Agenda Day 2
8:00 AM - 8:30 AM COFFEE & REGISTRATION
8:30 AM - 8:40 AM Conference Opening – Remarks from the Conference Chairperson
8:40 AM - 9:20 AM Innovating Analytics for Institutional Impact & Future PedagogySimon Buckingham Shum , Professor of Learning Informatics & Director of the Connected Intelligence Centre, University of Technology Sydney
University of Technology Sydney’s Connected Intelligence Centre (CIC) designs human-centred analytics tools with the specific mission to advance UTS teaching and learning. CIC is a multidisciplinary centre reporting to the DVC Education & Students, aligned with the UTS Data Science research priority, working closely with faculties and IT to design practical tools. Some tools break new technical and conceptual ground, while others simply deliver better information to the people who need it. This presentation will include:
- Organisational conditions enabling CIC’s creation and operation
- Ethical Design Critique workshops for rapid feedback on analytics dashboards
- From data dashboards, to analytics for 21st Century Competencies such as critical, reflective writing, and face-to-face collaboration.
Simon Buckingham ShumProfessor of Learning Informatics & Director of the Connected Intelligence Centre
University of Technology Sydney
9:20 AM - 10:00 AM Using Learning Analytics to Discover How Students Engage With Active LearningAbelardo Pardo, Associate Professor, University of Sydney
Educational institutions are supposedly collecting detailed information about their students. This data has the potential to inform decision making processes at various levels. However, using data at the level of a course while it is being delivered poses several additional hurdles for its mainstream adoption. In this session Abelardo will explore:
- A framework to obtain knowledge about how students participate in a course designed with an active learning approach
- The new dependencies that appear at the design and delivery stages with respect to data collection procedures, analysis, reporting, and actions
- The key challenges faced for adopting learning analytics processes
Abelardo PardoAssociate Professor
University of Sydney
10:00 AM - 10:40 AM Establishing Stakeholder Buy In and Ensuring Convergence Between Multiple Learning Analytics StakeholdersDavid Gibson, Director of Learning Futures and UNESCO Chair of Data Science in Higher Education Learning and Teaching, Curtin University
As the UNESCO Chair for Data Science in Higher Education, David’s goal is to share knowledge on how to use data science to improve learning and teaching and gather community support and buy in. Similarly, as Director of Learning Futures at Curtin University this session will look into global and domestic case studies in the learning analytics sphere.
- Curtin University: Developing automated feedback through Blackboard and Moodle for learners to help them make their next decision
- Enabling a holistic approach and setting aside frameworks, targets and policies to have a unified view of the learning analytics vision across the educational institution
- Establishing stakeholder buy in: how do you build long term cultivation efforts at senior executives, operations, IT, infrastructure and operational levels and ensure there is convergence
- Case study of cluster analysis approach for retention and predictions
David GibsonDirector of Learning Futures and UNESCO Chair of Data Science in Higher Education Learning and Teaching
10:40 AM - 11:00 AM Speed Networking
11:00 AM - 11:30 AM MORNING TEA
11:30 AM - 12:10 PM ‘Learning Centred’ Analytics:Cassandra Colvin, Manager of Learning Analytics, University of South Australia
The University of South Australia is a leader in the field of learning analytics. In a short period of time it has rolled out an impressive suite of reports that capture students’ online learning behaviour. However, recent attention has moved to broadening the focus of its learning analytics activities to provide insights. A particular focus is understanding how context, and students’ cognitive and affective processes, mediate student learning. The university has developed a multi-theoretical conceptual model of learning that it is using to inform data collection and analysis. It is believed that the addition of affective, cognitive and contextual data to existing data sets will elicit deep, nuanced insights into student learning and engagement, and the various affordances and constraints of positive student outcomes.
- What insights can learning analytics glean into cognitive and affective processes (eg self regulation, self efficacy, stress and well being), and relationships between these constructs and learning outcomes
- Identifying indicators of change and marrying together drivers of learning analytics for quality assurance and evaluation
- Understanding relationships between students’ sociocultural, academic and linguistic capital, affect, cognitions, and behaviour.
