other nodes.

It depicts the number of

members, a member

follows.

It counts the tension of the

node to be connected to

other nodes and to be

aware of their activities.


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Nodes’ size

proportional to the

Betweeness centrality

i.e. measures the brokering capability of a node.

It measures how much

removing a person

would disrupt the

connections between

other nodes in the

network.


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PRELIMINARY RESULTS

The highest indegree centrality node had

lower grade than the average one.

The highest outdegree centrality node had one

of the highest grades and also had the highest

betweeness centrality.

Students with high

grades have to increase

betweeness centrality.

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FUTURE WORK

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HOU2LEARN Running for 2nd year

Further deployment of Learning Analytics

Further deployment of Metrics

Combination of them

Integration of experiments (assignments with

groups)

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Ευχαριστώ!

[email protected]

@bekoul

Hellenic Open University 22

mailto:[email protected]