Can you Leverage Data to Understand Student Behaviour and Performance?
From class attendance, examination grades to entry results, educational institutions have a wealth of student data waiting to be unlocked. Can you identify high risk students to design learning interventions? Are you able to find patterns in students’ behaviour to customise learning experiences? Can you provide real-time recommendations based on students’ abilities and learning needs?
Acquire Fundamental Analytics Techniques to Improve Learning Outcomes
Join this 2-day introductory workshop to develop a practical understanding of the latest theories, tools, techniques and strategies in learning analytics. Gain insight into the applications, use cases and what makes successful organisations do learning analytics right. Understand the fundamental concepts of data science and learn how to work with predictive analytics models and basic algorithms to improve learning. Find out how to apply statistical learning models and methods for student profiling, experience and retention. Examine how to analyse complex charts and create clear data visualisation dashboards for personalised feedback.
- The power and purpose of learning analytics
- Applications and case studies of learning analytics
- Data, technology, privacy and ethics considerations
- Data science fundamentals behind learning analytics
- Applying predictive analytics in a learning environment [Hands-on exercise]
- Data visualisation dashboards for personalised feedback
What You Will Learn
- What your learning analytics capability needs to achieve
- Key concepts and trends in learning analytics
- Use cases and real-life examples of learning analytics
- How to create scalable, interactive and actionable learning analytics capabilities
- How to collect, input and visualise learners and institutional data
- How to effectively organise your data and dashboard
- Data science and analytics strategy fundamentals
- How to create learning analytics solutions using data science and predictive analytics
Hands-on Exercises for Practical Learning
- Identifying different data sources and determining commonalities
- Understanding the class make-up, student profiling and grouping students in clusters
- Building a predictive analytics model based on the specific characteristics of the different clusters
- Providing feedback to students and designing effective intervention for student success
Felipe is a leading advanced analytics and data science partner, working with teams in a range of different organisations and helping them build, manage and enhance their data science capabilities. Felipe is also an analytics instructor with experience disseminating practical, actionable analytics and data visualisation techniques in both classrooms and online settings.
When Felipe is not partnering with clients or helping students, he’s a research candidate in Learning Analytics at The University of Sydney. As part of his research, Felipe makes sense of students’ digital traces and looks at the role learning analytics dashboards play in influencing learning outcomes. His research has also been focused on exploring patterns of students’ engagement and performance profiles in learning environments. Alongside all this, Felipe is also a blogger, writing regularly on a wide range of topics including data science, learning analytics, predictive analytics, statistical learning and data visualisation.
Recognised internationally for his thought leadership, Felipe received over 62,000 visitors to his blog from over 180 countries last year and some of his articles have been ranked #1 in Google search. Felipe is widely referenced by many sources and leading educational institutions including StackOverflow, Udacity, Western Michigan University, UC Santa Barbara and Edinburgh Napier University among others.
Who Should Attend
Lecturers, teachers, professors and professionals working in educational institutions looking to use data to improve learning outcomes. No experience in data analytics is required.
Participants are required to bring along laptops for the hands-on exercises.
Session 1: Understand the Power and Purpose of Learning Analytics
- History of learning analytics and adjacent topics, recent developments and future outlook
- Learning analytics as a decision-making engine for educators and institutions
- Knowing your why and what your learning analytics capability needs to achieve
Session 2: Implementing a Learning Analytics Capability
- Making sense of your educational institution’s capacity to build learning analytics
- Plotting a roadmap from conception to execution of learning analytics experiences
- In-depth understanding on what makes successful organisations do learning analytics right
Session 3: Applications and Case Studies of Learning Analytics
- Discuss and illustrate main applications of analytics to improve learning
- How predictive analytics, adaptive learning and feedback are applied in learning analytics
- Practical interactive activities exploring use cases of learning analytics
Session 4: Data, Technology and Privacy for Successful Learning Analytics
- Overview of data, technology, privacy and ethics issues in a learning analytics environment
- Managing data and technology effectively and the importance of tool selection and usage
- Fundamentals for data manipulation in the context of learning analytics
Session 5: Data Science Fundamentals in the Context of Learning Analytics
- Explore fundamental concepts of data science behind learning analytics
- Understand latest concepts in data science powering learning analytics solutions
- Working with predictive analytics models and basic algorithms in learning analytics
Session 6: An Exercise of Predictive Analytics in a Fictitious Learning Environment
- Introduce and work through elements of a data science project with a fictitious example
- Explore and apply statistical learning models and methods for learning analytics
- Building analytics for student profiling, experience and retention strategies
Session 7: Learning Analytics Feedback through Data Visualisation Dashboards
- Selecting the right visualisation for feedback through dashboards
- Working with complex charts and data visualisations
- Creating a clear and accessible data visualisation model
Session 8: Learning Analytics in Action
- Revisit main themes, tools, techniques and strategies
- Build a practical action plan to apply learning analytics to your institution or classroom
- Group discussion, final reflections and insights