Slides from Keynote presentation at the University of Southern California's 2015 Teaching with Technology annual conference. "9:15 am – ANN Auditorium Key Note: What Do We Mean by Learning Analytics? Leah Macfadyen, Director for Evaluation and Learning Analytics, University of British Columbia Executive Board, SoLAR (Society for Learning Analytics Research) Leah Macfadyen will define and explore the emerging and interdisciplinary field of learning analytics in the context of quantified and personalized learning. Leah will use actual examples and case studies to illustrate the range of stakeholders learning analytics may serve, the diverse array of questions they may be used to address, and the potential impact of learning analytics in higher education."

1. …in
a
world
of
larger
and
larger
data
sets,
increasing
popula4ons
of
increasingly
diverse
learners,
constrained
educa4on
budgets
and
greater
focus
on
quality
and
accountability
(Macfadyen
&
Dawson,
2012),
some
argue
that
using
analy4cs
to
op4mize
learning
environments
is
no
longer
an
op4on
but
an
impera4ve…
…Educa4on
can
no
longer
afford
not
to
use
learning
analy4cs.
As
Slade
and
Prinsloo
(2013)
maintain,
“Ignoring
informa4on
that
might
ac4vely
help
to
pursue
an
ins4tu4on’s
goals
seems
shortsighted
to
the
extreme”
(p.
1521).
Macfadyen
et
al.,
2014

2. What
do
we
mean
by
‘learning
analy<cs’?
Leah
P.
Macfadyen,
PhD
Program
Director,
Evalua<on
and
Learning
Analy<cs
Faculty
of
Arts,
The
University
of
Bri<sh
Columbia
Vancouver,
Canada
leah.macfadyen@ubc.ca
hNp://www.slideshare.net/leahmac1
hNp://isit.arts.ubc.ca/learning-­‐analy<cs/

3. 1. Learning
analy<cs...just
another
buzzword?
2. Origins
and
drivers
3. Current
state
of
the
field:
Progress
and
challenges
4. Grassroots
LA:
Mee<ng
local
needs
5. ‘Big’
learning
analy<cs:
Ins<tu<onal
implementa<ons
6. Why
should
you
care?

4.
Learning
analy<cs
has
been
defined
as
the
process
of
developing
ac#onable
insights
through
problem
defini4on
and
the
applica4on
of
sta#s#cal
models
and
analysis
against
exis4ng
and/or
simulated
future
data
Cooper,
2012

5. The
Society
for
Learning
Analy<cs
Research
(SoLAR)
defines
the
core
ac<vi<es
of
learning
analy<cs
as
the
measurement,
collec4on,
analysis
and
repor4ng
of
data
about
learners
and
their
contexts
for
purposes
of
understanding
and
op#mizing
learning
and
the
environments
in
which
learning
occurs.
hNp://www.solaresearch.org

6. Where
has
LA
come
from?
LA
draws
from,
and
is
closely
<ed
to,
a
series
of
other
fields
of
study
including
— Business
intelligence
— Web
analy<cs
— “Academic
analy<cs”
(2005
-­‐
)
— Educa<onal
data
mining
(EDM)(~2000
-­‐)
— “Ac<on
analy<cs”
(~2008
-­‐
)
Elias,
2011

7. Drivers
The
underpinnings:
— Socio-­‐poli<cal/economic
factors
— Educa<onal
factors
— Technological
factors
Ferguson,
2012

8. New
developments….
1.
Converging
developments
in
data
science
— Even
bigger
“big
data”
— Increasing
volumes
of
teaching-­‐
and
learning-­‐related
data
— More,
and
more
accessible,
data
— Parallel
developments
in
educa<onal
data
science
— Data
mining
algorithms
and
predic<ve
modelling
— Approaches
to
network
analysis
— Approaches
to
text
mining
and
discourse
analysis
at
scale
— Increasingly
user-­‐friendly
sofware