Cassandra ColvinManager of Learning Analytics
University of South Australia
12:10 PM - 12:50 PM Solutions Clinic
Solutions clinics are designed to give participants strategies and tools to deal with their most pressing challenges. The room will split into tables, where each table member will share their biggest challenge, before collectively deciding which issue to investigate in more detail.
The table will then elect a facilitator/note taker who will record the topic being explored and the ideas of the table. After 40 minutes of discussion, the facilitator of each table will then report back to the rest of the audience their problem and potential solutions.
12:50 PM - 1:50 PM LUNCH BREAK
1:50 PM - 2:30 PM Heightening Engagement with Online Platforms to Increase Retention RatesSebastian Rosien, Head of Online and E-Learning, Australian Institute of Business
The Australian Institute of Business has come onboard with creating an engaging learner experience to drive student progression and satisfaction. They have found that students are not engaging with content and as a result are using analytics to understand student behaviours and aid in increasing participation.
- User flow analytics to gauge engagement levels and at what point students drop off
- Creating tailored and relevant online content to reduce disconnect with online platforms and underpin the student learning experience
- How to future proof so you can continuously add incremental value through analytics
Sebastian RosienHead of Online and E-Learning
Australian Institute of Business
2:30 PM - 3:10 PM Learner Facing Learning Analytics as an Enabler of Metacognition and Lifelong LearningKirsty Kitto, Senior Lecturer, UTS, Connected Intelligence Centre
Early work in learning analytics focussed upon risk and retention, but it is important that we start using educational data to help students of all abilities understand their strengths, weaknesses, and how they might improve. The quantified self movement shows that people have a keen interest in using personal data to interpret (and perhaps change) ongoing patterns of behaviour. How can this be incorporated into learning analytics?
- Enabling learners to explore and make sense of the digital traces that they leave in learning data
- Developing data literacy in students to encourage reflection, metacognition and self directed learning
- Exporting personal learning data from institutional silos to help people build lifelong learning data stores that provide rich evidence of skills and capabilities in the workforce
Kirsty KittoSenior Lecturer
UTS, Connected Intelligence Centre
3:10 PM - 3:40 PM AFTERNOON TEA
3:40 PM - 4:20 PM A journey of few weeks learning online – what drives learning and who has the final say on ‘my’ experience?Catherine Zhao, Catherine Zhao - Student (MA. Ed Psy), The University of Sydney
The use of learning analytics (LA) is often for universities and researchers to understand what students might have been through in a fully online learning episode (e.g. MOOCs). To derive useful and actionable points from the analytics, aspects that directly matter to learning, memory and thinking should be considered and a rethink of how to evaluate this learning experience. This presentation turns the table and provides a unique perspective from a ‘student’ who has worked in the LA field, in order to address the question that concerns ‘quality’ of online learning.
- What are the influences of prior experience and motivation on learning behaviours?
- Is promoting self-regulated learning the answer to sustain engagement?
- What can be done to enhance learning, encourage thinking, and to help forming memories in a duration of 6~8 weeks of a MOOC?
- What should be measured, and when, to evaluate this learning experience?
Catherine ZhaoCatherine Zhao - Student (MA. Ed Psy)
The University of Sydney
4:20 PM - 5:00 PM Using Learning Analytics to Empower Students and Provide Personalised AssistanceNicc Ryan, Student Engagement Manager – Faculty of Science and Engineering, Curtin University
At Curtin University they are using a blend of learning analytics, predictive analytics and student profiling. With a focus on analytics for upping engagement to increase positive student experience, Nicc will share the key findings from a survey on 500 first year students and their university experience.
- Setting a baseline for retention and attrition statistics
- Working with marketing to connect the transition experience
- Engaging students early before they become disengaged and drop off the radar
- How to use analytics to look at student behaviour to retain 2nd year students onwards
Nicc RyanStudent Engagement Manager – Faculty of Science and Engineering