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Data Protocol: The Collaborative Learning Cycle, Slides of Learning processes

University of California - Los Angeles (UCLA)Learning processes

Data Protocol: The Collaborative Learning Cycle. ACTIVATING & ENGAGING ... Identify additional data needed to confirm (or not) possible theories.

Typology: Slides

2022/2023

Uploaded on 03/01/2023

danmarino
danmarino 🇺🇸

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Data Protocol: The Collaborative Learning Cycle
ACTIVATING(&(ENGAGING(
•
Teams&make&predictions&prior&to&seeing&any&data&
•
Use&blank&versions&of&data&charts&they&will&see&
•
Make&and&record&predictions&
•
Record&related&assumptions&
o
Purpose&is&to&surface&and&understand&assumptions&(not&about&
right/wrong)&
o
Prediction:&something&you&expect&to&see&in&the&data&
o
Assumption:&something&that&you&think&but&that&will&not&show&up&in&
data&
EXPLORING(&(DISCOVERING(
▪
Visually&vibrant&data&displays&
▪
Encourage&all&voices&
▪
Data&experts&act&as&resources&
▪
Data&shy&encouraged&to&contribute&
▪
Individual&observations&are&publicly&charted&
▪
Stick&with&objective&“refined”&observations&(data&narrative&
statements)&
▪
Provide&wait&time,&ensuring&all&observations&are&charted&
ORGANIZING(&(INTEGRATING(
•
Select&two&observations&to&explore&further&
•
Generate&multiple&causation&theories&(why?)&
o
Stay&focused&on&things&that&we&can&control&
o
Generate&theories&from&MORE&than&one&category&
o
Identify&additional&data&needed&
•
Identify&additional&data&needed&to&confirm&(or&not)&possible&theories&
•
What&might&be&a&possible&action-step(s)/action-plan?&
Adapted&from:&
Got$Data?$Now$What?$
(Lipton&&&Wellman,&2012)
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Data Protocol: The Collaborative Learning Cycle

ACTIVATING & ENGAGING

  • Teams make predictions prior to seeing any data
  • Use blank versions of data charts they will see
  • Make and record predictions
  • Record related assumptions o Purpose is to surface and understand assumptions (not about right/wrong) o Prediction: something you expect to see in the data o Assumption: something that you think but that will not show up in data EXPLORING & DISCOVERING ▪ Visually vibrant data displays ▪ Encourage all voices ▪ Data experts act as resources ▪ Data shy encouraged to contribute ▪ Individual observations are publicly charted ▪ Stick with objective “refined” observations (data narrative statements) ▪ Provide wait time, ensuring all observations are charted ORGANIZING & INTEGRATING
  • Select two observations to explore further
  • Generate multiple causation theories (why?) o Stay focused on things that we can control o Generate theories from MORE than one category o Identify additional data needed
  • Identify additional data needed to confirm (or not) possible theories
  • What might be a possible action-step(s)/action-plan? Adapted from: Got Data? Now What? (Lipton & Wellman, 2012)