Phil Winne "Learning Analytics for Learning Science When N = me"
1. Learning Analytics for
Learning Science When N = me
Phil Winne
Simon Fraser University
Expanding Scope about Learning in the Wild
Summarized by Gaowei Chen
Faculty of Education, HKU
July 4, 20141
3. Professor of Educational Psychology at Simon
Fraser University
Canada Research Chair in self-regulated
learning and learning technologies
Research interests include self-regulated
learning, metacognition, motivation, adaptive
software for researching and promoting self-
regulated learning
About the Keynote Speaker-
Phil Winne
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4. Traditional learning science offers rather limited
support to me as a learner
Learning analytics of big data can leverage learning
science for me
nStudy --- An online tool for tracing and supporting
self-regulated learning in the Internet
Outline
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6. Randomized Controlled Trials (RCTs) studies
are often not replicable
Findings/implications not applicable to me
Odds are small I can benefit from RCTs
Limit of Traditional Learning Science
When N = me
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7. Factor RCTs Online Learning
Content Limited
Crafted
Isolated
Potentially vast
“Wild”
Linked
Nuisance variables Controlled Haphazard
Treatment Imposed
Unvarying
Largely absent
Irregular
Learning episodes Brief
Single or no review
Longer
Free ranging review
Significance of
content
Nil or trivial Self chosen
Consequences for me Nil or trivial It depends
Randomized Controlled Trials (RCTs)
vs. Online Learning
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9. Defines, collects, analyzes
& reports data
about learners & learning contexts
to: understand learning
optimize learning
& improve learning environments
Learning Analytics…
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10. Random assignment in classical RCTs yields an average not like
me
Big data has 2 dimensions
1. my studying over time
2. studying of 105 ± 103 others
some are just like me
Big N clustering of data about learning-as-process can
construct post hoc a homogeneous “population” of learners
whose moderator variables match mine
Big Data is Essential
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11. How Can Learning Analytics Help?
Gather big data about me (and my peers)
Make producing data practically effortless
Feed me analytics that help me track & adapt
Information I select to study
Operations I carry out to study
What I learn
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12. nStudy --- An online tool
for tracing and supporting
self-regulated learning in
the Internet
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13. A browser add-on for Firefox & Chrome
Data is logged server side
My curriculum = anything formatted as .html or .pdf
The full internet is my library
Each learner self-regulates learning
Information viewed / reviewed
Pace
Selective tools I apply to particular information
nStudy’s Key Attributes
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14. 1. Offer tools I can easily use to study
2. Gather data as I use tools in everyday, self-regulated
studying
What information do I generate, view, share?
What operations do I apply to each information?
Dual-Purpose Learning Systems
To paint as full a picture as possible of how I study
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17. Quoting
Chat
Filters
Bookmarks
Note templates
…
Typical Features of nStudy
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18. One Word Describes Operations for Learning:
SMART
Search I set standards that mark information for
another operation; then I seek
Monitor I compare information items by features
Assemble I add a relational features to join information
in two or more nStudy items
Rehearse I reinstate information
Translate I reformat information in a way that
• (mostly) preserves meaning
• affords some new meaning(s) because
the representation is different18
19. Quotes (highlight + copy to nStudy workspace)
1. metacognitively monitor
2. plan to review
Quote & annotate using a note template
1. metacognitively monitor for matching to a schema
2. assemble source information using a schema
Copy & paste
1. monitor knowledge
2. assemble information copied with information at the destination of
the paste
A Gallery of Traces & What They
Model
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20. Review an item
1. metacognitively monitor recall is deficient
2. plan to assemble information (pending later event)
Include one item as child of another
1. rehearse (at least some of ) the items to be included
2. assemble an item into a category titled by the parent (a
quote, folder, chat, note, document)
Search for information
1. enter search query with/without constraints
A Gallery of Traces & What They
Model
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22. What do I want to know about me?
What can nStudy tell me that I don’t know?
If I know that, what can I do (about it)?
Learning Analytics Reports for Me
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23. Counts of events
Work flow, durations & intervals at various scales
Tendencies
Pr [IF|THEN]
Effects
Pr [IF|THEN result]
Basic Statistics for Me in nStudy
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24. IF THEN events as graphs
Sequences of traced events
Patterns for Me in n Study
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25. Nudge me to monitordifferently:
Recommend new (profile of) standards
As I study, help me to identify
Information that fits standards
or
A pattern of events that fits new standards
Providing Actionable Analytics to…
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26. Nudge me to viewdifferently:
Identify information I view related to
Information I viewed previously
Information I operated on
Providing Actionable Analytics to…
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27. Nudge me to generateinformation
differently:
Use my term net to suggest terms I can use
given:
Sources I’ve viewed
Terms I’ve used / not used in quotes, notes,
chats
Providing Actionable Analytics to…
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28. nudge me to assembleinformation differently:
Suggest items to assemble based on
Similarities & differences of their information
Features of termnet neighborhoods for terms I
include in items’ text
Recommend using a note template
Providing Actionable Analytics to…
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29. nudge me to rehearse(re-view an “old” item)
differently:
Identify items based on
Temporal intervals
Patterns of operations applied to information
within those intervals
Providing Actionable Analytics to…
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30. Traditional learning science offers rather limited
support to me as a learner
Learning analytics of big data can leverage learning
science for me
nStudy --- An online tool for tracing and supporting
self-regulated learning in the Internet
Summary
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31. Video of the keynote speech available at
http://new.livestream.com/accounts/6514521/events/3
105335
Thank You
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