community learning analytics - challenges and opportunities - icwl 2013 invited talk

Lehrstuhl Informatik 5 (Information Systems)

Prof. Dr. M. Jarke 1

Learning Layers

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Community Learning Analytics –

Challenges and Opportunities

Ralf Klamma Advanced Community Information Systems (ACIS)

RWTH Aachen University, Germany [email protected]

ICWL 2013, Kensing, Taiwan, October 7, 2013



Learning Layers

Abstract Learning Analytics has become a major research area recently. In particular learning institutions seek ways to collect, manage, analyze and exploit data from learners and instructors for the facilitation of formal learning processes. However, in the world of informal learning at the workplace, knowledge gained from formal learning analytics is only applicable on a commodity level. Since professional communities need learning support beyond this level, we need a deep understanding of interactions between learners and other entities in community-regulated learning processes - a conceptual extension of self-regulated learning processes. In this presentation, we discuss scaling challenges for community learning analytics and give both conceptual and technical solutions. We report experiences from ongoing research in this area, in particular from the two EU integrating project ROLE (Responsive Open Learning Environments) and Learning Layers (Scaling up Technologies for Informal Learning in SME Clusters).



Learning Layers

RWTH Aachen University

•  1,250 spin-off businesses have created around 30,000 jobs in the greater Aachen region over the past 20 years. •  IDEA League

•  Germany’s Excellence Initiative: 3 clusters of excellence, a graduate school and the institutional strategy “RWTH Aachen 2020: Meeting Global Challenges”

•  260 institutes in 9 faculties as Europe’s leading institutions for science and research

•  Currently around 38,000 students are enrolled in over 130 academic programs

•  Over 5,000 of them are international students hailing from 120 different countries

http://www.rwth-aachen.de/cms/root/Die_RWTH/Profil/~enw/Daten_Fakten/lidx/1/



Learning Layers

Responsive Open

Community Information

Systems

Community Visualization

and Simulation

Community Analytics

Community

Support

Web Analytics W

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Advanced Community Information Systems (ACIS) Group @ RWTH Aachen

Requirements Engineering



Learning Layers

Agenda

Lear

ning A

nalyt

ics

Comm

unity

Lear

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ics

Lear

ning L

ayer

s

Conc

lusion

s & O

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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

–  Learning Analytics or Educational Data Mining

High-quality, specially designed, learning materials like books or course material

Gaps in personal knowledge identified mostly by real-world practice



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 Learning Management System 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

guidance & freedom of

learner

motivation of learner (intrinsic,

extrinsic)

stimulation of learner’s meta-

cognition

collaboration & good practice sharing among

peers

personalization & adaptability to learner & context What is the impact of these

findings from behavioral & cognitive psychology on

design of learning?

Goal setting Planning Reflection

Control & Responsibility Recommendation



Learning Layers Responsive Open Learning

Enviroments (ROLE) 2009-2012

•  Empower the learner to build their own responsive learning environment ROLE Vision

•  Awareness and reflection of own learning process Responsiveness

•  Individually adapted composition of personal learning environment User-Centered



Learning Layers ROLE Approach to the Design

of Learning Experiences How can we enable und exploit Learning Analytics

for 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 ROLE

Technical Infrastructure

  Sucessfully deployed in industry and education  Open Source Software Development Kit  ROLE Widget Store (role-widgetstore.eu)  ROLE Sandbox (role-sandbox.eu)



Learning Layers

ROLE PLE Sandbox & SDK

Space (shared by multiple users)

Web application (composed of widgets)

Widget (collaborative web component)

http://role-sandbox.eu/



Learning Layers Learning Analytics Visualization –

ROLE Dashboards



Learning Layers Learning Analytics vs. Community

Learning Analytics Formal Learning Learning Analytics Community

Regulated Learning

Community Learning Analytics

Environment LMS EDM/VA CIS/ROLE DM/VA/SNA/Role Mining

Tools Fixed LMS Specific Eco-System Tool Recommender

Activities Fixed Content Recommender

Dynamic Content Recommender / Expert Recommender

Goals Fixed Progress Dynamic Progess / 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

