ascilite 2012
TRANSCRIPT
Analytics and Complexity
Learning and leading for the future
Colin Beer (CQUni)David Jones (USQ)
Damien Clark (CQUni)
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Some definitions
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Managerialism
“The teleological approach to the management of universities is known as managerialism and its influence has extended to how universities manage their learning and teaching”
Beer, Jones & Clark (2012)
Educational Data Mining“Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.”
George Siemens, 2011 (http://www.learninganalytics.net/?paged=2)
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Academic Analytics…“marries statistical techniques and predictive modeling with the large data sets collected by HEI, including those collected by the LMS. Academic analytics has been described as business intelligence for HEI and is focused on the needs of the institution, such as recruitment, retention and pass rates”
Open University, 2012
Learning Analytics…“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”
George Siemens, 2011 (http://www.learninganalytics.net/?paged=2)
Educ
atio
nal D
ata
Min
ing
Academic
Analytics
Learning Analytic
s Course Level
Program Level
Faculty Level
School Level
Institutional Level
National Level
Adapted from Siemens (2011)
• Multiple CQUniversity internal L&T grants• DEHUB research grant (2011)• Numerous publications• Numerous conference presentations• Established 2008
The Indicators story so far
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Blackboard
300M+ records
Moodle
80M+records
PeopleSoft+80,000 Student results
PeopleSoft+80,000Student records
IndicatorsPlatform
independent data
Moodle 2
30M+records
SRQ
+5000records
Some simple patterns
WF F P C D HD0
50100150200250300350400450
Hits (n=39087)
Student Grades
Stud
ent
clic
ks
Some simple patterns
F P C D HD
-4-3-2-1012345
First Day of Access (n=35623) Distance Students
Student Grades
Firs
t da
y of
acc
ess
Some simple patterns
F P C D HD0
0.20.40.60.8
11.21.41.61.8
2Number of question marks
Student Grades
Aver
age
num
ber
of q
uest
ion
mar
ks
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The next big thing
“BIG data sets showing what students do online may prove as vital to education as genome databases have been to genetics or Europe's Large Hadron Collider to physics”
The Australian (15th October 2012)
The next big thing
“EDUCAUSE and the Bill and Melinda Gates Foundation have targeted learning analytics as one of 5 categories for funding initiatives”
Educause (2012)
The next big thingLearning analytics promises to harness the power of advances in data mining, interpretation, and modeling to improve understandings of teaching and learning, and to tailor education to individual students more effectively.
Horizon report (2011)
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Potential Problems
Abstraction losing detailOrganisational structuresConfusion between correlation and causationAssumptions of causality
“…the nature of learning analytics and its reliance on abstracting patterns or relationships from data has a tendency to hide the complexity of reality”
Gardner Campbell (2012)
Abstraction losing detail
Some simple patterns
F P C D HD0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
Forum Posts Forum Replies
Student Grades
Aver
age
num
ber
of p
osts
and
re
plie
s
The mythical mean
0
2
4
6
8
10
12
14
16
18
20
Moodle courses across a single year
Aver
age
num
ber
of c
ontr
ibut
ions
pe
r st
uden
t
Single HD student
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180
5
10
15
20
25
30
35
Individual courses
Num
ber
of fo
rum
con
tri-
buti
ons
Organisational Structures
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Design by division
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Inter-departmental rivalry
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Confusing correlation with causation
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Some simple patterns
F P C D HD0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
Single HD student
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 180
5
10
15
20
25
30
35
Assumptions of causality
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Minimum Standards
61%39%
Complex adaptive systems
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Complex adaptive systems
“A CAS is a dynamic network of semiautonomous, competing and collaborating individuals who interact and coevolve in nonlinear ways with their surrounding environment. These interactions lead to various webs of relationships that influence the system’s performance”Boustani (2012)
Macro level
Micro level
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Educ
atio
nal D
ata
Min
ing
Academic
Analytics
Learning Analytic
s Course Level
Program Level
Faculty Level
School Level
Institutional Level
National Level
Adapted from Siemens (2011)
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