Group Formation in eLearning-enabled Online Social Networks Steffen - - PowerPoint PPT Presentation

▶

Oct 21, 2022 229 likes •474 views

Group Formation in eLearning-enabled Online Social Networks Steffen Brauer, Thomas C. Schmidt steffen.brauer@haw-hamburg.de, t.schmidt@ieee.org iNET RG, Department of Computer Science Hamburg University of Applied Sciences September 26, 2012

SLIDE 1

Group Formation in eLearning-enabled Online Social Networks

Steffen Brauer, Thomas C. Schmidt

steffen.brauer@haw-hamburg.de, t.schmidt@ieee.org iNET RG, Department of Computer Science Hamburg University of Applied Sciences

September 26, 2012

SLIDE 2

Group Formation

Outline

1 Motivation 2 eLearning-enabled OSN 3 Group Formation Approach 4 Evaluation 5 Conclusion Steffen Brauer HAW Hamburg 2

SLIDE 3

Group Formation Motivation

Motivation

Classic eLearning environments Intra-group communication in predefined classrooms Managed by instructor

Creates groups Analyses course results Tracks learning progress

Online social networks (OSN) Socialize with friends Groups are user-triggered Ubiquitous use

How to provide a platform for self-paced learning on topics of personal interest?

jdflksajfdlkjsalkfjlksaä löksaölfdköskdaf ölsaökdsaf ölksölfdkösa

Steffen Brauer HAW Hamburg 3

SLIDE 4

Group Formation Motivation

Motivation

Objectives & Challenges

Our work focuses on integrating an OSN and an eLearning environment by removing the instructor Removal of instructor leads to challenges

1 How to stimulate a team building process that is effective for

learners?

2 How to provide access to the relevant content for a learning group? 3 How to facilitate a consistent learning progress, include feedback

and corrective actions?

Steffen Brauer HAW Hamburg 4

SLIDE 5

Group Formation Motivation

Motivation

Objectives & Challenges

Our work focuses on integrating an OSN and an eLearning environment by removing the instructor Removal of instructor leads to challenges

1 How to stimulate a team building process that is effective for

learners?

2 How to provide access to the relevant content for a learning group? 3 How to facilitate a consistent learning progress, include feedback

and corrective actions?

Steffen Brauer HAW Hamburg 4

SLIDE 6

Group Formation eLearning-enabled OSN

eLearning-enabled OSN

Base Structure

Extend commercial OSN by adding learning related features Communication is handled by commercial OSN via APIs All relevant objects are represented in the OSN Classical representation of an OSN user3 user1 user2

Steffen Brauer HAW Hamburg 5

SLIDE 7

Group Formation eLearning-enabled OSN

eLearning-enabled OSN

Base Structure

Extend commercial OSN by adding learning related features Communication is handled by commercial OSN via APIs All relevant objects are represented in the OSN Representation using the unified approach

member member friends studies related to edits edits

user1 user2 group1 topic1 user3 content1

Steffen Brauer HAW Hamburg 6

SLIDE 8

Group Formation eLearning-enabled OSN

eLearning-enabled OSN

User Model

Availability

Motivation of an user to start collaboration

Steffen Brauer HAW Hamburg 7

SLIDE 9

Group Formation eLearning-enabled OSN

eLearning-enabled OSN

User Model

Availability

Motivation of an user to start collaboration

Learning style (Felder & Silverman Theory)

Active or Reflective (Processing) Visual or Verbal (Input) Sensing or Intuitive (Perception) Sequential or Global (Understanding)

Steffen Brauer HAW Hamburg 7

SLIDE 10

Group Formation eLearning-enabled OSN

eLearning-enabled OSN

User Model

Availability

Motivation of an user to start collaboration

Learning style (Felder & Silverman Theory)

Active or Reflective (Processing) Visual or Verbal (Input) Sensing or Intuitive (Perception) Sequential or Global (Understanding)

Knowledge

Represented by tags Each topic defines required tags with weights Users also hold tags with an activity index Knowledge Rank is calculated by product of weights and activity index

