Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
1
Learning
Layers
This slide deck is licensed under a Crea...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
2
Learning
Layers
Responsive
Open
Community
Information
Sy...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
3
Learning
Layers
Abstract
With the advent of data collect...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
4
Learning
Layers
Agenda
TheNetworked
Scientist
LearningAn...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
5
Learning
Layers
THE NETWORKED SCIENTIST
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
6
Learning
Layers
Iconographic Images of Science
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
7
Learning
Layers
Iconographic Images of Science
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
8
Learning
Layers
Iconographic Images of Science
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
9
Learning
Layers
Computer Science Knowledge Network
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
10
Learning
Layers
The Knowledge Map of Computer Science
M...
Map of computer science in 1990 Map of computer science in 1995
Map of computer science in 2000 Map of computer science in 2005
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
14
Learning
Layers
Mechanical Turks
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
15
Learning
Layers
Consequences for Scientometrics
■  The ...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
16
Learning
Layers
LEARNING ANALYTICS
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
17
Learning
Layers
What is Learning Analytics?
"Field asso...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
18
Learning
Layers
Leaking
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
19
Learning
Layers
Recent News about
Lea(rn|k)ing Analytic...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
20
Learning
Layers
TeLLNet - SNA for European
Teachers‘ Li...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
21
Learning
Layers
Analysis and Visualization of
Lifelong ...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
22
Learning
Layers
Ethical Concerns During the Project
■  ...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
23
Learning
Layers
Still, Technology is a Powerful Tool
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
24
Learning
Layers
LESSONS LEARNT
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
25
Learning
Layers
Datability
■  A clear identification of...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
26
Learning
Layers
What we get
■  Will happen J Big Data ...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
27
Learning
Layers
Learning Analytics vs. Community
Learni...
Lehrstuhl Informatik 5
(Information Systems)
Prof. Dr. M. Jarke
28
Learning
Layers
Reflective Open
Community Information S...
Do Mechanical Turks Dream of Big Data?
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Do Mechanical Turks Dream of Big Data?

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Ralf Klamma, RWTH Aachen University
ACIS Group
BJET-Wiley Seminar
Birmingham, March 11, 2014

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Do Mechanical Turks Dream of Big Data?

