Learning Analytics: what are we optimizing for?

edfuture.net MOOC on Current/Future State of HigherEd

Learning Analytics
what are we optimizing for? Knowledge Media Institute

Simon Buckingham Shum
Knowledge Media Institute
The Open University UK
http://twitter.com/sbskmi
simon.buckinghamshum.net @

1

edX: “this is big data, giving us the chance
to ask big questions about learning”

Will the tomorrow’s
educational researcher be
as helpless without an
analytics infrastructure, as
a geneticist without
genome databases, or a
physicist without CERN? 2

the plan…

joined-up multi-layer analytics
an analytics ecosystem
are analytics (r)evolutionary?

3

the convergence of
analytics layers

4

Macro/Meso/Micro Learning Analytics

Macro:
region/state/national/international

Meso:
institution-wide

Micro:
individual user actions
(and hence cohort)

Macro/Meso/Micro Learning Analytics

Macro:

Meso:
institution-wide

Micro:
(and hence cohort)

Will institutions be dazzled by the
dashboards, or know what
questions to ask at each level?

For examples of each level of analytic…

Buckingham Shum, S. 2012. Our Learning Analytics are Our Pedagogy. Keynote Address, Expanding Horizons 2012 Conference, 7
Macquarie University, Sydney. http://www.slideshare.net/sbs/our-learning-analytics-are-our-pedagogy

The VLE—BI—ITS convergence

8

As data migrates up it enriches higher
layers, normally accustomed to sparse data

Macro:

Meso:
institution-wide

Micro:
(and hence cohort)

Aggregation of user traces
enriches meso + macro analytics
with finer-grained process data

…which in turn could enrich lower layers
— local patterns can be cross-validated

Macro:

Meso:
institution-wide

Micro:
(and hence cohort)

Aggregation of user traces Breadth + depth from macro
enriches meso + macro analytics + meso levels could add
with finer-grained process data power to micro-analytics

anatomy of an
analytics ecosystem

11

A learning analytics ecosystem

learners

educators

12


learners

educators

13


learners

?!*?!*

?!*?!* educators

14


data capture
design team
dashboard
design team
learners

?!*?!*
data curators/
translators

?!*?!* educators

15

Where did the data come from?

learners

16


learners

theories
pedagogies
assessments
tools

researchers / educators / instructional designers 17


learners
technologists
theories
pedagogies
assessments
tools


The map is not the territory
Analytics are not the end, but a means
The goal is to optimize the whole system

outcome

feedback
learners
design

Intent theories
Data
pedagogies
assessments
tools

intent


Optimize the system
for what?

20

Same outcomes,
but higher scores?
Learning Analytics as
Evolutionary Technology

• more engaging
• better assessed
• better outcomes
• deliverable at scale

21

New outcomes we
couldn’t assess before?
Learning Analytics as
Revolutionary Technology

• learner behaviours quantifiable
• interpersonal networks quantifiable
• discourse quantifiable
• moods and dispositions quantifiable

22

Learning analytics for this?

“We are preparing students for jobs
that do not exist yet, that will use
technologies that have not been
invented yet, in order to solve
problems that are not even
problems yet.”

“Shift Happens”
http://shifthappens.wikispaces.com

23


“While employers continue to demand high academic
standards, they also now want more. They want
people who can adapt, see connections,
innovate, communicate and work with
others. This is true in many areas of work. The new
knowledge-based economies in particular will
increasingly depend on these abilities. Many
businesses are paying for courses to promote creative
abilities, to teach the skills
and attitudes that
are now essential for economic
success…”
All our Futures: Creativity, culture & education, May 1999 24


Think about the analytics
products and initiatives
reviewed above – where
would you locate them
on these dimensions?

Creativity, Culture and
Education (2009)
Changing Young Lives
2012. Newcastle: CCE.
http://www.creativitycultureeducation.org/
changing-young-lives-2012 25


The Knowledge-Agency Window
co-generation

Expert-led enquiry Student-led enquiry
Knowledge

and use

Teaching as
Authenticity
learning design
Agency
Identity

Repetition,
Pre-scribed
Knowledge

Abstraction
Acquisition

Expert-led teaching Student-led revision

Teacher agency Student agency

Ruth Deakin Crick, Univ. Bristol, Centre for Systems Learning & Leadership
“Pedagogy of Hope”: http://learningemergence.net/2012/09/21/pedagogy-of-hope

analytics grounded in the
principles of good
assessment
for learning?
(not summative assessment for
grading pupils, teachers,
institutions or nations)

27

Assessment for Learning Few learning analytics are
http://assessment-reform-group.org currently able to take o
board the richness of this
original conception of
assessment for learning

28

Assessment for Learning
http://assessment-reform-group.org

29


To what extent
could automated
feedback be
designed and
evaluated with
emotional impact
in mind?

