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

2. 2

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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5. Traditional learning
science offers rather
limited support to me as a
learner
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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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8. Learning analytics of big
data can leverage learning
science for me
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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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15. nStudy Interface
Popup
menu
Side
bar
Learning
content
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16. nStudy Popup Menu
Popup
menu
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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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21.  Student movement, activities on the website
nStudy Log Data
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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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