Technical Challenges for Realizing Learning Analytics

Lehrstuhl Informatik 5 (Information Systems)

Prof. Dr. M. Jarke 1

Learning Layers

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Technical Challenges for Realizing Learning Analytics

Ralf Klamma Advanced Community Information Systems (ACIS)

RWTH Aachen University, Germany [email protected]

LEARNTEC, Karlsruhe, Germany, January 27th, 2015



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



Learning Layers

Responsive Open

Community Information

Systems

Community Visualization

and Simulation

Community Analytics

Community

Support

Web Analytics W

eb E

ngin

eerin

g

Advanced Community Information Systems (ACIS) Group @ RWTH Aachen

Requirements Engineering



Learning Layers

Agenda

Lear

ning A

nalyt

ics

Comm

unity

Lear

ning A

nalyt

ics

Expe

rts in

Com

munit

y Info

rmati

on

Syste

ms

Over

lappin

g Com

munit

y Ide

ntific

ation

Conc

lusion

s & O

utloo

k



Learning Layers

LEARNING ANALYTICS



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



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



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



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



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

learn reflect

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



Learning Layers Learning Analytics Visualization –

Dashboards



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



Learning Layers

COMMUNITY LEARNING ANALYTICS



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



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



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?



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



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



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

+



Learning Layers

COMMUNITY LEARNING ANALYTICS – EXPERT IDENTIFICATION



Learning Layers

Space (shared by multiple users)

Video-Based Learning Framework

Web application (composed of widgets)

Widget (collaborative web component)

http://role-sandbox.eu/



Learning Layers ROLE Sandbox – Geospatial &

Temporal Access

§  Users: 1046 §  Widgets: 523 §  Spaces/Activities: 1377 §  Shared Resources: 3764



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



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



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



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



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

Science

Technical Challenges for Realizing Learning Analytics