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Turning Learning into Numbers
      A Learning Analytics Framework




      Graphic by Alex Guerten, 2008

Hendrik Drachsler & Wolfgang Greller
Centre for Learning Sciences and Technologies (CELSTEC)
                                  1
Goals of the Presentation

    LA Framework
                   Examples
                              Data
                              initiatives
                              Participation




               2
A view on Learning Analytics
The Learning
Analytics
Framework



Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers.
Toward a Generic Framework for Learning Analytics. Journal of Educational
Technology & Society.
                                         3
4
4
4
4
4
4
4
Zooming into the Framework

Practical
examples for
application
and use



               5
Stakeholders
  data                     data
subjects                  clients




                 6
Objectives
Reflection




(Glahn, 2009)       7
Objectives
Reflection




(Glahn, 2009)       7
Objectives
Reflection                    Prediction




(Glahn, 2009)       7
Educational Data




Verbert, K., Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
& Society.                                    8
Educational Data




Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society.                                    8
Educational Data




Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society.                                    8
Educational Data
                         1.Privacy
                         2.Prepare datasets
                         3.Share datasets
                         4.Body of knowledge

Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for
Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder
Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven
SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28,
Research to Support Learning and Knowledge Analytics. Journal of Educational Technology
2010, Barcelona, Spain.
& Society.                                    8
Technologies
               Learning Analytics




Hanani et al., (2001). Information Filtering: Overview of Issues,
  Research and Systems", User Modeling and User-Adapted
  Interaction, 11, 2001 9
Technologies
               Learning Analytics




Hanani et al., (2001). Information Filtering: Overview of Issues,
  Research and Systems", User Modeling and User-Adapted
  Interaction, 11, 2001 9
Technologies
Prediction




                   9
Technologies
                                                                          Reflection




Peter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011):
On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection
                                               9
Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)
Technologies
                                                                          Reflection




Peter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011):
On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection
                                               9
Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)
Constraints




     10
Constraints

1.Legal protection




                     10
Constraints

1.Legal protection
2.Privacy




                     10
Constraints

1.Legal protection
2.Privacy
3.Ethics




                     10
Constraints

1.Legal protection
2.Privacy
3.Ethics
4.Ownership



                     10
Privacy




     11
Privacy
1.Privacy as confidentiality
  The right to be let alone (Warren and Brandeis, 1890)




                           11
Privacy
1.Privacy as confidentiality
  The right to be let alone (Warren and Brandeis, 1890)

2.Privacy as control
  The right of the individual to decide what information
  about herself should be communicated to others and
  under which circumstances.




                            11
Privacy
1.Privacy as confidentiality
  The right to be let alone (Warren and Brandeis, 1890)

2.Privacy as control
  The right of the individual to decide what information
  about herself should be communicated to others and
  under which circumstances.

3.Privacy as practice
  The right to intervene in the flows of existing data and
  the re-negotiation of boundaries with respect to
  collected data.
                            11
Privacy solutions




     12
Privacy solutions
1.Privacy as confidentiality
  Information services that minimizing, secure or
  anonymize the collected information




                          12
Privacy solutions
1.Privacy as confidentiality
  Information services that minimizing, secure or
  anonymize the collected information

2.Privacy as control
  Identity Management Systems (IDMS),
  with access control rules




                          12
Privacy solutions
1.Privacy as confidentiality
  Information services that minimizing, secure or
  anonymize the collected information

2.Privacy as control
  Identity Management Systems (IDMS),
  with access control rules

3.Privacy as practice
  Timestamp on data, data degradation technologies

                          12
Competences




      13
Reading score of 15 year olds of countries
        Competences
 compared to the percentage of migrants




                    13
Reading score of 15 year olds of countries
        Competences
 compared to the percentage of migrants


      1.E-literacy
      2.Interpretation skills
      3.Self-directedness
      4.Ethical understanding



                     13
Data initiatives
Relevant
data initiatives
for Learning
Analytics




                   14
Data Quality / Standard




           15
Data Quality / Standard
CAM -
Contextualized
Attention Metadata


https://
sites.google.com/site/
camschema/




                         15
Data Citing




      16
Sharing policies




        17
Sharing policies




        17
Sharing policies




        17
Policy guidelines




A brief guide on data licenses developed by SURF and the Centre for
Intellectual Property Law (CIER), 2009 available at
www.surffoundation.nl
                                          18

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Turning Learning into Numbers - A Learning Analytics Framework

