1. Initiating a culture of learning analytics literacy Garry Allan
RMIT University ACODE 70

2. Institutional Context: Shapes what is meaningful and appropriate
Using an implementation framework
From the OLT Project: Learning Analytics: Assisting Universities with Student Retention - Charles Darwin University
Targeted dialogue and change within the cultural context
What motivates change in your institutional context?

3. Splitting the ‘minefield’ in two
To progress understanding and uptake within the organisation, conceived in two components:
Academic Analytics: student characteristics, enrolment behaviours, and academic outcomes, within the institution, or at a national or international level.
Learning Analytics: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.
Significant Business Intelligence initiative - SAP Business Objects
Establishing a data steward role with central oversight of Learning and Teaching data.
Utilising a University-wide team of data stewards that are change agents for the promulgation and uptake of L&T reports.

4. Building analytics literacy within a familiar context: Enabling staff with the use of analytics tools in the LMS
Staff are directly interested in how analytics data on student performance can assist them.
Introduction of: Instructor Learning Analytics www1.rmit.edu.au/teaching/technology/learninganalytics
Using the standard tools in the LMS (Blackboard) to advance a staff culture of default use of verifiable data to better understand how students engage with and interact within the LMS:
Informing through examples of use
Listing of good practice.
Retention:
Focus on simple message with retention
Identification of basic engagement (logon) interact with generalised support text.
Culture - Challenges in building University-wide.

5. Commencing institutional dialogue on analytics for prioritisation and reporting
Necessarily requires behavioural change of users, therefore commencing with a low ‘barrier to entry’
Focus on prioritisation based on Academic Analytics, facilitating a culture of understanding the current context through the lens of data-informed decision-making
Avoiding a “data deluge”
Using visual forms and simplified communication to ensure basic analytics literacy
Discipline-based capability variation

6. Academic Analytics

7. Ethics and data release/reporting practices
Learner data becoming a currency of value to Universities (and third party providers)
As Learning Analytics strengthens as a university L&T practice, learner and educator data is of value both to our institutions and to third party organisations. Aggregation of the data will be a priority.
The importance of a staff body that is informed and engaged with data practices and the risks associated with abuse of the ethical management of both student and staff data.

8. References Charles Darwin project www.LetsTalkLearningAnalytics.edu.au
Business Intelligence at RMIT
www1.rmit.edu.au/browse;ID=8b5xl7g3q57n1
RMIT - Instructor Learning Analytics
www1.rmit.edu.au/teaching/technology/learninganalytics