Your SlideShare is downloading. ×
Phil Winne "Learning Analytics for Learning Science When N = me"
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Phil Winne "Learning Analytics for Learning Science When N = me"

252

Published on

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
252
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • Average (mean, SD) vs. me (individuals)
  • Transcript

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

    ×