  • 1. Turning Learning into Numbers A Learning Analytics Framework Graphic by Alex Guerten, 2008 Hendrik Drachsler & Wolfgang Greller Centre for Learning Sciences and Technologies (CELSTEC) 1
  • 2. Goals of the Presentation LA Framework Examples Data initiatives Participation 2
  • 3. A view on Learning Analytics The Learning Analytics Framework Greller, W., & Drachsler, H., (submitted). Turning Learning into Numbers. Toward a Generic Framework for Learning Analytics. Journal of Educational Technology & Society. 3
  • 4. 4
  • 5. 4
  • 6. 4
  • 7. 4
  • 8. 4
  • 9. 4
  • 10. 4
  • 11. Zooming into the Framework Practical examples for application and use 5
  • 12. Stakeholders data data subjects clients 6
  • 15. Objectives Reflection Prediction (Glahn, 2009) 7
  • 16. Educational Data Verbert, K., Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven Research to Support Learning and Knowledge Analytics. Journal of Educational Technology & Society. 8
  • 17. Educational Data Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28, Research to Support Learning and Knowledge Analytics. Journal of Educational Technology 2010, Barcelona, Spain. & Society. 8
  • 18. Educational Data Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28, Research to Support Learning and Knowledge Analytics. Journal of Educational Technology 2010, Barcelona, Spain. & Society. 8
  • 19. Educational Data 1.Privacy 2.Prepare datasets 3.Share datasets 4.Body of knowledge Drachsler, H., et al. (2010). Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning. 1st Workshop Recommnder Verbert, in Manouselis, N., Drachsler, H., and Duval,E. (submitted).Dataset-driven SystemsK., Technology Enhanced Learning (RecSysTEL@EC-TEL 2010) September, 28, Research to Support Learning and Knowledge Analytics. Journal of Educational Technology 2010, Barcelona, Spain. & Society. 8
  • 20. Technologies Learning Analytics Hanani et al., (2001). Information Filtering: Overview of Issues, Research and Systems", User Modeling and User-Adapted Interaction, 11, 2001 9
  • 21. Technologies Learning Analytics Hanani et al., (2001). Information Filtering: Overview of Issues, Research and Systems", User Modeling and User-Adapted Interaction, 11, 2001 9
  • 23. Technologies Reflection Peter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011): On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection 9 Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)
  • 24. Technologies Reflection Peter Kraker, Claudia Wagner, Fleur Jeanquartier, Stefanie N. Lindstaedt (2011): On the Way to a Science Intelligence: Visualizing TEL Tweets for Trend Detection 9 Sixth European Conference on Technology Enhanced Learning (EC-TEL 2011)
  • 30. Privacy 11
  • 31. Privacy 1.Privacy as confidentiality The right to be let alone (Warren and Brandeis, 1890) 11
  • 32. Privacy 1.Privacy as confidentiality The right to be let alone (Warren and Brandeis, 1890) 2.Privacy as control The right of the individual to decide what information about herself should be communicated to others and under which circumstances. 11
  • 33. Privacy 1.Privacy as confidentiality The right to be let alone (Warren and Brandeis, 1890) 2.Privacy as control The right of the individual to decide what information about herself should be communicated to others and under which circumstances. 3.Privacy as practice The right to intervene in the flows of existing data and the re-negotiation of boundaries with respect to collected data. 11
  • 35. Privacy solutions 1.Privacy as confidentiality Information services that minimizing, secure or anonymize the collected information 12
  • 36. Privacy solutions 1.Privacy as confidentiality Information services that minimizing, secure or anonymize the collected information 2.Privacy as control Identity Management Systems (IDMS), with access control rules 12
  • 37. Privacy solutions 1.Privacy as confidentiality Information services that minimizing, secure or anonymize the collected information 2.Privacy as control Identity Management Systems (IDMS), with access control rules 3.Privacy as practice Timestamp on data, data degradation technologies 12
  • 39. Reading score of 15 year olds of countries Competences compared to the percentage of migrants 13
  • 40. Reading score of 15 year olds of countries Competences compared to the percentage of migrants 1.E-literacy 2.Interpretation skills 3.Self-directedness 4.Ethical understanding 13
  • 42. Data Quality / Standard 15
  • 43. Data Quality / Standard CAM - Contextualized Attention Metadata https:// sites.google.com/site/ camschema/ 15
  • 48. Policy guidelines A brief guide on data licenses developed by SURF and the Centre for Intellectual Property Law (CIER), 2009 available at www.surffoundation.nl 18
  • 55. Learning Analytics Challenges • Reduce the drop-out rate by 10% through applying Learning Analytics prediction and reflection techniques. • Customize data mining techniques to learning and educational reality. • Evaluation criteria for Learning Analytics applications • Generic infrastructure for sharing, analyzing and reusing educational data. • Applying existing privacy and legal protection solutions for Learning Analytics. 21
  • 56. Open issues 1. Evaluation of LA approaches 2. Comparable experiments 3. Publicly available datasets 4. Body of knowledge 5. New Learning Analytic applications 6. Privacy and data protection 7. Best practice and ethical guidelines 22
  • 58. Special Interest Group - dataTEL 24
  • 59. Special Interest Group - dataTEL 1.Networking 2.Privacy, legal, ethics 3.LA datasets 4.LA products http://bit.ly/datatel 24
  • 60. Many Thanks::Questions? This presentation is available at: slideshare.com/ Drachsler Blackmore’s custom-built LSD Drive http://www.flickr.com/photos/rootoftwo/ Email: hendrik.drachsler@ou.nl Email: wolfgang.greller@ou.nl 25