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Live Interest Meter - Learning from QuantifiedFeedback in Mass Lectures                                                   ...
Agenda Introduction Use Case and Motivation Live Interest Meter App Tests and User Study Conclusions Ongoing Work12....
Use Case and Motivation           Holding a good         presentation is hard!12.04.2013                       © FZI Forsc...
Use Case and Motivation                                                           How to get better at it?                ...
Use Case and Motivation    LA for workplace learning    Capturing of feedback for reflective learning    Inspired by Qu...
Use Case and Motivation    LA for workplace learning    Capturing of feedback for reflective learning    Inspired by Qu...
Live Interest meter - LIM App Gathering, aggregation and visualization of feedback Protagonist: the meter               ...
LIM App: Evolution graph Available for Android and for Browsers (JavaScript) Browser to facilitate the configuration of ...
LIM App: Polls and Questions Polling and anonymous rated questions12.04.2013              © FZI Forschungszentrum Informa...
LIM App: JavaScript Version12.04.2013       © FZI Forschungszentrum Informatik   10
Experimental tests (1) Project meeting 10 participants used LIM Discussion driven12.04.2013              © FZI Forschun...
Experimental tests (1) Project meeting 10 participants used LIM Discussion driven (2) Lecture at university           ...
Experimental tests Project meeting 10 participants used LIM Discussion driven Lecture at university                   ...
User Study Main goals        Refine use case        Guide further development                In which scenarios and ho...
User Study 20 qualitative interviews        Willing to substitute traditional questionnaires for captured feedback      ...
User Study – Reaction to feedback                 What do you consider realistic regarding             how you can react t...
User Study – Data collection             64,37 %                             Data collected live and continuously is      ...
User Study – Information needed  Which informationwould the presenters  like to know aboutand which information does the a...
User Study – Characteristics of the LIM App             How participants evaluated the LIM App features12.04.2013         ...
Conclusion             78,16 % found the idea behind                   57,47 % would like to test the               the LI...
Towards a second prototype    Usability improvements    Support for reflection after the presentation    From presenter...
Any questions?      THANK YOU!                                                            rivera@fzi.de                   ...
Theoretical Model & Related Work12.04.2013        © FZI Forschungszentrum Informatik   23
Theoretical background[1] Applying Quantified Self Approaches to Support Reflective Learning. Verónica Rivera-Pelayo, Vale...
Related Work Clickers and feedback tools        Polling        Student outcomes and comprehension        Student atten...
ARS and Clickers    [1] M. Akbari, G. Böhm, and U. Schroeder. Enabling communication and feedback in mass     lectures. I...
Reflective learning in TEL    [1] R. Ferguson, S. B. Shum, and R. D. Crick. EnquiryBlogger: using widgets to support     ...