9. 2.
Moving
beyond
number
crunching
— EDM
laid
groundwork
of
computa<on
and
modelling
— RecogniFon
that
learning
cannot
simply
be
understood
by
algorithms,
and
that
social
and
pedagogical
theory,
approaches
and
insights
must
also
be
brought
to
bear.
— Technological,
ideological,
and
methodological
orienta<ons
of
the
field
of
learning
analy<cs
differen<ate
it
from
EDM
“…technical,
pedagogical,
and
social
domains
must
be
brought
into
dialogue
with
each
other
to
ensure
that
interven4ons
and
organiza4onal
systems
serve
the
needs
of
all
stakeholders.”
Siemens
&
Baker,
2012

10. 3.
Interdisciplinary
collaboraFon
Technical/AnalyFc
Social
Sciences/EducaFon
• sta<s<cs
• data
visualiza<on
and
visual
analy<cs
• educa<onal
data
mining
• computer
science
• machine
learning
• natural
language
processing
• human-­‐computer
interac<on
• and
others….
• social
sciences
• educa<on
• (educa<onal)
psychology
• psychometrics
• educa<onal
technology
• art
and
design
• and
others…

11. t 2013
e processing
r interaction
ychology
nology
gher education as a system
social pedagogical
technical/analytic
LA
EDM
Figure 1. The interdisciplinary scope of learning analytics

12. LA
ImplementaFon:
Current
state
of
the
field
— Stage
1:
Extrac<on
and
repor<ng
of
transac<on-­‐level
data
— Stage
2:
Analysis
and
monitoring
of
opera<onal
performance
— Stage
3:
“What-­‐if”
decision
support
(such
as
scenario
building)
— Stage
4:
Predic<ve
modeling
&
simula<on
— Stage
5:
Automa<c
triggers
and
alerts
(interven<ons)
Goldstein,
P.
&
Katz,
R.
(2005)

13. AnalyFcs
Panic?
• For more on challenges of acting on educational research, see McIntosh, 1979
• For more on LA strategy and policy, see Ferguson et al., 2014; Macfadyen et al.,
2014

14. Learning
analyFcs
goals
— Learner
awareness
— Monitoring
and
tracking
— Reflec<on
and
research
— Evalua<on
and
planning
— Repor<ng
and
communica<on
adapted
from
Kay
(2013)

15. Start
where
you
are:
Clarifying
stakeholders,
purposes
and
goals
Discussion Paper, August 2013
Table 2. Purposes and stakeholders in university learning analytics (adapted from Kay (2013))
LA tools or insights can facilitate…
Example
stakeholders?
Learner awareness of own learning strategies and performance to
encourage self-regulated learning and support metacognition
Learners
Monitoring and tracking for immediate decisions
• Identifying problems early enough to intervene
• Distinguishing students who are loafing, gaming the system
Individual educators
Reflection and research for recognizing long term issues
• Insights into learning processes and performance by many
learners
• Education research
o Attrition factors
o Insights into individual performance
Individual educators
Educational researchers
Administrators
Planning
• course design/re-design

16. • Identifying problems early enough to intervene
• Distinguishing students who are loafing, gaming the system
Individual educators
Reflection and research for recognizing long term issues
• Insights into learning processes and performance by many
learners
• Education research
o Attrition factors
o Insights into individual performance
Individual educators
Educational researchers
Administrators
Planning
• course design/re-design
• curriculum and program planning
• faculty-level planning with regards to course and program
offerings
• teaching assignments
• decision-making with regards to management, staffing, etc.
Individual educators
Administrators
Reporting and communication among and between stakeholder
groups e.g.:
• Educators to learners
• Institution to parents
• Institution to government
• Peer to peer
Learners
Educators
Administrators
Government
In sum, learning analytics as a field spans a wide range of stakeholders, ends and output types. It has at
its core an exploratory and analytic approach to educational research that makes use of large sets of
educational data, with the goal of uncovering new approaches to measuring or monitoring learning or
teaching. Output may include tools, systems or reports for learners and/or educators on their activities
within the teaching and learning system. It may also include automated or non-automated reporting
and/or visualization tools for educators, administrators, governments and policy makers on individual,
unit or institutional performance, with the goal of informing ongoing planning and educational

