Super Early Bird Fee
Register and Pay
by 18 Jan ’19
Early Bird Fee
Register and Pay
by 15 Feb ’19
Regular Fee
Register and Pay
after 15 Feb ’19
Singapore-registered companies $1,813.65 (SGD) $1,920.65 (SGD) $2,027.65 (SGD)
Non Singapore-registered companies $1,695 (SGD) $1,795 (SGD) $1,895 (SGD)

Group Discount!
Enjoy 10% off when you register for 3 or more OR
For groups of 3, 4th comes for free

IMPORTANT NOTES
  1. Fees for Singapore-registered companies are inclusive of GST.
  2. Super Early Bird and Early Bird promotion: Discount will only be valid if payment is received by the stipulated date.
  3. Group Discount only applies to registrations from the same company registering at the same time, issued in a single invoice and of the same billing source.
  4. Only corporate registrations will be accepted.
  5. Bank charges & taxes are to be borne by registrants, if applicable.
  6. Full payment is mandatory upon registration for admission to the event.
  7. Walk-in delegates will only be admitted on the basis of space availability at the event and with immediate full payment.
  8. Fee includes lunch, refreshments and event documentation.
  9. The organiser reserves the right to make any amendments that it deems to be in the interest of the event without any notice.
  10. Information provided will be used for event administration and updates on upcoming events. For more details, please visit: http://www.conferences.com.sg/personal-data-protection-statement/

CANCELLATION & REPLACEMENT

A replacement is allowed if registered participants are unable to attend. For cancellations received in writing before 15 Feb 2019, a full refund will be given with a 10% administrative charge. For cancellations received in writing before 22 Feb 2019, a 50% refund will be given together with the event documentation. There will be no refunds for cancellations received after 22 Feb 2019 or “no show” participants. However participants will receive a copy of the event documentation.

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.


Programme Highlights

    • 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
 

Workshop Leader

Felipe Rego

Data Science & Analytics Partner,

Australia

ED1-Felipe Logo

 

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.

 

Agenda

  • 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