“The hands-on practice was useful even though it was conducted online. Furthermore, I really like the interactive small group discussion which Patrick gave a lot of practical suggestions for me to work on my learning project.”
Are you Effectively Leveraging Valuable Student Data to Identify Learning Gaps?
The increasing use of technology for teaching and learning has led to a significant amount of data being collected on how learning occurs in today’s world. To capitalise on this rich data, Learning Analytics has emerged in recent years as an important field that enables the measurement, collection, analysis and reporting of educational data for the purpose of understanding and optimising learning. But how can educators tap on multiple data sources to monitor and improve student performance?
“With data residing on multiple platforms, it’s difficult to analyse data holistically.”
“How do I clean up the data for accuracy?”
“Lack of software skills is my main challenge.”
“How do I translate data analysis into teaching interventions?”
Extract Value-Adding Teaching & Learning Knowledge with Analytics and Data-Driven Insights
Join this 3-day practical and interactive live virtual workshop to acquire applicable knowledge and practical skills in Learning Analytics (LA). Learn basic concepts and recent developments in LA. Conduct data exploration exercises and perform predictive modelling on different data mining tools. Discover basic data structures and techniques, including qualitative data analysis. Understand the importance of a powerful data story and how effective visualisation can be executed. Gain a big picture view of LA projects, including non-technical issues such as ethics and interpretation.
- Led by Dr Patrick Tran, Educational Developer at the University of New South Wales, Canberra, Australia
- Be introduced to and work with a wide range of free and commercial software for both quantitative and qualitative analysis, including:
- Microsoft Excel
- Orange Data Mining
- Microsoft Power BI
- Optional: Python Anaconda, Weka and Rapid Miner
- Be supported in your learning and ask questions about your own Learning Analytics projects with dedicated small group discussion session on Day 3 and optional afternoon consultation sessions
- Explore comprehensive materials including references for further reading, demonstration videos, practical workflows and customisable code templates
Benefits of Attending
- Discover and build on foundational concepts of LA and critical data skills
- Apply practical data science skills to processing, analysing and visualising educational data
- Uncover the different sources of educational data and the variations of LA techniques
- Touch on recent developments in the field and an overview of the LA cycle
- Takeaway basic data structures, operations, predictive modelling skills and qualitative analysis
- Understand the prominence of machine learning and its close relations to predictive LA
- Be introduced to powerful visualisation tools to design graphs and tell data stories
- Analyse non-technical issues such as ethics, privacy and the dangers of data interpretation
- Hear potential pitfalls and caveats in using LA and best practices in managing LA projects
- Work on a final LA project to solve a LA problem using the skills and tools covered
Dr. Patrick Tran
Educational Designer & Developer, Learning & Teaching Group,
University of New South Wales (UNSW) Canberra, Australia
Patrick is a data scientist by training, instructional designer by choice and educator at heart! His main interests lie at the intersection of innovation, technology, and leadership. Patrick received a PhD in computer science for a thesis on improving performance of network Intrusion Detection Systems using Machine Learning. He is also a certified PRINCE2 project manager with an MBA in Accounting and Finance.
Through applied research and teaching, Patrick seeks to leverage data analytics to drive pedagogical decisions, revamp learner experience and revolutionize existing approaches to learning and teaching. He is currently working at the confluence of educational technology, learning analytics and educational research at University of New South Wales (UNSW) Canberra. His expertise lies in the domain of learning analytics, but Patrick has had a vast experience with all corners of the academic world, including research, teaching, academic program management and instructional design.
In the education space, Patrick has a wide range of research interests that are centred around learners such as:
• Learning Analytics and Educational Data Mining
• Educational Technologies
• Learning Theories
• Inventive problem solving
• Foresight – futures studies
Prior to UNSW, Patrick has held various positions in both teaching and management capacities at University of Technology Sydney, Victoria University and Australian National University. Over the years, he undertook several technology-enabled learning initiatives that involved analysing learners’ interaction data with intelligent tutoring systems and developing early intervention strategies for their success.
Past Delegate Testimonials
“Patrick is definitely an expert in his domain and generous in sharing his expertise and skills to us.”
“Dr Tran’s sharing of his past experience is insightful for people who do not have any prior knowledge on LA.”
Who Should Attend
Lecturers, Teachers, Professors, Academics looking to implement learning analytics. No prior experience in data analytics is required.
Log-in Time: 8.50am*
Day 1 & 2: 9.00am – 1.00pm* (There will be short breaks allocated at appropriate intervals.)
Day 3: Intimate Group Discussion between 9.00am – 1.00pm*
*Time stated in local Singapore time.
Day 1 – Learning Analytics (LA): The Basics and Beyond
On Day 1, you will learn the basic concepts and recent developments in LA. This is followed by a practical session on data exploration and predictive modelling with various data mining tools. There will be an optional support session at the end for the tutorial problems.
Session 1: Introduction to Learning Analytics
This session explains what LA is and why it is widely used by institutions today. We will look into different sources of educational data and LA techniques.
- An overview of LA: the what and the why
- Educational data
- LA techniques and recent developments
- The LA Cycle
Session 2: Analysing Educational Data
This session covers basic data structures and techniques used to manipulate them. We will introduce Machine Learning as a prominent approach to predictive LA. We then explore several modelling techniques and related tools.
- Introduction to data structures and basic data operations
- From LA to Machine Learning
- Predictive modelling techniques
- Qualitative data analysis
Day 2: Learning Analytics (LA) in Practice
Day 2 begins with cutting-edge data visualisation tools, followed by a discussion on what it takes to implement a successful LA initiative and a final group project. There will be an optional support session at the end for the tutorial problems.
Session 3: Visualising Educational Data
This session introduces intuitive yet powerful specialised tools for visualising your data. You will practice a hands-on and visual approach to designing graphs and data stories. This is a great way to present your analysis and findings to non-technical audience.
- Data visualisation with MS Power BI
- Visual analytics with Tableau
Session 4: LA in Practice
This session discusses the non-technical key issues of LA and their impacts on individuals and the society as a whole. This covers potential pitfalls and caveats in using LA and the best practices in managing LA projects. You will work as a team on a final project to solve a LA problem of choice using the skills and tools covered in this workshop.
- Key issues in the use of LA: Ethics, Data Privacy and Interpretation
- Managing LA projects
- Conclusion – Challenges and Directions
Day 3: Small Group Discussion Sessions
Participants in small groups are allocated a 1-hour Q&A session to ask questions regarding the contents covered or get advice for their own LA projects.