connecting analytics, theory and the organisation: a model for learning analytics in higher...

DIVISION OF STUDENT LEARNING

Connecting Analytics, Theory and

the Organisation:

Exploring a multi-dimensional

model of learning analyticsSimon Welsh (Senior Learning Analytics Officer): siwelsh@csu.edu.au

Assoc Prof Philip Uys (Director, Learning Technologies): puys@csu.edu.au

Presentation at ASCILITE 2014

Nov 2014 Slides available from: http://www.slideshare.net/puys/connecting-

analytics-theory-and-the-organisation

DIVISION OF STUDENT LEARNING

Acknowledgements

Learning Analytics Working Party, CSU

Assoc Prof Philip Uys (Chair)

Paul Bristow

Dr Andrea Crampton

Dr Nick Drengenberg

Pat Loria

Vicki Pitcher

Liz Smith

Simon Welsh

Prof Alan Bain

Nina Clemson

Prof Barney Dalgarno

Dr Yann Guisard

Julie Newham

Tim Scott

Assoc Prof Joy Wallace

Dr Janelle Wheat

DIVISION OF STUDENT LEARNING

Overview

A. Why connect analytics, theory and the organisation?

B. Unpacking the CSU Learning Analytics Model, including

group discussions

DIVISION OF STUDENT LEARNING

A. Why connect analytics, theory and the

organisation?

Learning analytics only exists to help us adapt learning and

teaching to enhance student success

Making adaptations in learning and teaching requires

understanding of how students learn, their learning dispositions

and the learning context

Theoretical assumptions about these elements need to be

explicitly built into our analytics ... or other assumptions will be

implicitly built in: theory defines what we need to measure

To drive adaptation, learning analytics must be “plugged into”

people (and systems) who have the capacity, desire and agency

to use them: organisational dynamics define how we use

analytics

DIVISION OF STUDENT LEARNING

A. Why connect analytics, theory and the

organisation?

Questions?

DIVISION OF STUDENT LEARNING

Student

Success:• Quality

learning

• Achievement

of goals

• Retention &

progress

• Wellbeing

Focussed on student success

B. Unpacking the CSU Learning Analytics Model

DIVISION OF STUDENT LEARNING

Subject

Level

Course

Level

University

Level

Student Learning Characteristics

Student Learning Behaviours

Teaching

Curriculum Design

Learning Environment

Support

Drivers of Student Success

Student

Success:• Quality

learning

• Achievement

of goals

• Retention &

progress

• Wellbeing

Six domains and

three levels

B. Unpacking the CSU Learning Analytics Model

DIVISION OF STUDENT LEARNING

Strategic Techno-

logical

Structural Operational Cultural

Subject

Level

Course

Level

University

Level

Student Learning Characteristics

Student Learning Behaviours

Teaching

Curriculum Design

Learning Environment

Support

Org Design Metrics

and

Methods

Agents

Drivers of Student Success

Emergent

Feedback

and

Reporting

Student

Success:• Quality

learning

• Achievement

of goals

• Retention &

progress

• Wellbeing

Organisational Dynamics

B. Unpacking the CSU Learning Analytics Model

DIVISION OF STUDENT LEARNING

Strategic Techno-

logical

Structural Operational Cultural

Subject

Level

Course

Level

University

Level

Student Learning Characteristics

Student Learning Behaviours

Teaching

Curriculum Design

Learning Environment

Support

Org Design Metrics

and

Methods

Agents

Drivers of Student Success

Intervention, Adaptation and

Evaluation

Emergent

Feedback

and

Reporting

Student

Success:• Quality

learning

• Achievement

of goals

• Retention &

progress

• Wellbeing

Organisational Dynamics

B. Unpacking the CSU Learning Analytics Model

DIVISION OF STUDENT LEARNING

B. Unpacking the CSU Learning Analytics Model

Questions?

DIVISION OF STUDENT LEARNING

References

Bain, A., & Parkes, R. J. (2006). Can Schools Realise the Learning Potential of Knowledge

Management? Canadian Journal of Learning and Technology, 32(2).

Diaz, V., & Fowler, S. (2012). Leadership and Learning Analytics. Educause Learning Initiative

Brief, November 2012, pp. 1-4. Retrieved from:

http://www.educause.edu/library/resources/leadership-and-learning-analytics

Ferguson, R., (2012). Learning analytics: drivers, developments and challenges. International

Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317.

Long, P & Siemens, G. (2011). Penetrating the Fog: Analytics in Learning and Education.

Educause Review, September/October 2011, pp. 31-40. Retrieved from:

http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education

Tynan, B., & Buckingham Shum, S. (2013). Designing Systemic Learning Analytics at the

Open University. Retrieved from: http://www.slideshare.net/sbs/designing-systemic-

learning-analytics-at-the-open-university

DIVISION OF STUDENT LEARNING

Thank You

Simon Welsh (Senior Learning Analytics Officer): siwelsh@csu.edu.au

Assoc Prof Philip Uys (Director, Learning Technologies): puys@csu.edu.au

Nov 2014

Slides available from: http://www.slideshare.net/puys/connecting-analytics-theory-and-

the-organisation