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 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 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

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 manage-ment

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 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

PLE/Community Information

Systems

Web 2.0 Processes (i*) (Structural, Cross-media)

Members (Social Network Analysis: Centrality,

Efficiency, Community Detection)

Network of Artifacts Content Analysis, Sentiment Analysis, Tiopic Mining, Goal

Mining, Social Network Analysis

Network of Members

Communities of practice

Media Networks



Learning Layers Community Learning Analytics

in CoP   User-to-Service Communication

•  Identification of successful CoP services •  Identification of CoP service usage patterns

  User-to-User Communication •  CoP-aware Social Network Analysis •  Identification of personal learning activities/goals/patterns etc. •  Identification of expert CoP members •  Identification of overlapping communities

+



Learning Layers Supporting Community Practice

with the MobSOS Success Model



Learning Layers Community SRE Processes–

i* Strategic Rationale



Learning Layers

ROLE Requirements Bazaar – Community-aware Requirements Prioritization

Factors influencing requirements ranking

User-controlled weighting of ranking factors

Community-dependent requirements ranking lists



Learning Layers

LEARNING LAYERS – SCALING UP TECHNOLOGIES FOR INFORMAL LEARNING IN SME CLUSTERS



Learning Layers

Learning Layers   Large scale integrated project on

scaling up informal workplace learning

  Objectives –  Support informal learning –  Unlock peer production –  Scaffold meaningful learning

  Two regional clusters –  Construction (Germany) –  Healthcare (UK)

29

http://learning-layers.eu/



Learning Layers

Maturing

Interacting with People at the workplace Paul discovers a problem at the construction site with PLC equipment ...

Generating dynamic Learning Material The regional training center observes the Q&A and links it to their course material ...

Q: How to use PLC equipment …? •  I have seen this before here … • Last time I did it, I … • Here is something helpful

Social Semantic Layer Emerging shared meaning,

giving context Energy Consump.on

Lightning

X3-‐PVQ X3-‐PJC

X3-‐POZ PLC Equipment Instructional Taxonomy

• What is … • How to … • Example of …

Tutorial: How to Use PLC What is PLC How to use it? Examples Further Information Hot Questions and Answers

Work Practice Taxonomy •  Installation • Testing • Operation

Peter

Paul

Mary

Interacting in the Physical Workplace Physical workplace is equipped with QR tags, learning materials are delivered just in time ...

A list of helpful resources • Tutorials: How to use … • Persons: Peter, Mary, … • Work Practice: Installation,.. • Concepts: PLC, Lightning • Q&A: …,

Learning Layers in the Construction Industry



Learning Layers

Learning Layers – Scaling up Technologies for Informal Learning in SME Clusters

Learning Layers – Scaling Technologies for Informal Learning



Learning Layers Collaborative Annotation of

Peer-Produced Video

32



Learning Layers AnViAnno & SeViAnno: Tools for Semantic

Annotations of (Mobile) Multimedia

Semantic Mobile Multimedia Services §  Collaborative Creation of Semantic Annotations §  Advanced Services via Cloud Computing

Multimedia semantization §  Descriptive Annotations (Search & Locate) §  Technical/administrative Annotations §  Structural Annotations

Mobile Multimedia Acquisition §  Capturing and Sharing Meaning §  3D/ real-time/ context-aware



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 ABack edges



Learning Layers

Conclusions & Outlook

  From Web 2.0 Knowledge Management to Personal Learning Environments

 ROLE - Responsive Open Learning Environments –  Enabling Learning Analytics in Personal Learning

Environments   Learning Layers - Scaling up Technologies for

Informal Learning in SME Clusters –  Informal Learning on the Workplace –  Community Learning Analytics on a Large Scale –  Collaborative Semantic Video Annotation

community learning analytics - challenges and opportunities - icwl 2013 invited talk

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