17. Big
data
in
UBC
Arts…
— Student
informa<on
(demographics,
origins,
academic
history…)
— Enrollment
informa<on
(enrollments,
withdrawals,
grades,
program
specializa<ons)
— Course
evalua<ons
(scores,
comments)
— LMS
ac<vity
data
(‘clickstream’
data)
— MOOC
data
— Ac<vity
data
from
other
technologies
(lecture
annota<on
systems,
blogs,
wikis)
— All
kinds
of
student
wri<ng
(online,
offline)
— Open
data
(LinkedIn
data,
geographic
data…)

18. LA
quesFons
in
UBC
Arts
— Do
students
from
different
parts
of
the
world
show
different
ac<vity
levels/paNerns
in
online
courses?
— Which
students
are
more
likely
to
complete
course
evalua<ons,
and
does
it
maNer?
— Have
average
grades
changes
over
<me,
and
how?
— Which
students
are
most
at
risk
of
failure?
(How
early
can
we
iden<fy
them
and
provide
beNer
support?)
— Who
are
our
students?
Where
do
they
come
from
and
how
has
that
changed
over
<me?

19. — What
can
clickstream
data
tell
us
about
how
students
learn?
— What
does
‘learner
engagement’
look
like
in
a
MOOC
and
how
can
we
promote
it?
— What
are
the
most
common
themes
in
student
course
evalua<on
comments?
— What
are
the
most
common
enrollment
choices
our
learners
make
to
complete
their
degrees?
Do
‘high
achievers’
make
different
choices
than
‘low
achievers’?
— Teaching
and
learning
detec<ve
stories
of
all
kinds…..

