what if we could observe all events in a learning environment?
DESCRIPTION
If we could observe all the events in a learning environment, even the most hidden ones, what kind of interventions would become feasible? The presentation finishes with a workshop to manipulate a dataset with R.TRANSCRIPT
What if we could observeall events in alearning environment?
MUI-TIC, Valladolid, April 2012
Abelardo Pardo
www.slideshare.net/abelardo_pardo
@abelardopardo
Auntie
pflick
r.com
Journey to 10 years from now
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libra
rym
an
flick
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ribarn
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flick
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Analyticstoday
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http
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Is she pregnant?
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http
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Analytics everywhere!
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The
Wall
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etJourn
al,
Feb.
22nd
2012
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lave
rrue
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r.com
No (unwanted) surprises!
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http
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Medicalrecords
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Reig
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C.E
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(Campbell, De Blois, Oblinger 2007)
Collect
Report
Predict
Act
Refine
The five stepsof analytics
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ribarn
ica
flick
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Analyticstoday
Potentialinlearning
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Shaylo
rflick
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Did they learn?
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Joshua
Rappeneke
rflick
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Bik
ing
Niko
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flick
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If you can not measure it,you can not improve it.
bencrowe flickr.com
“We need better evaluationto acknowledge and esteem
excellence when it occurs,as it does.”
John Hattie. Visible Learning: A Synthesis of Over 800Meta-Analyses Relating to Achievement.
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bodhith
ajflick
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Learning analytics improvethe feedback loop
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bodhith
ajflick
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(Jones, 2011)
Administration Students
Teachers Researchers
Marketingfacts flickr.com
Where is learning analytics?
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PranceyDog flickr.com
LA as vertical market
Specialized objectives
Specialized audiences
Specialized topic
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www.solaresearch.orgAbelardo Pardo What if we could observe all events in a learning environment? 22
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ribarn
ica
flick
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Analyticstoday
Potentialinlearning
Challenges
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ljubar
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More detailed observations
Keep the tutor in the loop
A clearly identifiedenvironment within
your computer
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Pepe
Ortu
ño
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Automatic discoveryof complementarylearning resources
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Sean
Dre
ilinger
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Measure engagement inout-of-class activities
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IAN
RA
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ION
flick
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Just in time teaching
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apeelflick
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jfrave
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Team dynamics
Quick intuitivefeedback
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Stakeholders observe, predict and act
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How are you?
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Joe
Shalo
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Moodchanges
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::cala
vera
café
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r.com
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purp
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llflick
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Dru
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ScenarioData setmanipulation
Interventions!
• 15 weeks. Sessions X, F 12-13:30
• 100 students
• 7 score tests per student
• Course notes with 30 “nodes” publishedgradually
• 5 events: forum_view, forum_post,visit_node, use_tool_1, use_tool_2
Make it your own course!
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ktflick
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Events (events.csv.gz): around 81,000 x 4 matrix
datetime,event_type,user,payload
2011-09-05 15:26:01,use_tool_1,7a869b4d9,block_1
...
Scores (scores.csv.gz): 100 x 7 matrix
user,score_1,score_2,score_3,score_4,...,score_7
7a869b4d9, 6.4, 9.0, 7.9, 7.6, 5.1, 5.5, 6.7
...
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jovike
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Let’s explore!
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cesara
stu
dillo
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Analyze, propose, discuss
theunquietlibrarian flickr.com
1 What did you observe?
2 Which data is missing?
3 What would you do?
4 What would the system do?
Which challenge for LA do you envision?
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script.R
1 Load data into “data frames”
2 Show data summaries
3 Change type of column in data frame
4 View sample histogram
5 Add a column with the mean scores
6 Plot scores for one student
7 Separate events into types
8 Merge event counts with scores
Select command + CRTL-Return (Execute)
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para
l_la
xflick
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R cheat sheet
• Data frame: heterogeneous matrix,“events.csv”
• Data frame column: events.csv$event_type
• Arrays: c(’one’, ’two’, ’three’), c(1, 2, 3),c(2:8)
• Sequences: 1:5, seq(1, 6)
• Graphics: barplot(), boxplot(), hist(), plot()
• Functions: length(), rownames(),colnames(), mean()
• cor() (correlation), var() (variance)
• mean(), median(), min(), max(), sum()
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Cals
idyro
se
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R tests
• cor.test(v1, v2)
• wilcox.test(v1, v2)
• shapiro.test(v1)
R help
• help(any function name)
• Various cheatsheets included in workspace(cheatsheet_*.pdf)
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Cals
idyro
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flick
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References
The personal analytics of my life
Stephen Wolframhttp://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/
How waiters read your table
Sara NassauerThe Wall Street Journal, Feb. 22nd 2012
The illusion of privacy (and what we actually care about)
Seth Godinhttp://sethgodin.typepad.com/seths_blog/2012/02/the-illusion-of-privacy-and-what-we-actually-care-about.html
Academic Analytics
Campbell J., DeBlois P., Oblinger D.EDUCAUSE White paper, 2007
Visible learning: A synthesis of over 800 meta-analyses related to achievement
Hattie, J. A. C.Routledge, New York 2009
Learning analytics: Starvation and telling us what we already know?
David Joneshttp://davidtjones.wordpress.com/2012/04/01/learning-analytics-starvation-and-telling-us-what-we-already-know/
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