2014 09 uys direction and mapping

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DIVISION OF STUDENT LEARNING

Learning Analytics at CSU: Direction and Mapping

Assoc Prof Philip Uys (Director Strategic Learning and Teaching Innovation) [email protected]

Presentation at the CSU Learning Analytics Symposium Sept 2014 Slides available from

http://www.slideshare.net/puys/2014-09-uys-direction-and-mapping



Acknowledgements• Learning Analytics Working Party, CSU

• Director, Strategic Learning and Teaching Innovation, Division of Student Learning (Convenor)

• Director, Planning and Audit (or nominee)

• Dean of Students (or Nominee)

• Executive Director, Student Administration (or nominee)

• Executive Director, Division of Information Technology (or nominee)

• Executive Director, Library Services (or nominee)

• Director of Smart Learning Project (or nominee)

• The Associate/Sub-Deans Learning and Teaching from each Faculty

• Academic Secretary & Manager, Office of Academic Governance

• Senior Learning Analytics Officer, Strategic Learning and Teaching Innovation, Division of Student Learning

• uImagine?

• Simon Welsh, Senior Learning Analytics Officer, SLTI, CSU



Overview

A. Concepts of learning analytics at CSU

B. Learning analytics developments at CSU

C. Learning analytics principles at CSU

D. Unpacking the CSU model for learning analytics



A. Concepts of learning analytics at CSU



Learning analytics is 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

Note that the learner context referred to above includes relevant computer systems, learning experience design, the role of teaching staff as well as learning and teaching support staff.



• Learning analytics and academic analytics• Academic analytics is the improvement of

organizational processes, workflows, resource allocation, and institutional measurement through

the use of learner, academic, and institutional data. Academic analytics, akin to business analytics, are concerned with improving organizational

effectiveness.

(Siemens, G., Long, P. (2011). Penetrating the Fog: Analytics in learning and education. EDUCAUSE Review, vol. 46, no. 4 (July/August 2011))



• Limitations• learning is a complex social activity and technical

methods do not fully capture the scope and nuanced nature of learning

• Large % of learning occurs off-line

• Proximity to drivers of student success on all levels critical

• Distributed over internal and external technologies



• Areas• Analytics around social interactions;

• Analytics around learning content;

• Analytics in different spaces;

• Analytics on interaction with the university system;

• Analytics on intervention and adaptation

(George Siemens, 2012)



• Adaptation • Action critical and ethical

• LA can be provided to the student, teaching staff, student support staff, teaching support staff, and administrators to support adaptive practice and adaptive systems

• Ultimately about adaptation of design, behaviour and systems

• Personalisation of learning



• Agency• Not “audiences” but agency

• Staff and students

• Active, not passive

• As part of normal work



• Benefits- Reduce attrition through early detection of at-risk students and

generating alerts for learners and educators.

- Personalize and adapt learning process and content, ensuring that each learner receives resources and teaching that reflect their current knowledge state.

- Extend and enhance learner achievement, motivation, and confidence by providing learners with timely information about their performance and that of their peers, as well as providing suggestions on activities and content that address identified knowledge gaps.

- Makes better use of teacher time and effort by providing information on which students need additional help, which students are candidates for mentoring others, and which teaching practices are making the biggest impact.



• Benefits- Higher quality learning design and improved curriculum

development processes through the utilization of data generated during real time instruction and learning activities.

- Interactive visualizations of complex information will give learners and educators the ability to “zoom in” or “zoom out” on data sets, depending on the needs of a specific teaching or learning context.

- More rapid achievement of learning goals by giving learners access to tools that help them to evaluate their progress and determine which activities are producing the best results.

(Siemens et al (2011). Open Learning Analytics: an integrated & modularized platform)



• Indicators of LA success at CSU• Increase in student success.

• Increase in the quality and effectiveness of online learning as per assessment results.

• Increase in the quality and effectiveness of online teaching as per Student Experience Survey due to adaptive online teaching practice and/or adaptive online systems.

