live interest meter - learning from quantified feedback in mass lectures
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Live Interest Meter - Learning from Quantified
Feedback in Mass Lectures
V. Rivera-Pelayo, J. Munk, V. Zacharias, and S. Braun
FZI Research Center for Information Technology, Karlsruhe, Germany
LAK Conference 2013 – Leuven, Belgium
10th April 2013
Agenda
Introduction
Use Case and Motivation
Live Interest Meter App
Tests and User Study
Conclusions
Ongoing Work
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Use Case and Motivation
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Holding a good
presentation is hard!
Use Case and Motivation
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How to get better at it?
• With practice
• Get feedback
• Learn from the feedback! Holding a good
presentation is hard!
Use Case and Motivation
LA for workplace learning
Capturing of feedback for reflective learning
Inspired by Quantified Self approaches
Mass lectures, virtual courses and conferences
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Use Case and Motivation
LA for workplace learning
Capturing of feedback for reflective learning
Inspired by Quantified Self approaches
Mass lectures, virtual courses and conferences
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Live Interest meter - LIM App
Gathering, aggregation and visualization of feedback
Protagonist: the meter
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General
performance
Speech speed
Comprehension
LIM App: Evolution graph
Available for Android and for Browsers (JavaScript)
Browser to facilitate the configuration of the presenter
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LIM App: Polls and Questions
Polling and anonymous rated questions
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LIM App: JavaScript Version
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Experimental tests
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(1) Project meeting
10 participants used LIM
Discussion driven
Experimental tests
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(1) Project meeting
10 participants used LIM
Discussion driven
(2) Lecture at university
15 participants
Interactive session
Technical results very satisfactory
Acceptance results rather limited
Experimental tests
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Project meeting
10 participants used LIM
Discussion driven
Lecture at university
15 participants
Interactive session
Technical results very satisfactory
Acceptance results rather limited
Suitable for big audiences Presenter‘s role well defined
User Study
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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?
User Study
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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
87 valid responses
Presenter 44,82% Audience
55,18%
User Study – Reaction to feedback
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What do you consider realistic regarding
how you can react to the feedback (as presenter)?
User Study – Data collection
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Data collected live and continuously is
better and more significant than only periodically collected data.
64,37 %
User Study – Information needed
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Which information would the presenters
like to know about and which information
does the audience want to evaluate?
User Study – Characteristics of the LIM App
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How participants evaluated the LIM App features
Conclusion
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 process
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78,16 % found the idea behind
the LIM App very positive
57,47 % would like to test the
LIM App
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 features
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THANK YOU!
Any questions?
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rivera@fzi.de
@veronicarp
vriverapelayo
Theoretical Model & Related Work
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Theoretical background
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[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, Valentin
Zacharias, Lars Müller, Simone Braun. IADIS International Conference on Mobile Learning (Mlearning 2012), Berlin, Germany
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 lecture
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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.
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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.
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