20. Author's personal copy
Mining LMS data to develop an ‘‘early warning system” for educators: A proof
of concept
Leah P. Macfadyen a,*, Shane Dawson b
a
Science Centre for Learning and Teaching, The University of British Columbia, 6221 University Boulevard, Vancouver, British Columbia, Canada V6T 1Z1
b
Graduate School of Medicine, University of Wollongong, Wollongong, NSW 2522, Australia
a r t i c l e i n f o
Article history:
Received 21 May 2009
Received in revised form 31 August 2009
Accepted 3 September 2009
Keywords:
Collaborative learning
Evaluation methodologies
Learning communities
Teaching/learning strategies
Post-secondary education
a b s t r a c t
Earlier studies have suggested that higher education institutions could harness the predictive power of
Learning Management System (LMS) data to develop reporting tools that identify at-risk students and
allow for more timely pedagogical interventions. This paper confirms and extends this proposition by
providing data from an international research project investigating which student online activities accu-
rately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported
course identified 15 variables demonstrating a significant simple correlation with student final grade.
Regression modelling generated a best-fit predictive model for this course which incorporates key vari-
ables such as total number of discussion messages posted, total number of mail messages sent, and total num-
ber of assessments completed and which explains more than 30% of the variation in student final grade.
Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of
students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded
insight into the development of the student learning community by identifying disconnected students,
patterns of student-to-student communication, and instructor positioning within the network. This study
affirms that pedagogically meaningful information can be extracted from LMS-generated student track-
ing data, and discusses how these findings are informing the development of a customizable dashboard-
like reporting tool for educators that will extract and visualize real-time data on student engagement and
likelihood of success.
Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction
Higher education institutions (HEIs) around the world are undergoing rapid changes as they adapt to the new realities of the knowledge
society. While the development, maintenance and dissemination of knowledge have long been the primary goals of higher education insti-
tutions (Bloland, 1995), recent social and economic changes are forcing universities to adopt new approaches in the way these goals are
achieved. Educators are being asked to demonstrate quality teaching practices with decreasing fiscal and human resources, whilst catering
to the learning needs of a burgeoning student population that is increasingly diverse in many dimensions (Twigg, 1994, 1994a, 1994b).
Student demographics have radically shifted, and student enrollment numbers have dramatically increased (Patrick & Gaële, 2007). The
traditional and idealized experience of higher education as ‘‘academically oriented living and learning communities” where ‘‘full-time stu-
dents receive a good deal of faculty contact and many academic support services in the residential setting” (Volkwein, 1999, p. 14) relies
upon intensive fiscal and labour resources. Unfortunately, the reality is that federal, state and provincial government funding commitments
for higher education have failed to match the escalating sector resourcing requirements (Bates, 2000; Rossner & Stockley, 1997). To sup-
Computers & Education 54 (2010) 588–599
Contents lists available at ScienceDirect
Computers & Education
journal homepage: www.elsevier.com/locate/compedu
Author's personal copy
3.3. Logistic regression
A binary logistic regression analysis was conducted to test the reliability of the model in predicting whether or not an individual student
is considered ‘at risk of failure’. Individual students with course final grade <60% were coded as ‘at risk’ (0), while students with final grade
P60% were coded as ‘performing adequately or better’ (1). In the UBC grading scheme, <60% represents a grade of C- or poorer; <50% is
considered a failing grade (University of British Columbia, 2009). We selected this division point to include students whose final grade indi-
cates that they barely passed the course and may have benefitted from earlier support and intervention.
Details of the regression model are shown in Table 5. For the purposes of this study, the ‘hit rate’ or predictive power of the model is of
greatest significance. Overall, the logistic regression model accurately placed individual students in either the ‘at risk’ or ‘performing ade-
quately’ category 73.7% of the time (Table 6). The model resulted in ‘Type II’ errors (classifying an ‘at risk’ student as ‘performing ade-
quately’) at a rate of only 12.7%: 15 students out of 118 were predicted to be performing adequately, while their final course grade
placed them in the ‘at risk’ category. However, of these 15 students, only four actually failed the course (achieving a final grade of
<50%), representing a ‘predictive failure’ rate of only 3.4% (four students out of 118). The logistic model also resulted in Type I errors
13.6% of the time by placing 16 students in the ‘at risk’ category even though these students eventually passed the course (achieving grades
of >60%). However, given the importance of identifying students at risk of failure early in their course progression, the occurrence of Type I
errors is of less concern. More simply put, it is better to mistakenly identify a student as at risk of failure then to neglect a student requiring
additional learning support. In sum, logistic modelling effectively identified the majority of the students who failed or almost failed this
course and who would have been considered ‘at risk of failure’ by instructors if this data had been accessible earlier in the term.
Table 5
Logistic regression analysis summary for course LMS tracking variables (Nstudents = 118).
Included 95% CI for Exp b
b (SE) Lower Exp b Upper
Constant .46 (.24) 1.58
# Mail messages sent .74*
(.35) 1.05 2.10 4.19
Total # discussion messages posted 1.02*
(.39) 1.30 2.78 5.98
# Assessments finished .66*
(.26) 1.16 1.93 3.22
Note: r2
= .31 (Cox & Snell, 1989), .32 (Nagelkerke, 1991). Model v2
= 9.59.
*
p < .05.
Table 6
‘Risk of failure’ classification results for students in course (Nstudents = 118).
Observed Predicted
At risk Not at risk Percentage correct
At risk 38 15 71.7
Not at risk 16 49 75.4
Overall percentage 73.7
‘At risk’ = final grade <60%; ‘Not at risk’ = final grade >60%.
L.P. Macfadyen, S. Dawson / Computers & Education 54 (2010) 588–599 595

21. SNAPP http://www.snappvis.org

23. A ‘Top 10%’ student’s ego network”

24. A ‘Bottom 10%’ student’s ego network”

25. Can network analysis help us understand learner
behaviour and persistence in MOOCs?

26. Change in the
student body
over time?

27. Who is dropping out?

28. Finding
“Failure
Hotspots”

29. “Student
Success”
Dashboards

30. Temporal analysis of enrollment patterns

32. The
Purdue
Signals
Project
hNp://www.itap.purdue.edu/studio//signals/
“The premise behind CS
is fairly simple: utilize the
wealth of data found at an
educational institution,
including the data
collected by instructional
tools, to determine in real
time which students might
be at risk, partially
indicated by their effort
within a course.”
Arnold & Pistilli, 2012

34. UMBC’s
‘Check
My
AcFvity’
tool
hNp://www.educause.edu/ero/ar<cle/video-­‐demo-­‐umbcs-­‐check-­‐my-­‐ac<vity-­‐tool-­‐students

