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Dr David Jones University of Southern Queensland Toowoomba QLD Colin Beer CQUniversity Rockhampton QLD Four questions for understanding Learning Analytics 4

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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 @djplaner

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Page 1: 1 introduction

Dr David JonesUniversity of Southern QueenslandToowoomba QLD

Colin BeerCQUniversity Rockhampton QLD

Four questions for understanding Learning Analytics

4

Page 2: 1 introduction

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

Page 3: 1 introduction

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

Page 4: 1 introduction

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?

Page 5: 1 introduction

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

Page 6: 1 introduction

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)

Page 7: 1 introduction

Context

http://farm3.staticflickr.com/2734/4152919570_3acdefc13e_z.jpg

Page 8: 1 introduction

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?

Page 9: 1 introduction

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?

Page 10: 1 introduction

Where it all started

• The Indicators project• LMS support• Curiosity about LMS behaviour and student

results• Interesting Correlations

Page 11: 1 introduction
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Page 16: 1 introduction

Blackboard, term 1, 2006

Learner-Content

69%

Learner-Learner

19%

Learner-Teacher11%

Teacher-Teacher0%

Page 17: 1 introduction

Moodle, term 1, 2011

Learner-Content

78%

Learner-Learner

11%

Learner-Teacher10%

Teacher-Teacher1%

Page 18: 1 introduction

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)

Page 19: 1 introduction

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:

Page 20: 1 introduction

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)

Page 21: 1 introduction

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)

Page 22: 1 introduction

Learning analytics definitions

“Learning analytics is the application of … Big Data techniques to improve learning”

(Clow, 2013)

Page 23: 1 introduction

Learning analytics definitions

“Learning analytics is the application of … Big Data techniques to improve learning”

(Clow, 2013)

Page 24: 1 introduction
Page 25: 1 introduction

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

Page 26: 1 introduction

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

Page 27: 1 introduction

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

Page 28: 1 introduction

Task

Page 29: 1 introduction
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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

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Page 38: 1 introduction

SNAPP

Page 39: 1 introduction

Gephi

Page 40: 1 introduction
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BIM

Page 42: 1 introduction

ga1ga2

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ga7ga8

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

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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

Page 43: 1 introduction
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www.knewton.com

Page 49: 1 introduction

Individual/specific – Institutional/vague

http://farm6.staticflickr.com/5002/5226383821_378b5a136e_z.jpg

Page 50: 1 introduction

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


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