This document discusses the synergies between learning analytics and learning design. Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Learning design is the intentional design of learning experiences to achieve specific educational goals. When combined, learning analytics and learning design can provide insights to continuously improve learning environments and make them more data-informed.

1. Creating
Data-Informed
Learning
Environments
Synergies for
Learning Analytics
& Learning Design
Alyssa Wise
Ed | Tech | Learn | Design
Simon Fraser University
Image Credit: Billie Grace Ward’s Trammell's LED matrix cube via Flickr (CC BY 2.0), adapted

2. LEARNING
ANALYTICS

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4. Sub UniquePostsRead()
For k = 1 To MaxUser Step 1
RowCount = Range("A1").CurrentRegion.Rows.Count
For w = 1 to MaxWeek Step 1
StartTime = Sheets("Week").Cells(w + 1, 2)
EndTime = Sheets("Week").Cells(w + 1, 3)
PostNum = 0
PostsIndex = 0
Do While Cells(i, datestamp) <= EndTime And i <= RowCount
If Cells(i, Source) = “Read" Then
If Cells(i, Message_Author) <> Val(ActiveSheet.Name)
And Cells(i, Scan) <> "X" Then
flag = 0
For j = 1 To PostsIndex Step 1
If Posts(j) = Cells(i, Message_Id) Then
flag = 1
j = PostsIndex
End If
Next j
If flag = 0 Then
PostsIndex = PostsIndex + 1
Posts(PostsIndex) = Cells(i, Message_Id)
End If
End If
End If
Sheets(“Stats").Cells(Line, 22) = PostsIndex
Next w
Next k
End Sub
PercentPostsRead =SUniquePostsRead
TotalPostNumber

6. Image Credit: Joshua Rothass via Flickr (CC BY 2.0), adapted

7. Image Credit: US Department of Education via Flickr (CC BY 2.0), adapted

8. Image Credit: MoDOT Photos via Flickr (CC BY 2.0)

9. THE COLLECTION AND
ANALYSIS OF DATA
TRACES RELATED TO
LEARNING IN ORDER TO
INFORM AND IMPROVE
THE PROCESS AND/OR
ITS OUTCOMES
( S I E M E N S E T A L . , 2 0 1 1 )
LEARNING
ANALYTICS

10. “[LEARNING] ANALYTICS
EXIST AS PART OF A SOCIO-
TECHNICAL SYSTEM WHERE
HUMAN DECISION-MAKING
AND CONSEQUENT ACTIONS
ARE AS MUCH A PART OF
ANY SUCCESSFUL ANALYTICS
SOLUTION AS THE
TECHNICAL COMPONENTS”
V A N H A R M E L E N & W O R K M A N ( 2 0 1 2 )
LEARNING
ANALYTICS

11. Image Credit: Christopher Sessums via Flickr (CC BY 2.0), adapted

12. Image Credit: Christopher Sessums via Flickr (CC BY 2.0), adapted

13. I T ’ S A L S O N O T J U S T
A B O U T IF L E A R N I N G
A N A LY T I C S A R E U S E D
B U T HOW T H E Y A R E
U S E D T H AT M AT T E R S ...

14. W H AT
OPPORTUNITY
D O YO U S E E ?
W H AT PROBLEM
A R E YO U T R Y I N G
T O S O LV E ?

15. L E A R N I N G A N A LY T I C S &
L E A R N I N G D E S I G N S Y N E R G Y
# 1
COLLECT
SMARTER
DATA

20. DATA MINING
Image Credit: Scott Clark via Flickr (CC BY 2.0), adapted

21. DATA GEOLOGY
Image Credit: APS Museum via Flickr (CC BY 2.0), adapted
( S H A F F E R , 2 0 1 3 )

22. DATA ARCHEOLOGY
Image Credit: Pedro Szekely via Flickr (CC BY 2.0), adapted
( W I S E , 2 0 1 4 )

23. L E A R N I N G A N A LY T I C S &
L E A R N I N G D E S I G N S Y N E R G Y
# 2
IDENTIFY CRITICAL
PAT TERNS

25. StatMed’13
StatMed’14
StatLearn
YBW
PSY
Course Subject Learning Process Question Words Connectors Existence/Condition Course TasksQuality/Quantity Effort / Action People Appreciation/Greeting
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Content - Related
NonContent - Related
Numberoffeatures Content-Related
Discussion Posts
Non-Content-Related
Discussion Posts

26. StatMed’13
StatMed’14
StatLearn
YBW
PSY
Course Subject Learning Process Question Words Connectors Existence/Condition Course TasksQuality/Quantity Effort / Action People Appreciation/Greeting
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Content - Related
NonContent - Related
Numberoffeatures Content-Related
Discussion Posts
Non-Content-Related
Discussion Posts
E.g. “can” “does” “why”
“how” “which “and”
“of” “than” “is”
E.g. “answer” “exam”
“course” “lecture”
“thank” “good” “I” “my”

27. Adding a linguistic filter to the discussion forums would
let students and instructors focus on only content-related
discussion when they wanted to.
This could reduce the number of threads to look at by
HALF and increase the hit rate from ~40% to 80%.

28. L E A R N I N G A N A LY T I C S &
L E A R N I N G D E S I G N S Y N E R G Y
# 3
CHECK
ASSUMPTIONS

29. E X P E C T E D I N T E R A C T I V E
D I S C U S S I O N P A T T E R N
( B A S E O N B L E N D E D C L A S S )
A C T U A L D I S C U S S I O N
P A T T E R N F O R F U L L Y
O N L I N E C O U R S E
F R O M B R O O K S , G R E E R & G U T W I N ( 2 0 1 4 ) T H E D A T A - A S S I S T E D A P P R O A C H T O
B U I L D I N G I N T E L L I G E N T T E C H N O L O G Y E N H A N C E D L E A R N I N G E N V I R O N M E N T S .

