www.mq.edu.au/educationstudio

The Learning Genome

Introduction

Is there a Learning Genome?

Are there a set of factors we can point to which are clear indicators of student success?

Can these be found amongst the sea of data we collect about our students?

Can these be used to recommend customised pathways of success for our students?

How can they be used to enhance learning design, teaching and the student experience?  The purpose of this project is to apply techniques and technologies to open opportunities and enrich the learning of our students. We want to find out if, based on the data we have about our students, we can make suggestions (or recommendations) as to what actions the University should take and students  should do to become the best they can be.

Expected Outcomes/Progress

•Identification of algorithms to achieve associative and analytical outcomes

•An understanding of the issues and implication (both technical and ethical) related to the handling of large teaching •An understanding of how the findings can be used to open opportunities and enhance the student experience

•Establishment of a community of practice within and external to Macquarie

•Exploratory models for mining and interpreting data which will be useful for both MQ Analytics and the wider higher education community

•Scholarly outputs in the form of academic papers and conference presentations

•An ongoing agenda for the development of learning analytics at Macquarie

Aims 

•To empower and inform staff of the ways to use smart-data to inform their teaching

•To illustrate the kinds of data that can be made available to revolutionize teaching at this university

•To empower students with knowledge and information to make informed decisions that will open opportunities and maximise their learning

•Substantially improve the learning opportunities and educational results of our students at Macquarie

•Provide students, academics and academic advisors with knowledge and information to make informed decisions that will open opportunities for students to maximise their learning.

Find out more

Email us:Learning_genome@mq.edu.au

Visit us online: http://mq.edu.au/research/centres_and_groups/learning_genome

Mauricio Marrone Burgoa, Jamie Gabriel, Maree Gosper, Gary Lau, Vanessa WarrenInnovation & Scholarship Program Grant

Approach

Technical - to explore analytical approaches, accessing and interrogating both structured and unstructured datasets. Working with MQ analytics to update knowledge of state of the art techniques, developing the skills and protocols to aggregate and interrogate data and undertaking data analysis are within this stream.

 Educational interpretation and

implication – to explore the possible interpretations of the data, their application and the short and long term issues and implications that will emerge are the focus of this stream. This will encompass a literature review to identify current practice, identification of ethical issues, focus groups of stakeholders to review findings and explore implications.

 Establishment of a community of practice

– to develop networks within and outside Macquarie. Learning analytics is attracting enormous interest at the moment and it is essential that we are part of the wider community of practice.

Figure 1. Data Never Sleeps

Figure 2. Companies that use Recommended Algorithms

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Acknowledgements

This project has been funded through the Innovation and Scholarship Program, Macquarie University.