Vital signs 2010 head camera conference paper

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Vital signs 2010 head camera conference paper

  1. 1. Nicola Beddall-Hill Informatics ESRC PhD candidate Attached to the TLRP TEL project Ensemble Making sense of (unusual) visual data: Dealing with the implications of using a head mounted camera to observe learning in the field Vital Signs 2: Engaging Research Imaginations, 7-9 September 2010, University of Manchester, Manchester UK Making sense of (unusual) visual data: Dealing with the implications of using a head mounted camera to observe learning in the field Vital Signs 2: Engaging Research Imaginations, 7-9 September 2010, University of Manchester, Manchester UK Supervisors: Prof. J., Raper; U., Patel; Prof. P., Carmichael & Prof. F., Webster
  2. 2. CONISTON FIELD TRIPS • April 2009 & 2010 with City University, as part of their MSc in Geographic Information Systems (GIS). This was based in the Lake District. • 6-12 students mixed gender, UK & international, varying age & experience • Two projects each lasted two days: brief, planning, data collection, analysis, presentation & assessment. • Devices used; Garmin Geckos, Trimble GeoXM & HP PDA • Observation via video indoors, head cam outdoors, photography, field notes, structured observation record, GPS trackers.
  3. 3. • Ethnographic observation creating mixed data over time • The data is the observation of the life cycle of a student project using technology to enhance learning • Lots of actors, actants, artifacts and boundary objects present • Need to start at the beginning to understand the outcomes • Mixed media taken simultaneously that requires joining up • 65GB of raw data!
  4. 4. Research Questions 1. What is ‘going on’ in the field trip setting? 1. How does the technology enable, change or create barriers to learning processes on field trips? 1. How can the role of technology in this setting be theorised? 1. What have we learnt? And how can that be given back to the educators, designers and students?
  5. 5. The head mounted Camera: option 1 POV1.5 Action Camera (POV1) • Fully integrated point-of-view (POV) video system (nearly to DVD quality). • Waterproof, dustproof & shock-resistant. • Mountable bullet camera with built-in recorder & wireless remote (3m). • Used a 4GB SDHC Card (takes up to 8) • Has its own editing software if required. • RRP £500 approximately
  6. 6. The head mounted Camera: option 2 Kodak Zx1 flip camera and Gorilla tripod • 720p HD video capture at 60fps, up to 10 hours record time (limited to 2hr by rechargeable batteries). • Weather resistant & rugged design. • Stills & video at variety of speeds in HD & VGA quality. • Mountable flip camera with internal memory (128MB) which is expandable with SDHC Cards (up to 32GB) • Has built-in software for editing videos. • Built-in mono microphone. • RRP £45 for camera & £12 for Gorilla tripod
  7. 7. Using head mounted Cameras – weighing up the pros and cons… Advantages Disadvantages Excellent sound & picture quality (DD) Delicate equipment-easily broken! Reduced capture of participant’s faces Creates a lot of footage to review- but can ‘tag’ sections (DD) Some are water others weatherproof (DD) Difficult to maintain the correct recording angle Focused recording of the mobile device & interactions around it Not clear what is happening on the mobile device’s screen Students are interrupted less by using this method Long record times, easy download (DD) Focus of attention/event is not wholly selected by the researcher DD = Device dependant
  8. 8. Issues that I have came across • Technical Which camera to use, various battery, file format and quality issues (picture and sound) • Participant Student embarrassment and lecturer discomfort at potential interference and maybe discomfort at lessons being filmed? • Ethical issues Strict anonymity enforced by the University committee. What is essential and what begins to erode the richness of the data?
  9. 9. QUESTIONS THAT NEED ADDRESSING • How to ‘convert’ what is captured digitally into a form that can be analyzed and disseminated? – there are similar issues with other forms of digital data, such as data feeds • What are suitable analysis techniques and software for this method? • How do you Interpret and disseminate your findings? And before we get this far… How to deal with the volume of data produced – hours and GB’s of data
  10. 10. Raw data Raw data Bento-files linked & labeled Bento-files linked & labeled When software fails! When software fails! Level 1Level 1 Level 2Level 2 Level 3Level 3
  11. 11. RAW DATARAW DATA DatabaseDatabaseTimeline Level 1: Organize raw data Level 2: Sort in database Level 3: Timeline critical events Level 4: Make critical events anonymous into Fedora Level 5: Critical events in ATLAS using theoretical concepts Level 6: Create a model Level 7: Test model
  12. 12. DatabaseDatabase RAW DATARAW DATA Timeline FEDORAFEDORA Level 1: Organize raw data Level 2: Sort in database Level 3: Timeline critical events Level 4: Make critical events anonymous into Fedora Level 5: Critical events in ATLAS using theoretical concepts Level 6: Create a model Level 7: Test model
  13. 13. RAW DATARAW DATA DatabaseDatabase ATLAS ti.6ATLAS ti.6Concepts to theorize Concepts to theorize Timeline FEDORAFEDORA Field trip test bed Field trip test bed Level 1: Organize raw data Level 2: Sort in database Level 3: Timeline critical events Level 4: Make critical events anonymous into Fedora Level 5: Critical events in ATLAS using theoretical concepts Level 6: Create a model Level 7: Test model
  14. 14. Analysis, interpretation and dissemination… • Where I am currently working…
  15. 15. ACKNOWLEDGEMENTS This work was carried out as part of an ESRC studentship at City University, linked to TLRP-TEL project Ensemble (http://www.ensemble.ac.uk/) The author would like to thank her supervisors Prof Jonathan Raper, Prof. Patrick Carmichael, Ms. Uma Patel, Prof. Frank Webster and her family for their support.
  16. 16. Email: nicola.beddall.1@city.ac.uk Twitter: http://twitter.com/citymobileangel Web: http://www.ensemble.ac.uk/ Nicola Beddall-Hill ESRC PhD candidate – Informatics Dept. TLRP TEL project Ensemble Learning with mobile devices in the field

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