35. UMBC
Course
AcFvity
by
Final
Grade
Credit: John Fritz, UMBC

36. CMA
Use
by
Final
Grade
Credit: John Fritz, UMBC

37. PercentGrade
UBC
data
on
‘self-­‐monitoring’
and
performance

38. Student
ApFtude
Data
(SATs,
current
GPA,
etc.)
Student
Demographic
Data
(Age,
gender,
etc.)
Sakai
Event
Log
Data
Sakai
Gradebook
Data
PredicFve
Model
Scoring
Iden<fies
students
“at
risk”
to
not
complete
course
SIS
Data
LMS
Data
IntervenFon
Deployed
“Awareness”
or
Online
Academic
Support
Environment
(OASE)
Model
Developed
Using
Historical
Data
Step
#1:
Developed
model
using
historical
data
Academic
Alert
Report
(AAR)
CreaFng
an
Open
Academic
Early
Alert
System
hbps://confluence.sakaiproject.org/display/LAI/Learning+AnalyFcs+IniFaFve

39. Research
Findings:
Final
Course
Grades
— Analysis
showed
a
staFsFcally
significant
posi<ve
impact
on
final
course
grades
— No
difference
between
treatment
groups
— Similar
trend
among
low
income
students
50
60
70
80
90
100
Awareness
OASE
Control
Final
Grade
(%)
Mean
Final
Grade
for
"at
Risk"
Students
Jayaprakash et al., 2014

40. Why
should
you
care?
— Driving
and
evalua<ng
innova<on
— Support
for
self-­‐directed
learning
— Provision
of
informed
and
targeNed
advice
and
support
at
all
stages
of
the
learning
journey:
recruitment,
academic
advising
and
recommending,
facilita<on,
reten<on
— Con<nuous
assessment
of
quality
of
learning
materials
— Development
of
new
(beNer?)
educa<onal
performance
indicators
and
modes
of
assessment
— Real-­‐<me
academic
performance
monitoring
and
management
for
learners
and
educators
— Maintenance
of
comprehensive
student
records
— Effec<ve
repor<ng
at
mul<ple
levels

41. Learning
analy4cs
(LA)
offers
the
capacity
to
inves4gate
the
rising
4de
of
learner
data
with
the
goal
of
understanding
the
ac4vi4es
and
behaviors
associated
with
effec4ve
learning,
and
to
leverage
this
knowledge
in
op4mizing
our
educa4onal
systems
(Bienkowski,
Feng,
&
Means,
2012;
Campbell,
DeBlois,
&
Oblinger,
2007).
Macfadyen
et
al.,
2014
leah.macfadyen@ubc.ca
http://www.slideshare.net/leahmac1

42. Arnold,
K.
E.
&
Pis<lli,
M.
(2012).
Course
Signals
at
Purdue:
Using
Learning
Analy<cs
To
Increase
Student
Success.
Course
Signals
at
Purdue:
Using
learning
analy<cs
to
increase
student
success.
Proceedings
of
the
2nd
Interna<onal
Conference
on
Learning
Analy<cs
&
Knowledge.
New
York:
ACM.
hNps://www.itap.purdue.edu/learning/docs/research/Arnold_Pis<lli-­‐
Purdue_University_Course_Signals-­‐2012.pdf
Bichsel,
J.
(2012).
2012
ECAR
study
of
analy4cs
in
higher
educa4on.
Retrieved
from
hNp://www.educause.edu/library/resources/2012-­‐ecar-­‐study-­‐analy<cs-­‐higher-­‐educa<on
Cooper,
(2012).
What
is
Analy<cs?
Defini<on
and
Essen<al
Characteris<cs.
CETIS
Analy4cs
Series,
1
(5).
hNp://publica<ons.ce<s.ac.uk/2012/521
Elias,
T.
(2011).
Learning
Analy4cs:
Defini4ons,
Processes
and
Poten4al.
hNp://learninganaly<cs.net/LearningAnaly<csDefini<onsProcessesPoten<al.pdf
Ferguson,
R.
(2012).
Learning
analy<cs:
drivers,
developments
and
challenges.
Interna4onal
Journal
of
Technology
Enhanced
Learning,
4(5/6)
pp.
304–317.
Forrester,
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W.
(1961).
Industrial
dynamics.
Cambridge,
MA:
MIT
Press.
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P.,
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D.,
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B.,
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S.,
&
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S.
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