• Increase in student retention rates through more effective interventions either automated or human.

• Increase in online engagement due to feedback on learning practices.

• Increase in the appropriateness of subjects selected by students.



Increasing public accountability and

transparency

Decreasing Funding

Increasingly Distal Student

Relationship

Increasing expectations by

students

Successful use of analytics within

Higher Ed & other sectors of society

Increasing Competition

Student Success at

scale

Increase student

retention and progress

Evidence-based professional learning of

teaching staff

• Drivers



B. Learning analytics developments at CSU

• LA Working Party (started 2013)

• LA Strategy (middle 2013)

• At risk students (Planning and Audit)

• Student Responsiveness Rating

• Smart learning (currently feedback on design)

• Framework, Model and Plan (under development)

• Theoretical model of student engagement & pilot



Assessment Performance

Other Performance Factors

Performance Approach Goals

Learning

Cognitive Involvement

Behavioural Involvement

Positive Affect

Situational Interest

Individual Interest

Dispositions

Theoretical model of student engagement

2. Learning Network Analytics

1. Learner Profile Diagnostics

4. Learning Point Feedback

3. Assessment Analytics

Proximal Learning

Indicators

Learning

Affordances



• LA Working Party (start 2013)

• LA Strategy (middle 2013)

• At risk students (Planning and Audit)

• Student Responsiveness Rating

• Smart learning (feedback on design)

• Framework, Model and Plan (under development)

• Theoretical model of student engagement & pilot

• BB (Interact2) Analytics - coming

• Roles: P&A; DSL; Library; DIT; Smart Learning; uImagine

• Governance: LAWP reporting to the CLTC



C. Learning analytics principles at CSU

• Broad stakeholder engagement• Proximity• Student success• People focus• Ethics



• Student success is the main focus of LA at CSU - a combination of

• quality learning

• achievement of (personal) goals

• retention and progress

• wellbeing



• People focus• Ultimately about people: students and staff => respect

• Empower and motivate

Photograph: Alamy



• Ethics



• Ethics• Use data for what it is collected for

• Guarantee confidentiality and privacy

• The trust principle underlies LA

• Uni can be seen as the “digital Big Brother”

• How much info to provide to the student

• Will LA influence grading?

• The accountability to act on what has been collected



D. Unpacking the CSU model for learning analytics in higher education



Student Success:• Quality

learning•

Achievement of goals

• Retention & progress

• Wellbeing

Focussed on student success



Subject Level

Course Level

University Level

Student Learning Characteristics

Student Learning Behaviours

Teaching

Curriculum Design

Learning Environment

Support

Drivers of Student Success


learning•



• Wellbeing

Six domains and three levels



Strategic

Techno-logical

Structural

Operational

Cultural

Subject Level

Course Level

University Level



Teaching

Curriculum Design


Support

Org Design Metrics

and

Methods

Agents


Emergent Feedback

and Reporting


learning•



• Wellbeing

Organisational Dynamics



Strategic

Techno-logical

Structural

Operational

Cultural

Subject Level

Course Level

University Level



Teaching

Curriculum Design


Support

Org Design Metrics

and

Methods

Agents


Intervention, Adaptation and Evaluation

Emergent Feedback

and Reporting


learning•



• Wellbeing

Organisational Dynamics



References

Siemens, G. (2012). Learning analytics: new insight or new buzzword? ACODE webinar. October 2012

Siemens G., Dawson, S., Lynch, G (December 2013).

Improving the Quality and Productivity of the Higher Education Sector

Policy and Strategy for Systems-Level:Deployment of Learning Analytics

Society for Learning Analytics Research (SoLAR)

http://www.solaresearch.org/



We have started the journey at CSU...

and there are many more hills to climb!



Thank You

Assoc Prof Philip Uys (Director Strategic Learning and Teaching Innovation) [email protected]

Sept 2014 Slides available from

http://www.slideshare.net/puys/2014-09-uys-direction-and-mapping