SoLAR-FlareUK-2012.11.19-breakouts

2,603 views
2,548 views

Published on

http://www.solaresearch.org/flare/solar-flare-uk/

Published in: Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
2,603
On SlideShare
0
From Embeds
0
Number of Embeds
1,102
Actions
Shares
0
Downloads
8
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

SoLAR-FlareUK-2012.11.19-breakouts

  1. 1. Breakout   groups  feedback  
  2. 2. Reten%on  and  success  •  Reten%on  and  success  are  dis%nct,  but  linked.   Qualita%ve  vs  binary.  •  Applica%ons:  quick/early  drop-­‐out,  adapa%ve   learning.  •  Ethical  issues.  •  Media%ng  feedback,  using  analy%cs  to  present   the  model  with  the  ra%onale,  used  as  the   basis  for  a  personalised  conversa%on.   Photo  (CC)  Trey  Ratcliff  hJp://www.flickr.com/photos/stuckincustoms/4622806283/  
  3. 3. Mul%ple  Purposes   • Aggrega%on   Ethics   • Interven%on   •  Emo%ons   • Mo%va%on   • Informed  decision  making   •  Anxiety   • ‘De-­‐ •  Surveillance   modularisa%on’  (holis%c   •  Privacy   informa%on)   • Ipsa%ve  vs  norm   •  Transparency   informa%on   Opera%onalisa%on  Mul%ple  audiences   • Selec%ng  data  sets  •  Different  purposes   • Timeliness  and  efficacy  •  Same  data  sets   • evalua%on   • Granularity  •  Interpreta%on  and   • Interac%vity   clarity   • Proprietary  tool  providers   •  Training  and  sense   making   Dashboards   preemp%ng  our  needs/ wants   • Pedagogically  drivers  
  4. 4. Dashboard  Examples   Student   •  How  am  I  doing  compared  to  cohort?   Tutor   •  Is  what  I’m  doing  with  my  students  working?   Ins%tu%on   •  Which  students  are  most  likely  to  drop  out?   PSRB   •  Are  any  students  gradua%ng  from  this  ins%tu%on   without  all  of  the  required  learning  outcomes?  Researchers   •  Across  the  sector  which  ins%tu%ons  produce  the   best  graduates  in  each  discipline?  
  5. 5. Analy5cs  for  Student  Success  &  Reten5on:  Issues   Pre-­‐fail   Dangers  of  a  Pre-­‐Crime  Unit   Ethics  of  interven5on:     Just  for  those  who  are  failing?   What  about  the  rest?   Beware  self-­‐fulfilling  failure  prophecies!   “Dear  <field1>…”   Beware  back-­‐firing  personalisa%on  expecta%ons:  “So  I  really  am  just  a  number”  Informed  interven%ons  hopefully  changing  learners’  futures  for  the  beJer…  But  what  does  that  do  for  datasets  and  historical  comparison?  Important  to  collect  data  about  interven%ons  to  assess  their  impact  amongst  other  variables     Beware:  can’t  count,  doesn’t  count:  we’re  in  a  complex  people  business!  
  6. 6. Pedagogy  &  LA  
  7. 7. Issues  •  How  do  we  measure  learning  (rather  than  ‘success’  in   assessments)  •  Approximate  proxies  for  learning…  •  Shouldn’t  assessment  be  our  ‘best  measure’  of  learning  –   well,  perhaps  it  should  be  a  suite  of  analy%cs  •  What  ‘knowledge’  do  we  want  from  our  graduates  •  ‘Recipe’  issue  of  LA?  –  so  we  have  to  make  sure  we’re   looking  for  the  ‘right’  processes  •  Assessment/analy%cs:  Snapshots,  con%nuity,  and  change   metrics;  how  can  they  be  used?  •  Analy%cs  driven  by  what  we  want  to  achieve  rather  than   what  data  is  available  
  8. 8. Examples  •  Dialogue  analysis,  perhaps  analysis  of  use  of   social  networks  •  LA  as  pedagogy  v  LA  for  pedagogy  –  LA  which   feeds  back  in  to  ‘improving’/adap%ng.  LA  can   help  us  challenge  our  assump%ons  about  how  the   learning  is  taking  place.    Can  LA  allow  us  to   hypothesis  test  our  (as  teachers)  assump%ons   about  learning?  •  Pass  rate  and  online  ac%vity  has  a  correla%on  –   effec%ve  ‘proxy’?  
  9. 9. Data  sources  
  10. 10. Issues  •  Availability   •  Awareness  of  data  •  Quality   collec%on  •  Enrich  (combining  data)   •  Sharing  (ethics,  •  Private     commercially  sensi%ve)   •  Infrastructure  •  Paying  to  access  your  own   data   •  Planning  in  rapidly  evolving  •  Need?   area  (itera%ons)   •  Granularity  (nano)  •  Data  ownership  •  Not  everything  is  online  –   •  Purpose   no  footprint  (overall   •  Culture  change   visibility  of  interac%ons)  •  Volume  
  11. 11. Examples  •  TINCAN  API  •  IBM  –  (data  don’t  ask,  don’t  get)  •  midata  

×