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Live Interest Meter - Learning from Quantified Feedback in Mass Lectures

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Presentation at the LAK Conference 2013 in Leuven, Belgium

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Live Interest Meter - Learning from Quantified Feedback in Mass Lectures

  1. 1. Live Interest Meter - Learning from QuantifiedFeedback in Mass Lectures INFORMATIK FZI FORSCHUNGSZENTRUMV. Rivera-Pelayo, J. Munk, V. Zacharias, and S. BraunFZI Research Center for Information Technology, Karlsruhe, GermanyLAK Conference 2013 – Leuven, Belgium10th April 2013
  2. 2. Agenda Introduction Use Case and Motivation Live Interest Meter App Tests and User Study Conclusions Ongoing Work12.04.2013 © FZI Forschungszentrum Informatik 2
  3. 3. Use Case and Motivation Holding a good presentation is hard!12.04.2013 © FZI Forschungszentrum Informatik 3
  4. 4. Use Case and Motivation How to get better at it? • With practice • Get feedback Holding a good • Learn from the feedback! presentation is hard!12.04.2013 © FZI Forschungszentrum Informatik 4
  5. 5. Use Case and Motivation LA for workplace learning Capturing of feedback for reflective learning Inspired by Quantified Self approaches Mass lectures, virtual courses and conferences12.04.2013 © FZI Forschungszentrum Informatik 5
  6. 6. Use Case and Motivation LA for workplace learning Capturing of feedback for reflective learning Inspired by Quantified Self approaches Mass lectures, virtual courses and conferences12.04.2013 © FZI Forschungszentrum Informatik 6
  7. 7. Live Interest meter - LIM App Gathering, aggregation and visualization of feedback Protagonist: the meter  General performance  Speech speed  Comprehension12.04.2013 © FZI Forschungszentrum Informatik 7
  8. 8. LIM App: Evolution graph Available for Android and for Browsers (JavaScript) Browser to facilitate the configuration of the presenter12.04.2013 © FZI Forschungszentrum Informatik 8
  9. 9. LIM App: Polls and Questions Polling and anonymous rated questions12.04.2013 © FZI Forschungszentrum Informatik 9
  10. 10. LIM App: JavaScript Version12.04.2013 © FZI Forschungszentrum Informatik 10
  11. 11. Experimental tests (1) Project meeting 10 participants used LIM Discussion driven12.04.2013 © FZI Forschungszentrum Informatik 11
  12. 12. Experimental tests (1) Project meeting 10 participants used LIM Discussion driven (2) Lecture at university Technical results very satisfactory 15 participants Interactive session Acceptance results rather limited12.04.2013 © FZI Forschungszentrum Informatik 12
  13. 13. Experimental tests Project meeting 10 participants used LIM Discussion driven Lecture at university Technical results very satisfactory 15 participants Interactive session Acceptance results rather limited Presenter‘s role well defined Suitable for big audiences12.04.2013 © FZI Forschungszentrum Informatik 13
  14. 14. User Study Main goals  Refine use case  Guide further development  In which scenarios and how can the quantification of feedback performed by the LIM App support reflective learning?  Which features are more appreciated by users, both presenters and audience members?12.04.2013 © FZI Forschungszentrum Informatik 14
  15. 15. User Study 20 qualitative interviews  Willing to substitute traditional questionnaires for captured feedback  Anonymity appreciated (for feedback and questions)   Polls but  Chat  Reflection needs time – preferred after the session Online survey  1 month (May-June 2012)  120 participants Presenter  87 valid responses Audience 44,82% 55,18%12.04.2013 © FZI Forschungszentrum Informatik 15
  16. 16. User Study – Reaction to feedback What do you consider realistic regarding how you can react to the feedback (as presenter)?12.04.2013 © FZI Forschungszentrum Informatik 16
  17. 17. User Study – Data collection 64,37 % Data collected live and continuously is better and more significant than only periodically collected data.12.04.2013 © FZI Forschungszentrum Informatik 17
  18. 18. User Study – Information needed Which informationwould the presenters like to know aboutand which information does the audience want to evaluate?12.04.2013 © FZI Forschungszentrum Informatik 18