30


Can analytics
identify proxies
for such
advanced
qualities? 31


How do we provide
analytics feedback
that does not
disempower and de-
motivate struggling
learners?

32

analytics for
learning conversations

33

Socio-cultural discourse analysis
(Mercer et al, OU)

•  Disputational talk, characterised by disagreement and
individualised decision making.

•  Cumulative talk, in which speakers build positively but
uncritically on what the others have said.

•  Exploratory talk, in which partners engage critically but
constructively with each other's ideas.

Mercer, N. (2004). Sociocultural discourse analysis: analysing classroom talk as a social
mode of thinking. Journal of Applied Linguistics, 1(2), 137-168.
34

Socio-cultural discourse analysis
(Mercer et al, OU)

•  Exploratory talk, in which partners engage critically but
constructively with each other's ideas.
•  Statements and suggestions are offered for joint consideration.

•  These may be challenged and counter-challenged, but challenges are
justified and alternative hypotheses are offered.

•  Partners all actively participate and opinions are sought and considered
before decisions are jointly made.

•  Compared with the other two types, in Exploratory talk knowledge is made
more publicly accountable and reasoning is more visible in the talk.

Mercer, N. (2004). Sociocultural discourse analysis: analysing classroom talk as a social
mode of thinking. Journal of Applied Linguistics, 1(2), 137-168.
35

Analytics for identifying Exploratory talk

Elluminate sessions can
be very long – lasting for
hours or even covering
days of a conference

It would be useful if we could
identify where quality learning
conversations seem to be taking
place, so we can recommend
those sessions, and not have to
sit through online chat about
virtual biscuits

Ferguson, R. and Buckingham Shum, S. Learning analytics to identify exploratory dialogue within synchronous text chat. 36
1st International Conference on Learning Analytics & Knowledge (Banff, Canada, 27 Mar-1 Apr, 2011)

Defining indicators of Exploratory Talk

Category Indicator
Challenge But if, have to respond, my view
Critique However, I’m not sure, maybe
Discussion of Have you read, more links
resources
Evaluation Good example, good point
Explanation Means that, our goals
Explicit reasoning Next step, relates to, that’s why
Justification I mean, we learned, we observed
Reflections of Agree, here is another, makes the
perspectives of others point, take your point, your view
37

Extract classified as Exploratory Talk

Time Contribution
2:42 PM I hate talking. :-P My question was whether "gadgets" were just
basically widgets and we could embed them in various web sites,
like Netvibes, Google Desktop, etc.
2:42 PM Thanks, that's great! I am sure I understood everything, but looks
inspiring!
2:43 PM Yes why OU tools not generic tools?
2:43 PM Issues of interoperability
2:43 PM The "new" SocialLearn site looks a lot like a corkboard where you
can add various widgets, similar to those existing web start pages.
2:43 PM What if we end up with as many apps/gadgets as we have social
networks and then we need a recommender for the apps!
2:43 PM My question was on the definition of the crowd in the wisdom of
crowds we acsess in the service model?
2:43 PM there are various different flavours of widget e.g. Google gadgets,
W3C widgets etc. SocialLearn has gone for Google gadgets 38

Discourse analytics on webinar
textchat
Given a 2.5 hour webinar, where in the live
textchat were the most effective learning
conversations?

Not at the start and end of a webinar
Sheffield, UK not as sunny but if we zoom in on a peak… See you!
as yesterday - still warm
bye for now!
Greetings from Hong Kong
bye, and thank you
Morning from Wiltshire,
80
sunny here! Bye all for now

60

40

20

0
9:28
9:32

10:13

11:48

12:00

12:05
12:04
9:36
9:40
9:41
9:46
9:50
9:53
9:56
10:00
10:05
10:07
10:07
10:09

10:17
10:23
10:27
10:31
10:35
10:40
10:45
10:52
10:55
11:04
11:08
11:11
11:17
11:20
11:24
11:26
11:28
11:31
11:32
11:35
11:36
11:38
11:39
11:41
11:44
11:46

11:52
11:54

12:03
-20

-40
Average Exploratory
-60

Extensions to: Ferguson, R. and Buckingham Shum, S. (2011). Learning Analytics to Identify Exploratory Dialogue within Synchronous Text Chat.
Proc. 1st Int. Conf. Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. ACM Press. Eprint: http://oro.open.ac.uk/28955

