Learning Analytics act as custodians of RMIT’s data for education, who gather, analyse and present data to enhance the learning and teaching experience.
Types of work
This process drives improvements across the University by ensuring decisions are supported with the right data at the right time. It involves five core types of work:
Facilitative
Driving data governance policies and procedures that ensure the integrity and quality of data throughout its lifecycle and protect student confidentiality.
Descriptive
Administering surveys, connecting data systems and fulfilling data requests to provide a better understanding of the student journey and to highlight areas for improvement.
Diagnostic
Using data to identify patterns, set key performance indicators and drive decision making to increase student engagement, satisfaction and support.
Prescriptive
Applying the insights gained from data analysis to allocate resources where they will have the highest impact on the student experience.
Predictive
Analysing data patterns to predict the student groups that are in the most need of support and providing targetied resources
Eight key principles
All work undertaken by our team is framed by eight key principles, which ensure the integrity and transparency of our data analysis process.
These principles act as a reminder to engage students in the feedback cycle, so that our work can be targeted to actively improve the RMIT student experience.
The principles also form the basis of our data governance policies and procedures, which define clear strategies for data storage, management and implementation.
- Learning analytics is a justified and ethical practice that is core to organisational principles.
- RMIT University has a responsibility to all stakeholders to use and extract meaning from student data for the benefit of students where feasible
- Learning analytics contributes to equitable and inclusive participation in education by providing data in support of quality learning and teaching, and student centred practice.
- Students should not be wholly defined by their visible data or our interpretation of that data.
- The University (and its employees) will be transparent with regard to the collection, analysis and use of data from student and learning and teaching systems.
- Students should be engaged as active agents in the implementation of learning analytics (e.e. informed consent, personalised learning paths, interventions).
- Modelling and interventions based on analysis of data should be sound and free from bias.
- Adoption of learning analytics within RMIT University requires broad acceptance of the values and benefits (organisational culture) and the development of appropriate skills across the organisation.
Research
Lifen Sudirjo, Akshay Sharma & Pablo Munguia 2018
Graduate feedback plays an important role in determining whether institutions are producing ‘work ready’ graduates. This presentation compares data from graduate surveys conducted five-months and three-years post-graduation to gain insights into RMIT graduate satisfaction levels and employment outcomes.
Amelia Brennan, Akshay Sharma & Pablo Munguia 2018 (in press)
A common use of technology in higher education is the provision of online course materials, invoking an investigation of the ways in which students engage with online course content, and how their participation changes over time.
Sarah Taylor & Pablo Munguia 2018
Data solutions in the teaching and learning space are in need of pro-active innovations in data management, to ensure that systems for learning analytics can scale up to match the size of datasets now available.
Amelia Brennan, Christina Peace & Pablo Munguia 2018
Teaching face-to-face is still a major education mode in many universities, yet institutions are increasingly tasked with improving efficient use of teaching spaces.
