Learning Analytics for
Learning Science When N = me
Phil Winne
Simon Fraser University
Expanding Scope about Learning in t...
2
 Professor of Educational Psychology at Simon
Fraser University
 Canada Research Chair in self-regulated
learning and le...
 Traditional learning science offers rather limited
support to me as a learner
 Learning analytics of big data can lever...
Traditional learning
science offers rather
limited support to me as a
learner
5
Randomized Controlled Trials (RCTs) studies
are often not replicable
Findings/implications not applicable to me
Odds ar...
Factor RCTs Online Learning
Content Limited
Crafted
Isolated
Potentially vast
“Wild”
Linked
Nuisance variables Controlled ...
Learning analytics of big
data can leverage learning
science for me
8
Defines, collects, analyzes
& reports data
about learners & learning contexts
to: understand learning
optimize learning
& ...
 Random assignment in classical RCTs yields an average not like
me
 Big data has 2 dimensions
 1. my studying over time...
How Can Learning Analytics Help?
 Gather big data about me (and my peers)
 Make producing data practically effortless
 ...
nStudy --- An online tool
for tracing and supporting
self-regulated learning in
the Internet
12
 A browser add-on for Firefox & Chrome
 Data is logged server side
 My curriculum = anything formatted as .html or .pdf...
1. Offer tools I can easily use to study
2. Gather data as I use tools in everyday, self-regulated
studying
 What informa...
nStudy Interface
Popup
menu
Side
bar
Learning
content
15
nStudy Popup Menu
Popup
menu
16
 Quoting
 Chat
 Filters
 Bookmarks
 Note templates
 …
Typical Features of nStudy
17
One Word Describes Operations for Learning:
SMART
Search I set standards that mark information for
another operation; then...
 Quotes (highlight + copy to nStudy workspace)
 1. metacognitively monitor
 2. plan to review
 Quote & annotate using ...
 Review an item
1. metacognitively monitor recall is deficient
2. plan to assemble information (pending later event)
 In...
 Student movement, activities on the website
nStudy Log Data
21
 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)...
 Counts of events
 Work flow, durations & intervals at various scales
 Tendencies
 Pr [IF|THEN]
 Effects
 Pr [IF|THE...
 IF  THEN events as graphs
 Sequences of traced events
Patterns for Me in n Study
24
Nudge me to monitordifferently:
 Recommend new (profile of) standards
 As I study, help me to identify
Information that...
Nudge me to viewdifferently:
Identify information I view related to
Information I viewed previously
Information I opera...
Nudge me to generateinformation
differently:
 Use my term net to suggest terms I can use
given:
Sources I’ve viewed
Ter...
nudge me to assembleinformation differently:
 Suggest items to assemble based on
Similarities & differences of their inf...
nudge me to rehearse(re-view an “old” item)
differently:
 Identify items based on
Temporal intervals
Patterns of operat...
 Traditional learning science offers rather limited
support to me as a learner
 Learning analytics of big data can lever...
 Video of the keynote speech available at
http://new.livestream.com/accounts/6514521/events/3
105335
Thank You
31
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  • Average (mean, SD) vs. me (individuals)
  • Phil Winne "Learning Analytics for Learning Science When N = me"

    1. 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. 2
    3. 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 3
    4. 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 4
    5. 5. Traditional learning science offers rather limited support to me as a learner 5
    6. 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 6
    7. 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 7
    8. 8. Learning analytics of big data can leverage learning science for me 8
    9. 9. Defines, collects, analyzes & reports data about learners & learning contexts to: understand learning optimize learning & improve learning environments Learning Analytics… 9
    10. 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 10
    11. 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 11
    12. 12. nStudy --- An online tool for tracing and supporting self-regulated learning in the Internet 12
    13. 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 13
    14. 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 14
    15. 15. nStudy Interface Popup menu Side bar Learning content 15
    16. 16. nStudy Popup Menu Popup menu 16
    17. 17.  Quoting  Chat  Filters  Bookmarks  Note templates  … Typical Features of nStudy 17
    18. 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. 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 19
    20. 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 20
    21. 21.  Student movement, activities on the website nStudy Log Data 21
    22. 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 22
    23. 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 23
    24. 24.  IF  THEN events as graphs  Sequences of traced events Patterns for Me in n Study 24
    25. 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… 25
    26. 26. Nudge me to viewdifferently: Identify information I view related to Information I viewed previously Information I operated on Providing Actionable Analytics to… 26
    27. 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… 27
    28. 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… 28
    29. 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… 29
    30. 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 30
    31. 31.  Video of the keynote speech available at http://new.livestream.com/accounts/6514521/events/3 105335 Thank You 31

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