Discourse analytics on webinar
textchat

Given a 2.5 hour
webinar, where in the
live textchat were the
most effective learning
conversations?
Classified as
“exploratory
talk”

(more
substantive
100 for learning)

50

0
9:28

“non-
9:40
9:50
10:00
10:07
10:17
10:31
10:45
11:04
11:17
11:26
11:32
11:38
11:44
11:52
12:03

-50 exploratory”

Averag
-100

Wei & He extensions to: Ferguson, R. and Buckingham Shum, S. (2011). Learning Analytics to Identify Exploratory Dialogue within Synchronous
Text Chat. Proc. 1st Int. Conf. Learning Analytics & Knowledge. Feb. 27-Mar 1, 2011, Banff. ACM Press. Eprint: http://oro.open.ac.uk/28955

KMi’s Cohere:
a web deliberation platform enabling semantic social
network and discourse network analytics

Rebecca is playing
the role of broker,
connecting 2 peers’
contributions in
meaningful ways

De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. Discourse-centric learning analytics. 1st
International Conference on Learning Analytics & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829

analytics for
scholarly writing

42

Discourse analysis (Xerox Incremental Parser)
Detection of salient sentences in scholarly reports,
based on the rhetorical signals authors use:
BACKGROUND KNOWLEDGE: NOVELTY: OPEN QUESTION:
Recent studies indicate … ... new insights provide direct evidence ... … little is known …

… the previously proposed … ... we suggest a new ... approach ... … role … has been elusive
Current data is insufficient …
… is universally accepted ... ... results define a novel role ...

CONRASTING IDEAS: SIGNIFICANCE: SUMMARIZING:
… unorthodox view resolves … studies ... have provided important The goal of this study ...
paradoxes … advances Here, we show ...
In contrast with previous Knowledge ... is crucial for ... Altogether, our results ... indicate
hypotheses ... understanding
... inconsistent with past findings ... valuable information ... from studies

GENERALIZING: SURPRISE:
... emerging as a promising approach We have recently observed ...
surprisingly
Our understanding ... has grown
exponentially ... We have identified ... unusual
... growing recognition of the The recent discovery ... suggests Ágnes Sándor & OLnet Project:
http://olnet.org/node/512
intriguing roles
importance ...

De Liddo, A., Sándor, Á. and Buckingham Shum, S., Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine
Annotation Study. Computer Supported Cooperative Work, 21, 4-5, (2012), 417-448. http://oro.open.ac.uk/31052

Human and machine analysis of a text for key
contributions

Document 1 19 sentences annotated 22 sentences annotated
11 sentences same as human annotation
Document 2 71 sentences annotated 59 sentences annotated
42 sentences same as human annotation

http://technologies.kmi.open.ac.uk/cohere/2012/01/09/cohere-plus-automated-rhetorical-annotation
De Liddo, A., Sándor, Á. and Buckingham Shum, S., Contested Collective Intelligence: Rationale, Technologies, and a Human-Machine
Annotation Study. Computer Supported Cooperative Work, 21, 4-5, (2012), 417-448. http://oro.open.ac.uk/31052

analytics for
intepersonal networking

45

Semantic Social Network Analytics

De Liddo, A., Buckingham Shum, S., Quinto, I., Bachler, M. and Cannavacciuolo, L. Discourse-centric learning analytics. 1st
International Conference on Learning Analytics & Knowledge (Banff, 27 Mar-1 Apr, 2011) http://oro.open.ac.uk/25829

Visualizing and filtering social ties in
SocialLearn by topic and type

Visualising Social Learning in the SocialLearn Environment. Bieke Schreurs and Maarten de Laat (Open University, The
Netherlands), Chris Teplovs (Problemshift Inc. and University of Windsor), Rebecca Ferguson and Simon Buckingham
Shum (Open University UK), SoLAR Storm webinar, Open University UK. http://bit.ly/UaFhbL

Closing thoughts

54

“The basic question is not
what can we measure?

The basic question is
what does a good education look
like?”

(Gardner Campbell)

http://chronicle.com/blogs/techtherapy/2012/05/02/episode-95-learning-analytics-could-lead-to-wal-martification-of-college
http://lak12.wikispaces.com/Recordings 55

Our analytics promote
values, pedagogy and
assessment regimes.

Are we clear which master
our analytics serve? Are we
happy to be judged by them?

56

Will learning analytics merely
turbocharge the current
educational paradigm?

— which is so often declared
not fit for purpose…

57

…or will learning analytics
reflect what we now know
about designing authentic,
engaged learning, developing
the new qualities that a
complex society demands?

58

Learning Analytics: what are we optimizing for?

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