  1. 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. Do Mechanical Turks Dream of Big Data? Advanced Community Information Systems (ACIS) RWTH Aachen University, Germany klamma@dbis.rwth-aachen.de
  2. 2. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 2 Learning Layers Responsive Open Community Information Systems Community Visualization and Simulation Community Analytics Community Support WebAnalytics WebEngineering Advanced Community Information Systems (ACIS) Requirements Engineering
  3. 3. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 3 Learning Layers Abstract With the advent of data collections on a planetary level, also the role of researchers producing, processing and analysing such data sets is debated as heated as in the early days of nuclear research. It seems that the Dr. Strangelove image of scientists has turned into a faceless mass of Mechanical Turks hiding behind agencies and large research networks. So, it is time to peek behind the curtain to disclose the network nature of modern science. A basic ethical obligation is to get enough knowledge to make informed decisions. So, we visit some recent incidents of big data debates in higher education and mass surveillance. In particular, we are questioning the role of computer science as producer of dual use weapons of mass surveillance. Ironically, computer science is not only part of the problem but also part of the solution. We discuss some interesting socio-technical approaches of giving back the power of data transparently into the hands of the owners.
  4. 4. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 4 Learning Layers Agenda TheNetworked Scientist LearningAnalytics LessonsLearnt Conclusions& Outlook
  5. 5. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 5 Learning Layers THE NETWORKED SCIENTIST
  6. 6. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 6 Learning Layers Iconographic Images of Science
  7. 7. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 7 Learning Layers Iconographic Images of Science
  8. 8. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 8 Learning Layers Iconographic Images of Science
  9. 9. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 9 Learning Layers Computer Science Knowledge Network
  10. 10. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 10 Learning Layers The Knowledge Map of Computer Science Map of computer science in 2010 [Pham, Klamma & Jarke, SNAM 2010] HCI Networks and Communication Software Engineering Artificial Intelligence Theory Database Computer Graphics Computer Vision Security and Privacy Distributed and Parallel Computing Machine Learning Data Mining
  11. 11. Map of computer science in 1990 Map of computer science in 1995
  12. 12. Map of computer science in 2000 Map of computer science in 2005
  13. 13. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 14 Learning Layers Mechanical Turks
  14. 14. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 15 Learning Layers Consequences for Scientometrics ■  The great „iron fence“ has been replaced by many fences around research communities –  Dr. Strangelove is a faceless community now –  The long tail of research communities –  Many research communities under public pressure (e.g. environmental sciences - http://www.pangaea.de/) –  It will get worse! (open access/data, public funding cuts) ■  Big Data Research for Understanding Science –  Social Network Analysis, Machine Learning –  Mechanical Turks? ■  Where is the research ethics? –  Menlo Report (2012)
  15. 15. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 16 Learning Layers LEARNING ANALYTICS
  16. 16. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 17 Learning Layers What is Learning Analytics? "Field associated with deciphering trends and patterns from educational big data, or huge sets of student- related data, to further the advancement of a personalized, supportive system of higher education." (2013 Horizon Report)
  17. 17. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 18 Learning Layers Leaking
  18. 18. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 19 Learning Layers Recent News about Lea(rn|k)ing Analytics L ■  BIFIE-Leak - 400.000 confidential tests of pupils and 37.000 E- mail addresses of Austrian teachers have been found on Romanian servers accessible from the Internet (Die Presse) ■  UMD-Leak - 300.000 personal record data were compromised by a hack at the University of Maryland (UMD) ■  FSU-Leak: 47.000 teachers in training data leaked at Florida State University (FSU) ■  Oxford-Leak: University of Oxford Leaks List of Its 50 Worst- Performing Students (The Chronicle of Higher Education) This list is really endless
  19. 19. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 20 Learning Layers TeLLNet - SNA for European Teachers‘ Life Long Learning ■  How to manage and handle large scale data on social networks? ■  How to analyse social network data in order to develop teachers’ competence, e.g. to facilitate a better project collaboration? ■  How to make the network visualization useful for teachers’ lifelong learning? Song, Petrushyna, Cao, Klamma: Learning Analytics at Large: The Lifelong Learning Network of 160, 000 European Teachers. EC-TEL 2011
  20. 20. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 21 Learning Layers Analysis and Visualization of Lifelong Learner Data
  21. 21. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 22 Learning Layers Ethical Concerns During the Project ■  The eTwinning platform data should be protected as much as possible –  No live access, access only in anonymous dumps –  Better: Privacy preserving technologies ■  Teacher Workshops –  Identification of teachers only with consent ■  Learning Analytics Tools –  Tool was available on the Web –  Data accessible only for teacher in the networks
  22. 22. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 23 Learning Layers Still, Technology is a Powerful Tool
  23. 23. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 24 Learning Layers LESSONS LEARNT
  24. 24. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 25 Learning Layers Datability ■  A clear identification of benefits, risks and harms for collecting ICT data ■  Ethical guidelines, approval routines and best practices for data sharing in science and education ■  Transparency and accountability without the loss of privacy ■  Academic freedom
  25. 25. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 26 Learning Layers What we get ■  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
  26. 26. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 27 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
  27. 27. Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke 28 Learning Layers Reflective Open Community Information Systems •  Network Models •  Network Analysis •  Actor Network Theory •  Communities of Practice •  Expert Identification •  Community Detection •  Web Mining •  Recommender Systems •  Multi Agent Simulation WebAnalytics •  Advanced Web & Multimedia Technologies •  XMPP •  HTML5 •  MPEG-7 •  Web Services •  REST •  LAS •  Cloud Computing •  Mobile Computing WebEngineering • MediaBase • MobSOS • D-VITA • Requirements Bazaar • Direwolf • AERCS/CAMRS • yFiles • Repast • AERCS • LAS & LAS2peer • youTell • SeViAnno 2.0 Responsive Open Community Information Systems Community Visualization & Simulation Community Analytics Community Support Requirements Engineering •  Large-Scale Web-Based Social Requirements Engineering •  Agent and Goal Oriented i* Modeling •  Participatory Community Design

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