30. Image Credit: Pedro Figueiredo via Flickr (CC BY 2.0), adapted
O N E S I Z E
D O E S N ’ T
F I T A L L

31. MORE
IS
BETTER
?
Image Credit: Torley via Flickr (CC BY 2.0)

32. L E A R N I N G A N A LY T I C S &
L E A R N I N G D E S I G N S Y N E R G Y
# 4
GUIDE
INSTRUCTOR
INQUIRY

33. Image Credit: Nicolas Raymond’s Grunge Warning Sign via Flickr (CC BY 2.0), adapted
FROM ANALYTICS-DRIVEN INTERVENTION
TO ANALYTICS-INFORMED IMPROVEMENTS

34. FROM ANALYTICS-DRIVEN INTERVENTION
A
A’
A A’’
POINT-IN-TIME
INTERRUPTIONS TO
ADDRESS PROBLEMS
PRODUCTIVE ONGOING
ADJUSTMENTS TO
TEACHING & LEARNING
TO ANALYTICS-INFORMED IMPROVEMENTS

35. Starburst
A Graphical Discussion Forum with
Embedded Analytics

36. What are your goals for this activity?
What data would help you know if they are being achieved?

37. L E A R N I N G A N A LY T I C S &
L E A R N I N G D E S I G N S Y N E R G Y
# 5
SUPPORT STUDENT
SELF-REGULATION

38. Starburst
A Graphical Discussion Forum with
Embedded Analytics

39. Starburst
A Graphical Discussion Forum with
Embedded & Extracted Analytics
Metric Your Data
(Week X)
Class
Average
(Week X)
% of posts read 72% 87%
% of real reads
41% 66%
Av. length of
real reads
2.37m 4.12m
#of reviews of
own posts
22 13
#of reviews of
others’ posts
8 112

40. Learning Analytics
&
Learning Activities
Grounding
Goal Setting
Reflection

41. INTEGRATING STUDENT USE OF
ANALYTICS AS PART OF
LEARNING PRACTICES IN A
PRINCIPLED WAY OFFERS EXCITING
OPPORTUNITIES TO HELP
STUDENTS BECOME
PURPOSEFUL ABOUT THEIR
LEARNING BASED ON DATA-
INFORMED DECISIONS

42. S U M M A R Y O F ( J U S T S O M E )
S Y N E R G I E S B E T W E E N L E A R N I N G
A N A L Y T I C S & L E A R N I N G D E S I G N
- C O L L E C T S M A R T E R D A T A
- C H E C K A S S U M P T I O N S
- I D E N T I F Y C R I T I C A L P A T T E R N S
- G U I D E I N S T R U C T O R I N Q U I R Y
- S T U D E N T S E L F - R E G U L A T I O N

43. S O M E T H I N G S T O
K E E P I N M I N D
C H A L L E N G E S O F B O T H
I N T E R P R E TA T I O N & A C T I O N
P R I N C I P L E S O F
C O O R D I N A T I O N , C O M P A R I S O N
& C U S T O M I Z A T I O N

44. F O R F U R T H E R R E A D I N G
Wise, A. F. & & Vysatek, J. M. (in review). Learning analytics implementation
design. Handbook of learning analytics and educational data mining.
Brooks, C., Greer, J. & Gutwin, C. (2014). The data-assisted approach to building
intelligent technology enhanced learning environments. In J. Larusson & B. White
Eds. Learning analytics: From research to practice (pp123-156). NY: Springer.
Marbouti, F., & Wise, A. F. (2016). Starburst: a new graphical interface to support
purposeful attention to others’ posts in online discussions. Educational Technology
Research and Development, 64(1), 87-113.
Winne, P. H., & Hadwin, A. F. (2013). nStudy: Tracing and supporting self-regulated
learning in the Internet. In The international handbook of metacognition and learning
technologies (pp. 293-308). Springer New York.
Wise, A. F., Cui, Y. & Vysatek, J. M. (in press). Bringing order to chaos in MOOC
discussion forums with content-related thread identification. To appear in the
Proceeding of the International Conference on Learning Analytics and Knowledge.

45. E V E N F U R T H E R R E A D I N G
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action:
Aligning learning analytics with learning design. American Behavioral Scientist,
57(10), 1439-1459.
Persico, D., & Pozzi, F. (2015). Informing learning design with learning analytics to
improve teacher inquiry. British Journal of Educational Technology, 46(2), 230-248.
van Leeuwen, A. (2015). Learning analytics to support teachers during synchronous
CSCL: balancing between overview and overload. Journal of Learning Analytics, 2(2),
138-162.
Wise, A. F. Vysatek, J. M., Hausknecht, S. N. & Zhao, Y. (in press). Developing learning
analytics design knowledge in the “middle space”: The student tuning model and
align design framework for learning analytics use. To appear in Online Learning.
Wise, A. F., Zhao, Y. & Hausknecht, S. N. (2014). Learning analytics for online
discussions: Embedded and extracted approaches. Journal of Learning Analytics,
1(2), 48-71.

46. Creating
Data-Informed
Learning
Environments
Synergies for
Learning Analytics
& Learning Design
Alyssa Wise
Ed | Tech | Learn | Design
Simon Fraser University
Image Credit: Billie Grace Ward’s Trammell's LED matrix cube via Flickr (CC BY 2.0), adapted