  19. 19. User Study – Characteristics of the LIM App How participants evaluated the LIM App features12.04.2013 © FZI Forschungszentrum Informatik 19
  20. 20. Conclusion 78,16 % found the idea behind 57,47 % would like to test the the LIM App very positive LIM App Readiness of presenters to learn retrospectively Motivate students to use the application Main concern is distraction Applying Learning Analytics to informal workplace learning and data gathered during the learning process12.04.2013 © FZI Forschungszentrum Informatik 20
  21. 21. Towards a second prototype Usability improvements Support for reflection after the presentation From presenter to collaborative approach e.g. topics Mock-ups to adapt the results to real features12.04.2013 © FZI Forschungszentrum Informatik 21
  22. 22. Any questions? THANK YOU! rivera@fzi.de @veronicarp vriverapelayo12.04.2013 © FZI Forschungszentrum Informatik 22
  23. 23. Theoretical Model & Related Work12.04.2013 © FZI Forschungszentrum Informatik 23
  24. 24. Theoretical background[1] Applying Quantified Self Approaches to Support Reflective Learning. Verónica Rivera-Pelayo, Valentin Zacharias, Lars Müller,Simone Braun. Learning Analytics and Knowledge 2012 (LAK 2012), Vancouver, Canada[2] A Framework for Applying Quantified Self Approaches to Support Reflective Learning. Verónica Rivera-Pelayo, ValentinZacharias, Lars Müller, Simone Braun. IADIS International Conference on Mobile Learning (Mlearning 2012), Berlin, Germany 12.04.2013 © FZI Forschungszentrum Informatik 24
  25. 25. Related Work Clickers and feedback tools  Polling  Student outcomes and comprehension  Student attendance and interest on the course Reflective Learning in TEL  Student’s perspective  Data from LMS, blogs or software used LIM App  Improve lecturer/presenter’s professional performance  Students give feedback and contribute to it  Enrich lecture  Feedback contextualized by polls, questions and chat  Gathering of data during the lecture4/12/2013 © FZI Forschungszentrum Informatik 25
  26. 26. ARS and Clickers [1] M. Akbari, G. Böhm, and U. Schroeder. Enabling communication and feedback in mass lectures. In ICALT, pages 254{258, 2010. [2] M. Bonn, S. Dieter, and H. Schmeck. Kooperationstools der Notebook Universität Karlsruhe (TH). In Mobiles Lernen und Forschen, pages 63-71. Klaus David, Lutz Wegener (Hrsg.), November 2003. [3] J. E. Caldwell. Clickers in the large classroom: Current research and Best-Practice tips. CBE Life SciEduc, 6(1):9-20, Mar. 2007. [4] D. Duncan and E. Mazur. Clickers in the Classroom: How to Enhance Science Teaching Using Classroom Response Systems. Pearson Education, 2005. [5] J. Hadersberger, A. Pohl, and F. Bry. Discerning actuality in backstage - comprehensible contextual aging. In EC-TEL, volume 7563 of Lecture Notes in Computer Science, pages 126- 139. Springer, 2012. [6] D. Kundisch, P. Herrmann, M. Whittaker, M. Beutner, G. Fels, J. Magenheim, W. Reinhardt, M. Sievers, and A. Zoyke. Designing a Web-Based Application to Support Peer Instruction for Very Large Groups. In ICIS 12, Research in Progress, Orlando, USA, December 2012. [7] G. Rubner. mbclick - an electronic voting system that returns individual feedback. In WMUTE, pages 221-222. IEEE, 2012. [8] A. Wessels, S. Fries, H. Horz, N. Scheele, and W. Effelsberg. Interactive lectures: Effective teaching and learning in lectures using wireless networks. Comput. Hum. Behav., 23(5):2524- 2537, Sept. 2007.4/12/2013 © FZI Forschungszentrum Informatik 26
  27. 27. Reflective learning in TEL [1] R. Ferguson, S. B. Shum, and R. D. Crick. EnquiryBlogger: using widgets to support awareness and reflection in a PLE Setting. In ARPLE11, PLE Conference 2011, Southampton, UK, 11-13 July, 2011. [2] S. Govaerts, K. Verbert, E. Duval, and A. Pardo. The student activity meter for awareness and self-reflection. In CHI 12 Extended Abstracts on Human Factors in Computing Systems, pages 869-884, New York, NY, USA, 2012. ACM. [3] J. L. Santos, S. Govaerts, K. Verbert, and E. Duval. Goal-oriented visualizations of activity tracking: a case study with engineering students. In 2nd International Conference on Learning Analytics and Knowledge, LAK 12, pages 143-152, New York, NY, USA, 2012. ACM.4/12/2013 © FZI Forschungszentrum Informatik 27

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