1 introduction
DESCRIPTION
The introduction to the workshop followed by discussion of Context and Task. Part of the slides for a workshop titled "Four questions for understanding Learning Analytics" by @beerc and @djplanerTRANSCRIPT
Dr David JonesUniversity of Southern QueenslandToowoomba QLD
Colin BeerCQUniversity Rockhampton QLD
Four questions for understanding Learning Analytics
4
Facilitators• Dr David Jones– Faculty of Education @ University of Southern
Queensland– Foundation member of the Indicators project.
• Colin Beer– Learning and Teaching Services @ CQUniversity– Foundation member of the Indicators project.
http://indicatorsproject.wordpress.com
This workshop will use a research-based framework of four questions to help you:
• Increase your awareness of what learning analytics is and what is currently being done (and not done) with it
• Understand how insights from a range of knowledge bases can better inform learning analytics projects
• Develop insights into how you can use learning analytics to complete your own task
How it will work
• Context– Yours, ours and what we’d like to get out of this
• Task– Some examples of Learning Analytics
• Information?• Representation?• Affordances?• Change?
How it will work
• Context– Yours, ours and what we’d like to get out of this
• Task– Some examples of Learning Analytics
• Information?• Representation?• Affordances?• Change?
Theory and discussion
Some hands on
Assumptions
• Value of learning analytics is whenIntegrated into “tools & processes of teaching &
learning” (Elias, 2011, p. 5)
“provide workers with the help they need to perform certain job tasks, at the time they need
that help, and in a form that will be most helpful” (Reiser, 2001, p.63)
Context
http://farm3.staticflickr.com/2734/4152919570_3acdefc13e_z.jpg
Who we are
• Colin Beer from CQUniversity (Rockhampton)• Lecturer (Educational Technology) within the
Learning and Teaching Services Area• Why am I interested in Learning Analytics?• Learning Analytics activities at CQUniversity• Systems and technologies?• What I would like from this session?
Who we are
• David Jones from USQ (Toowoomba)• Senior Lecturer within the Faculty of
Education• Why am I interested in Learning Analytics?• Learning Analytics activities at CQUniversity• Systems and technologies?• What I would like from this session?
Where it all started
• The Indicators project• LMS support• Curiosity about LMS behaviour and student
results• Interesting Correlations
Blackboard, term 1, 2006
Learner-Content
69%
Learner-Learner
19%
Learner-Teacher11%
Teacher-Teacher0%
Moodle, term 1, 2011
Learner-Content
78%
Learner-Learner
11%
Learner-Teacher10%
Teacher-Teacher1%
F P C D HD0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Number of question marks (n=273814)
Number of question marks Linear (Number of question marks)
Your Context
• Who you are and where you are from?• What is your interest in Learning Analytics?• What Learning Analytics activities are planned
or underway at your institution?• What systems/technologies are potential
sources of information in your institution?• What do you want from this session?
Please tell us:
Learning analytics definitions
A key concern in learning analytics is the need to use the insights gathered from the data to make interventions, to improve learning and to generate ‘actionable intelligence’ which informs appropriate interventions
(Campbell, DeBlois & Oblinger 2007)
Learning analytics definitions
“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”
(Long and Siemens 2012)
Learning analytics definitions
“Learning analytics is the application of … Big Data techniques to improve learning”
(Clow, 2013)
Learning analytics definitions
“Learning analytics is the application of … Big Data techniques to improve learning”
(Clow, 2013)
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
Ave
rage
num
ber o
f pos
ts a
nd re
plie
s
The mythical mean
0
2
4
6
8
10
12
14
16
18
20
Moodle courses across a single year
Ave
rage
num
ber o
f con
trib
ution
s pe
r stu
dent
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 o
f for
um c
ontr
ibuti
ons
Task
First day of access
F P C D HD
-4-3-2-1012345
First Day of Access (n=35623) Distance Students
Student Grades
Firs
t day
of a
cces
s
SNAPP
Gephi
BIM
ga1ga2
ga3ga4
ga5ga6
ga7ga8
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Assessment Graduate Attribute Average Levels
Learning Outcome Graduate Attribute Average Levels
Average Graduate Attribute Levels by Assessment & Learning Outcomes - CQUni (2011)
Assessment Graduate Attribute Average Levels Learning Outcome Graduate Attribute Average Levels
CQUni Graduate Attributes
Aver
age
Grad
uate
Att
ribut
e Le
vel
www.knewton.com
Individual/specific – Institutional/vague
http://farm6.staticflickr.com/5002/5226383821_378b5a136e_z.jpg
Layers of Learning AnalyticsMicro- Meso- Macro-
Process-level Institutional Cross-institutional
Learner and teacher
Department, University
Region, state, international
Social network analysis, NLP, assessing engagement
Risk-detection, intervention and support services
Optimisation, external comparison, regulatory reporting