Steffen Brauer HAW Hamburg 7

SLIDE 11

Group Formation Group Formation Approach

Group Formation

Overview

1 User initiate group building by selecting a topic, which requires

collaboration

Steffen Brauer HAW Hamburg 8

SLIDE 12

Group Formation Group Formation Approach

Group Formation

Overview

1 User initiate group building by selecting a topic, which requires

collaboration

2 Starting at the initiator, the social network is searched for

candidates

Steffen Brauer HAW Hamburg 8

SLIDE 13

Group Formation Group Formation Approach

Group Formation

Overview

1 User initiate group building by selecting a topic, which requires

collaboration

2 Starting at the initiator, the social network is searched for

candidates

3 If a number of candidates is found, the group formation tries to

find the best constellation

Steffen Brauer HAW Hamburg 8

SLIDE 14

Group Formation Group Formation Approach

Group Formation

Overview

1 User initiate group building by selecting a topic, which requires

collaboration

2 Starting at the initiator, the social network is searched for

candidates

3 If a number of candidates is found, the group formation tries to

find the best constellation

4 Selected users are invited and learning experience starts Steffen Brauer HAW Hamburg 8

SLIDE 15

Group Formation Group Formation Approach

Candidate Selection

Input: social network, number of candidates, threshold Vertex is added to candidate set, if distance to initiator and topic is lower than threshold Distance formula includes learning style and knowledge rank (scale: 0 - 1) Implemented search algorithms:

Breath First Search(BFS) Random Walk Search(RWS) Best Connected Search(BCS)

Output: candidate set

Steffen Brauer HAW Hamburg 9

SLIDE 16

Group Formation Group Formation Approach

Group Formation

Input: candidate set Group fitness defined by:

common learning style high knowledge rank low distance in social network

Implemented by genetic algorithms to reduce complexity

Group constellations are treated as chromosomes in a population In each generation cross-over and mutation

perations are performed

Only constellations with a high fitness are selected for next generation

Output: best group constellations

Steffen Brauer HAW Hamburg 10

SLIDE 17

Group Formation Evaluation

Evaluation

Open questions

1 How are the user attributes distributed? 2 What is the impact of search algorithms? 3 Does the threshold influence the search complexity? 4 Does the candidate count influence the group fitness? Steffen Brauer HAW Hamburg 11

SLIDE 18

Group Formation Evaluation

Evaluation

Generating test data

No implementation exists and no appropriate test data Evaluation on synthetic data Simplification: Only user objects in the social network and all users are available Forest fire model was used to generate a social network with 1000 vertices and 31522 edges Challenge: How to distribute the user attributes?

Learning style: empirical data from Felder & Spurlin Knowledge: 20 tags are power-law distributed over all vertices with random activity index

Steffen Brauer HAW Hamburg 12

SLIDE 19

Group Formation Evaluation

Evaluation

User Model How are the user attributes distributed?

Distance in learning style

0.00 0.10 0.20

Distance Frequency

0.25 0.5 0.75 1

Normal distribution Low average distance Knowledge rank

0.00 0.02 0.04 0.06

Knowledge Rank Frequency

0.13 0.3 0.45 0.6 0.75 0.9

0 = 0.27 Very low average knowledge rank

Steffen Brauer HAW Hamburg 13

SLIDE 20

Group Formation Evaluation

Evaluation

Candidate Selection What is the impact of the search algorithms?

BFS RWS BCS

Group Knowledge Rank

0.0 0.2 0.4 0.6 0.8 BFS RWS BCS

Group Distance Learning Style

0.00 0.15 0.30

No significant differences in distance

f learning style and

knowledge rank

BFS RWS BCS Group Density 0.00 0.10 0.20 0.30

BFS and BCS produce nearly equal results RWS produce low group density

Steffen Brauer HAW Hamburg 14

SLIDE 21

Group Formation Evaluation

Evaluation

Candidate Selection Does the threshold influence the search complexity?

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 20 40 60 80

Threshold Visited Vertices Breath First Random Walk Best Connected

RWS performs best if threshold < 0.7 BFS and BCS convert at 0.9

Steffen Brauer HAW Hamburg 15

SLIDE 22

Group Formation Evaluation

Evaluation

Group Formation Does the candidate count influence the group fitness?

10 15 20 25 30 35 40 0.0 1.0 2.0 3.0

Candidate count Group fitness

BFS was used to find candidates Threshold = 0.8 No significant change in group fitness by increasing candidate count

Steffen Brauer HAW Hamburg 16

SLIDE 23

Group Formation Conclusion

Conclusion & Outlook

Problem: How to simulate a team building process that is effective for learners? User model includes availability, learning style and knowledge Approach divided in two parts:

Candidate selection Group formation

Evaluation based on synthetic data Future research Improve data base by empirical data Include tie strength to take full advantage of unified approach

Steffen Brauer HAW Hamburg 17