1. !
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CASE-BASED WORKFLOW MODELING IN
SUPPORT OF AUTOMATION THE
TEACHERS’ PERSONAL AND SOCIAL
BEHAVIOR
Malinka Ivanova
Technical University of Sofia
Mirjam Minor
Johann Wolfgang Goethe-University Frankfurt am
Main

2. Aim
• To explore the possibilities for activities’
automation of teachers when they use their
PLN and this could facilitate their learning

3. Introduction
• Do you use social networking sites?
• What kind of activities do you perform? - search,
interact, share, like/dislike, join groups, etc.
• How much time do you spend?
• Is it possible to shorten the distance among learning,
effectiveness and time?
• What about optimization of teacher’ s behavior
through different techniques for automation?

4. Introduction
• Exist strong research about use of social
networking sites for educational purposes
– Activities in PLN (Ivanova et al., 2012)
– Twitter in EFL education (Mork, 2009)
– Graasp for collaborative learning (Li et al., 2012)
– social media for engineering communication (Mehlenbacher et
al., 2010)
typical activities performed by teachers and learners are
drawn
 these activities are not structured in any criterion
• One example for activities grouping in time (weekly)
when Facebook is utilized as LMS (Wang et al., 2012)

5. Related work
• effectiveness of teachers' activities in social
networks  automation functions  related
to the people and content searching, filtering
and recommending

6. Automation Techniques
• A method for selection of social media content (Twitter) (De
Choudhury et al., 2011)
• A system that tracks conversations on social platforms (Facebook
and Twitter) and prioritizes posts and messages that are related to
a given topic (enterprises) (Ajmera et al., 2013)
• An algorithm that filter content that is most rated and liked -
influential users and passive users on Twitter (Romero et al., 2011)
• Personalized item recommendation widget (Guy et al., 2010) -
recommendations are done after collecting the relationships among
people, tags and items
• Recommender system based on user-model (Seth and Zhang,
2008)
• A framework with a possibility to summarize Twitter stream
messages, retirement of messages and their reconstruction around
a given topic (Yang et al., 2012)

7. Types of Users in Social Networks
• (Brandtzæg and Heim, 2011) - five groups: sporadic,
lurkers, socialisers, debaters and actives according to
their performed activities and level of participation
• (Guo et al., 2009) - users’ behavior is related to daily
and weekly contributions through posting - the
authors propose models describing how users create
links and how their networks progress in time
• (Lang and Wu, 2011) research the factors that are
important for lifetime forming - active and passive
lifetime according to users’ activities and behavior

8. • For purposes of our research:
– users of social networking sites – passive (learn by
observation ) and active (participate)
– with different level of activeness in different time
of their learning sessions according to their
learning priorities and goals
– users possess favorite activities
learning optimization recommendations
with structured activities

9. Criterion and procedure for generation of structured workflows
Understanding
the favorite
activities in
SNS
User model
preferences
passive user
active user
time,
learning priority
recommendations
to satisfy passive
to motivate
passive to be
active
to satisfy active

10. • Two different sets with activities typical for passive and
active users are created
– passive user - a person who prefers to learn alone without
getting advantages of participation and communication
– passive users learn through observation: read the shared
knowledge, accept or not friendships, follow people,
monitor activity, track activity stream, use applications
with special purposes, search
– active users – perform activities that contributes to
enrichment of the network knowledge: add comments,
publish content/opinion, share link/file, like/dislike,
join/create groups, use chat, communicate via direct
messaging or other applications, extend contacts, make
friendships

11. Serendipity, Accidental and
Intentional Learning
• Usually, learning in SNS occurs accidentally and in a
serendipitous way – it is unique for every PLN
• Every teacher sees different stream of messages and
receive different information
This fact influences on learning curiosity and changing
learning needs
• Kop (2012) - recommenders, RSS aggregators and
microblog platforms are effective means of facilitating
serendipitous learning on open online networks
• Teachers have control on their PLN organization, but also
they are in touch with unexpected information sources
• At this moment serendipity is not automated, just
serendipitous content and contacts could be
recommended

12. • PLNs are created intentionally according to the teachers’ interests and
future plans  they strive to be connected to people who are sources of
topic related content
• intentional disposition of PLNs and serendipitous events and processes
 teachers respond to serendipitous events in intentionally topic-driven PLN
serendipitous
event
Is it my topic?
passive user
active user
case-based
workflows
process it
ignore it
yes
no
Serendipitous events in intentionally topic-driven PLN

13. RESEARCH METHODS
• the design-oriented paradigm of business informatics (Hevner et al.,
2004)
– It aims at conducting a feasibility study on whether workflow technology is
applicable in order to partly automate the work of teachers in PLN’s
• Following a build-and-evaluate cycle (Hevner et al. 2004), a workflow
model for learning procedures within PLNs is created (during the build
phase)
• its technical feasibility is tested by deriving a couple of workflow instances
from the activities observed in recent PLNs (during the evaluate phase)
• The results of this technical feasibility study are a prerequisite for our
future work
• The main research questions are:
– Representation: How can activities of teachers in social networks be
represented and structured in a workflow model?
– Applicability: Can the workflow model be populated by cases (workflow
instances) for different learning scenarios and user types?

14. Modelling Workflows
• workflows - “the automation of a business process, in
whole or part, during which documents, information or
tasks are passed from one participant to another for
action, according to a set of procedural rules” (WFMC,
1999)
• Recently, a broader notion is emerging, where a
workflow describes any flow of activities
• This notion includes the activities of a learner during the
use of a PLN
• Workflows are stored in form of cases
• Any case could be reused and modified
• A new case could be created from existing cases

15. Workflow 1: Getting to know a new
subtopic from the topic
• Workflow 1 for a passive user

16. Workflow 1: Getting to know a new
subtopic from the topic
• Workflow 1 for a passive user with an intention to be
activated

17. Workflow 1: Getting to know a new
subtopic from the topic
• Workflow 1 for an active user

18. Workflow 2: Getting feedback for slides
• Workflow 2 for an active user

19. Workflow 3: Discover an expert for a topic
• Workflow 3 for a passive user

20. Workflow 3: Discover an expert for a topic
• Workflow 3 for a passive user with an intention to be
activated

21. Workflow 3: Discover an expert for a topic
• Workflow 3 for an active user

22. Conclusions
• modeling of structured activities in time and space and according to the learning
priority and learning needs utilizing case-based workflow technology
•The workflows are originated from serendipitous events and they are categorized
according to the user type
•These workflows describe important cases of activities performed during the PLNs
organization and utilization
•They will support teachers through recommendations and guidance giving, making
their learning more effective
• They figure the main functions for activities’ automation and semi-automation
facilitating the teachers’ personal and social behavior
• The created workflows are the first step in the process of software development

23. Thank you for your attention!