Technical Challenges for Realizing Learning Analytics Learntec 2015, January 28, 2015, Karlsruhe, Germany, Ralf Klamma Advanced Community Informations Systems (ACIS) Group RWTH Aachen University

1. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
1
Learning
Layers
This slide deck is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
Technical Challenges for Realizing
Learning Analytics
Ralf Klamma
Advanced Community Information Systems (ACIS)
RWTH Aachen University, Germany
klamma@dbis.rwth-aachen.de
LEARNTEC, Karlsruhe, Germany, January 27th, 2015

2. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
2
Learning
Layers
RWTH Aachen University
• 512 professors, 4675 academic and 2443
non-academic colleagues
• Annual budget around 884 million Euros,
445 million Euros funded by third parties
• 1,250 spin-off businesses have created
around 30,000 jobs in the greater Aachen
region over the past 20 years
• 260 institutes in 9 faculties as Europe’s
leading institutions for science and research
• Currently around 40,375 students are enrolled
in over 130 academic programs
• Over 6,300 of them are international students
hailing from 120 different countries

3. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
3
Learning
Layers
Responsive
Open
Community
Information
Systems
Community
Visualization
and
Simulation
Community
Analytics
Community
Support
WebAnalytics
WebEngineering
Advanced Community Information
Systems (ACIS) Group @ RWTH Aachen
Requirements
Engineering

4. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
4
Learning
Layers
Agenda
LearningAnalytics
CommunityLearningAnalytics
ExpertsinCommunityInformation
Systems
OverlappingCommunityIdentification
Conclusions&Outlook

5. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
5
Learning
Layers
LEARNING ANALYTICS

6. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
6
Learning
Layers
Self- and Community Regulated
Learning Processes
Based on [Fruhmann, Nussbaumer & Albert, 2010]
Learner profile
information is
defined or
revised
Learner finds
and selects
learning
resources
Learner works
on selected
learning
resources
Learner reflects
and reacts on
strategies,
achievements
and usefulness
plan
learnreflect
The Horizon Report – 2011 Edition

7. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
7
Learning
Layers
The long tail of personal knowledge
in life-long learning
■ Zillions of new learning opportunities
■ Abundance of learning materials
■ But: Extremely challenging to find & navigate
High-quality, specially designed,
learning materials like books or
course material
Gaps in personal knowledge
identified mostly by real-world
practice

8. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
8
Learning
Layers
Web 2.0 Competence Development
Cultural and Technological
Shift by Social Software
Impact on
Knowledge Work
Impact on
Professional
Communities
Web 1.0 Web 2.0 Microcontent
Providing
commentary
Personal knowledge
publishing
Establishing personal
networks
Testing Ideas
Social learning
Identifying competences
Emergent Collaboration
Trust & Social capital
personal
website and
content
management
blogging and
wikis
User generated
content
Participation
directories
(taxonomy)
and stickiness
Tagging
("folksonomy")
and syndication
Ranking
Sense-making
Remixing
Aggregation
Embedding
Emergent Metadata
Collective intelligence
Wisdom of the Crowd
Collaborative Filtering
Visualizing Knowledge
Networks

9. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
9
Learning
Layers
Personal Learning Environment (PLE)
PLE describes the tools, communities, and services that constitute the
individual educational platforms learners use to direct their own learning and
pursue educational goals
LMS – course-centric vs. PLE – learner-centric:
• Extension of individual research
• Students in charge of their learning process
• self-direction, responsibility
• Promotes authentic learning (incorporating expert feedback)
• Student’s scholarly work + own critical reflection + the work and voice of
others
• Web 2.0 influence on educational process
• customizable portals/dashboards, iGoogle, My Yahoo!
• Learning is a collaborative exercise in collection, orchestration, remixing,
& integration of data into knowledge building
• Emphasis on metacognition in learning

10. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
10
Learning
Layers
ROLE Approach to the Design
of Learning Experiences
What is the impact of these findings from behavioral & cognitive psychology on
design of Personal Learning Environments?
learner profile information
is defined and revised
learner finds and selects
learning resources
learner works on selected
learning resources
plan
learnreflect
learner input regarding
goals, preferences, …
creating PLE
recommendations
from peers or tutors
assessment and
self-assessment
evaluation and
self-evaluation
feedback
(from different sources)
learner should understand and
control own learning process
ROLE infrastructure should
provide adaptive guidance
attaining skills using different
learning events (8LEM)
learner reflects and reacts
on strategies, achievements,
and usefulness
monitoring
recommen-dations
be aware of

11. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
11
Learning
Layers
Learning Analytics Visualization –
Dashboards

12. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
12
Learning
Layers
Learning Analytics vs. Community
Learning Analytics
Formal Learning Learning Analytics Community
Regulated
Learning
Community
Learning Analytics
Environment LMS EDM/Visual
Analytics (VA) –
xAPI??
Responsive Open
Learning
Environment
(ROLE)
Data Mining / VA /
Social Network
Analysis / Role
Mining
Tools Fixed LMS Specific Eco-System Tool Recommender
Activities Fixed Content
Recommender
Dynamic Content
Recommender /
Expert
Recommender
Goals Fixed Progress Dynamic Progress / Goal
Mining / Refinement
Communities Fixed Not applicable Dynamic (Overlapping)
Community
Detection
Use Cases Courses Learning Paths Peer Production /
Scaffolding
Semantic Networks
of Learners /
Annotations

13. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
13
Learning
Layers
COMMUNITY LEARNING
ANALYTICS

14. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
14
Learning
Layers
Learning Communities
Communication /
Cooperation ?
Cultural heritage
in Afghanistan
Database
Content input / request
Content retrieval
Surveying/
safeguarding
Sketch
drawing
Photographing
Surveying/
recording
GPS
positioning
Experiences
imparting
Administration
UNESCO
Teaching/
presentation
Asia
ICOMOS
Standards
defining
Research
RWTH
Aachen
SPACH
www.bamiyan-development.org

15. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
15
Learning
Layers
Experts in
Learning Communities
■ In learning communities
many experts from
different fields meet
– Intergenerational learning
– Interdisciplinary learning
■ New Openness for Amateur
Contributions
■ Methods, Tools & CoP
co-develop
– Expert role models needed
– Expert identification based
on complex media traces

16. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
16
Learning
Layers
Communities of Practice
■ Communities of practice (CoP) are groups of people
who share a concern or a passion for something they
do and who interact regularly to learn how to do it
better (Wenger, 1998)
■ Characterization of experts in CoP
– Shared competence in the domain
– Shared practice over time by interactions
– Expertise based on gaining and having reputation within the CoP
– Being an expert vs. being a layman, a newcomer, an amateur etc.
– Informal leadership
– Identity as an expert depends on the lifecycle of the communities
Expertise in highly dynamic, locally distributed multi-disciplinary
and heterogeneous communities?

17. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
17
Learning
Layers
Proposed Development of the
Community Learning Analytics Field
■ Will happen J Big Data by Digital Eco Systems (Quantitative Analysis)
– A plethora of targets (Small Birds)
– Professional Communities are distributed in a long tail
– Professional Communities use a digital eco system
– An arsenal of weapons (Big Guns)
– A growing number of community learning analytics methods
– Combined methods from machine intelligence and knowledge representation
■ May not happen L Deep Involvment with community
(Qualitative Analysis)
– Domain knowledge for sense making
– Passion for community and sense of belonging
– Community learns as a whole
→ Community Learning Analytics for the Community by the Community

18. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
18
Learning
Layers
Interdisciplinary Multidimensional
Model of Communities
■ Collection of CoP Digital Traces in a MediaBase
– Post-Mortem Crawlers
– Real-time, mobile, protocol-based (MobSOS)
– (Automatic) metadata generation by Social Network Analysis
■ Social Requirements Engineering with i* Framework
for defining goals and dependencies in CoP
Social Software
Cross-Media Social Network
Analysis on Wiki, Blog, Podcast,
IM, Chat, Email, Newsgroup, Chat
…
Web 2.0 Business
Processes (i*)
(Structural, Cross-media)
Members
(Social Network Analysis: Centrality,
Efficiency, Community Detection)
Network of Artifacts
Content Analysis on Microcontent, Blog entry, Message,
Burst, Thread, Comment, Conversation, Feedback (Rating)
Network of Members
Communities of practice
Media Networks

19. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
19
Learning
Layers
Community Learning Analytics
in CoP
■ User-to-Service Communication
• CoP-aware Usage Statistics
• Identification of successful CoP services
• Identification of CoP service usage patterns
■ User-to-User Communication
• CoP-aware Social Network Analysis
• Identification of influential CoP members
• Identification of CoP member interaction/learning patterns
+

20. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
20
Learning
Layers
COMMUNITY LEARNING
ANALYTICS –
EXPERT IDENTIFICATION

21. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
21
Learning
Layers
Space (shared by multiple users)
Video-Based Learning Framework
Web application (composed of widgets)
Widget (collaborative web
component)
http://role-sandbox.eu/

22. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
22
Learning
Layers
ROLE Sandbox – Geospatial &
Temporal Access
§ Users: 1046
§ Widgets: 523
§ Spaces/Activities: 1377
§ Shared Resources: 3764

23. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
23
Learning
Layers
YouTell - A Web 2.0 Service for
Collaborative Storytelling
§ Collaborative storytelling
§ Web 2.0 Service
§ Story search and
“pro-sumption”
§ Tagging
§ Ranking/Feedback
§ Expert finding
§ Recommending
Klamma, Cao, Jarke: Storytelling on the Web 2.0 as a New Means of Creating Arts
Handbook of Multimedia for Digital Entertainment and Arts, Springer, 2009

24. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
24
Learning
Layers
Expert Finding – Computation of
Actual Knowledge
■ Data vector consists of
– Personal data vector
– Competences, skills,
qualification profile
– Self-entered data
– Story data vector
– Visits of stories
– Involvement in projects
– Expert data vector
– Advice given
– Advice received
– Value = #Keywords – Date
Decay – Feedback
Motivation
PESE:
Web 2.0 –Anwen-
dung für community-
basiertes Storytelling
Der PESE-
Prototyp
Evaluierung des
Prototypen
Zusammen-
fassung
Ausblick
Find the most appropriate expert
Data vector represents knowledge of the expert

25. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
25
Learning
Layers
Knowledge-Dependent
Learning Behaviour in Communities
Renzel, Cao, Lottko, Klamma: Collaborative Video Annotation for Multimedia Sharing between Experts and Amateurs,
WISMA 2010, Barcelona, Spain, May 19-20, 2010
§ Expert finding algorithm: Knowledge value of community sorted by keywords
§ Community behavior: Experts spent more time on the services
§ Experts prefers semantic tags while amateurs uses “simple” tags frequently
§ Community tags: Experts use more precise tags

26. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
26
Learning
Layers
Threads to Expert Finding
■ Compromising techniques
— Sybil attack [Douc 2002], Reputation theft, Whitewashing attack, etc..
— Compromising the input and the output of the expert identification algorithm
■ Example: Sybil attacks
— Fundamental problem in open collaborative Web systems
— A malicious user creates many fake accounts (Sybils) which all reference the user to
boost his reputation (attacker’s goal is to be higher up in the rankings)
Sybil
region
Honest
region
A0ack
edges

27. Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
27
Learning
Layers
Conclusions & Outlook
■ Learning Analytics for Formal and Informal Learning
– Challenges for data gathering and data management
– Challenges for quantitative and qualitative analysis
– Challenges for visual analytics, feedback and
interventions
■ Community Learning Analytics
– Responsive Open Learning Environments (ROLE)
– Learning Layers – Learning Analytics as a Service
– Social Network Analysis
– Community Detection
– Expert Identification