Supporting Professional Communities in the Next Web

Lehrstuhl Informatik 5 (Information Systems)

Prof. Dr. M. Jarke 1

Learning Layers

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Supporting Professional

Communities in the Next Web

Ralf Klamma Advanced Community Information Systems (ACIS)

RWTH Aachen University, Germany [email protected]

PWM Wissenstag Social Enterprise @ I-KNOW 2013 Wednesday, September 4, 2013 in Graz (Austria)



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

The N

ext W

eb

Lear

ning L

ayer

s Use

-Cas

e Se

manti

c Vide

o Ann

otatio

n

Resp

onsiv

e Ope

n Lea

rning

En

viron

ment

(ROL

E)

Dire

Wolf

- Di

stribu

ting a

nd

Migr

ating

Use

r Inte

rface

s for

W

idget-

base

d Web

App

licati

ons

Conc

lusion

s & O

utloo

k



Learning Layers

The Web:

Long Tail & Fragments

  The Web is a scale-free, fragmented network –  The power law (Pareto-Distribution etc.) –  95 % of users are located in the Long Tail (Communities) –  Trust and passion based cooperation

Island Tendrils

IN Continent Central Core OUT Continent

Tunnels

[Barabasi, 2002]

[Anderson, 2006]



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 Professional Communities –  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

Supporting highly dynamic, locally distributed, multi-disciplinary and heterogeneous communities?



Learning Layers The Next Web will

be no Internet of Things …

… but a huge collaboration platform for professional communities with (some) of the following features

–  P2P Browsers as new Web OS in the Cloud –  Mobile & Social (Location-Based Community Services) –  Near Real-Time (WebRTC, XMPP) –  Multi-Device Operations

In the following we will address some of the Web Engineering Challenges



Learning Layers

USE CASE – SEMANTIC VIDEO ANNOTATION



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 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 Semantic Video Annotation

(SeViAnno)   Aims at capturing and sharing meaning

–  In communities of practice –  For personal use

  Example –  Markup of text for learning (different colors, etc.)

  Different kinds of semantic annotations

–  Descriptive (search & locate, e.g. keywords, description) –  Technical/administrative (internal info, e.g. creation, type) –  Structural (storage & presentation, e.g. video segments)



Learning Layers

SeViAnno User Interfaces

SeViAnno (Web)

SeViAnno 2.0 (ROLE Widgets)

AnViAnno (Android)

DireWolf Sevianno (Distributed User Interface)



Learning Layers

RESPONSIVE OPEN LEARNING ENVIRONMENTS



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

Technical Infrastructure

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



Learning Layers

Space (shared by multiple users)

SeViAnno in the ROLE 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

DIREWOLF - DISTRIBUTING WEB USER INTERFACES



Learning Layers DireWolf Framework:

Distributed User Interfaces (DUI)   Challenge for a web application with only one device   Goal: distribute widgets over multiple devices of the single user

when requires full screen video, other widgets become unreachable

expand the application to multiple devices

Map on iPhone Other widgets on PC Video player on iPad



Learning Layers

Motivation

Sing

le-de

vice U

I Mu

lti-de

vice d

istrib

uted U

I

Smartphones: on-site video capture, geo-tagging on maps and video

annotation Laptops and PCs: text editing

Tablets: video players with

multi-touch interaction

Limited space and interaction

possibilities

Lack of tools & methodologies for Web-based

DUIs

Trend to using multiple personal devices



Learning Layers Distributing UI in

Widget-based Web Applications   Widget – reusable minimal

service interfaces   Widget space – container for widget

orchestration, collaboration, customization

  Widget-based application

Typical single-device widget Web application

DUI widget Web application with DireWolf



Learning Layers

Prototype

Web application (composed of widgets) Widget (collaborative web component)

EU FP7 ROLE Project http://role-sandbox.eu/ Space (shared by multiple users)



Learning Layers

Conclusions & Outlook

  Learning Layers - Scaling up Technologies for Informal Learning in SME Clusters –  Informal Learning on the Workplace –  Collaborative Semantic Video Annotation

 Responsive Open Learning Environments –  ROLE SDK for Near Real-Time Widget-Based Web

Applications –  DireWolf – a Framework to facilitate User Interactions on

a Federation of Multiple Managed Devices  Mobile Web Apps in Learning Layers

Supporting Professional Communities in the Next Web

Technology

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