Social Learning techniques

1. Social Learning
Presented to : Dr. Abeer El Korany
Presented by : Yomna Hassan

2. Content
● What is Social Learning
● Over the years
● Why Social Learning
● Research Trends
● Potential Future Research

3. What is Social Learning?
● Not a New Trend
● Story Telling and Sharing Experiences
● Internet -> no time/ place barriers
● Social Networks

4. Social Learning [1]

5. Over the years [1]

6. Why social learning?
● Motivation to Learn
● Encouraging distant learning -> greater content
availability
● Social Presence of student as real people -> enhance
learning [2]

8. Motivation- Analytical Hierarchical Process
on EFL case study [3] (2013)

9. Current Research Trends
Social
Learning
Computer Psychology
Sciences

10. Current Research Trends
● Gamification
● Identification of hierarchy of relationships
(Ontology)
● SNA

11. Gamification

12. How to Identify Award System[1] (2012)
Promoting Certain Behaviour of Learning Through Gaming
Game Elements Game Mechanics
Points Rewards
Levels Status
Trophies Badges
achievements achievements
virtual goods self expression
leaderboards competition
virtual gifts Alturism

13. Gamification for Learning in Healthcare [4]
(2013)
- Patient recognize the behaviors that might compromise her/his
health
- Train non-specialist medical and paramedical staffs on the
procedures for diagnosis and patients follow-up
- UBICARE project-> de-hospitalization of patients suffering from
peritoneal dialysis and chronic heart failure.
- The Edugames are simulations that, using the learning-by-doing
approach allow specific skills related to both treatment protocols
and the possible actions to take in emergency situations to be
acquired.
- User Profile is matched to existing cases by DSS

14. Social -Learning - Software Engineering[5]
(2014)
- Continuous social screencasting is a promising technique for sharing and
learning about new software development tools.
- Steps:
- Individuals perform a software engineering task.
- Information about that task is recorded, even if that record is only a
memory.
- Another person later performs or plans to perform a new software
engineering task.
- Elements of the new task are compared against the record of prior
tasks.
- Relevant elements of the prior tasks are extracted and presented to
the person performing the new task in the form of a recommendation,
improving the accomplishment of that task or future tasks.

16. Ontology for E-Learning

17. Ontology for E-Learning [6] (2013)
- Social network analysis (SNA) is treating the students'
interaction merely as node and edge with less meaning.
- Ontology structure of e-learning Moodle that can enrich
the relationships among students, as well as between
the students and the teacher.

19. Classification of Learning Material [7] (2014)

20. SNA and Learning

21. SNA and Learning [8] (2014)
1. Performance Analysis
2. Drop-off rate
3. Recommendation - Courses, study groups
4. Personalization - Individualization

22. Social Networks Adapting Pedagogical
Practice[9] (2009)
- Network Visualization Tool
- Allows academic staff to identify patterns of
student behaviour and facilitate appropriate
interventions as required

24. Recommendation of Material[10] (2011)
- Clustering algorithm (neighborhood estimation ,
prediction of users interest)
- The recommendation is based on the tags defined by
the network learners and the items to be recommended
include not only contents but also social connections
that could enrich the user’s learning process.
- suggest new learning activities, users, and discussion
groups according to the user’s learning and knowledge
needs.

26. Influence of Relationships[11] (2011)
-Positive/ Negative Influence (not only PIDS)
-Selection algorithm named Weight Positive Influence Dominating Set (WPIDS)

27. Recommendation of Material[7] (2014)
● Based on Profile, context
and Level of Interaction and
collaboration
● According to students needs
and feedback
● Data is seen as important:
relevance,
presentation, context, accessibility

28. Content Recommendation
Service by Exploiting Mobile Social Interactions [12] (2014)
Individual learning content is able to be recommended according to the
behavioral characteristics of the response message of individual learners in the
community

31. Potential work
- Drop-off reasons and rates analysis
- IoT Integration (for personalization)
- Storage Overhead (Characterising important
factors in mobile environments)
- Standardization

32. References
[1]Social Learning: the organization learns how to learn, Social Business Manifesto, May 2012.
[2]N. Adman et. al, Online Social Learning Model , International Conference on Teaching and Learning in Computing and Engineering,2014..
[3]Hsu, Liwei. "Leveraging Interactivities on Social Networking Sites for EFL Learning." International Journal of English Language Education 1.3
(2013): pp-244.
[4]Simões, Jorge, Rebeca Díaz Redondo, and Ana Fernández Vilas. "A social gamification framework for a K-6 learning platform." Computers
in Human Behavior 29.2 (2013): 345-353.
[5]Murphy-Hill, Emerson. "The Future of Social Learning in Software Engineering." (2014): 1-1.
[6] N.Yusof et al., Ontology Development of e-Learning Moodle for Social Learning Network Analysis , World Academy of Science, Engineering
and Technology Vol:7 2013-06-21
[7]Di Bitonto, P., et al. "Distance Education and Social Learning in e-Health." International Journal of Information and Education Technology 4.1
(2014): 71-75.
[8]Brinton, Christopher G., and Mung Chiang. "Social learning networks: A brief survey." Information Sciences and Systems (CISS), 2014 48th
Annual Conference on. IEEE, 2014.
[9]Bakharia, Aneesha, Elizabeth Heathcote, and Shane Dawson. "Social networks adapting pedagogical practice: SNAPP." Same Places,
Different Spaces. ascilite 2009 (2009).
[10]Di Bitonto, Pierpaolo, Teresa Roselli, and Veronica Rossano. "Recommendation in e-learning social networks." Advances in Web-Based
Learning-ICWL 2011. Springer Berlin Heidelberg, 2011. 327-332.
[11]Wang, Guangyuan, et al. "Positive influence dominating set in e-learning social networks." Advances in Web-Based Learning-ICWL 2011.
Springer Berlin Heidelberg, 2011. 82-91.
[12]Chao, H., et al. "A M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions." (2014): 1-1.