Super Early Bird Fee
Register and Pay
by 26 Jul ’19
Early Bird Fee
Register and Pay
by 23 Aug ’19
Regular Fee
Register and Pay
after 23 Aug ’19
Singapore-registered companies $1,813.65 (SGD) $2,027.65 (SGD) $2,241.65 (SGD)
Non Singapore-registered companies $1,695 (SGD) $1,895 (SGD) $2,095 (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. Super Early Bird and Early Bird promotion: Discount will only be valid if payment is received by the stipulated date.
  2. Group discount only applies to registrations from the same company, attending the same event in the same country location. Delegates must register at the same time and be of the same billing source. Only a single invoice will be issued.
  3. Only corporate registrations will be accepted.
  4. Bank charges & taxes are to be borne by registrants, if applicable.
  5. Full payment is mandatory upon registration for admission to the event.
  6. Walk-in delegates will only be admitted on the basis of space availability at the event and with immediate full payment.
  7. Fee includes lunch, refreshments and event documentation.
  8. The organiser reserves the right to make any amendments that it deems to be in the interest of the event without any notice.
  9. 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 23 Aug 2019, a full refund will be given with a 10% administrative charge. For cancellations received in writing before 5 Sep 2019, a 50% refund will be given together with the event documentation. There will be no refunds for cancellations received after 5 Sep 2019 or “no show” participants. However participants will receive a copy of the event documentation.

Can you use Data to Optimise Student Learning?

“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?”

Apply Learning Analytics in your Teaching & Learning

Join this 2-day hands-on workshop to acquire the latest methods, techniques and tools in learning analytics. Discover the multiple applications of learning analytics including adaptive learning, multi-model analytics and assessment analytics. Learn how to access and integrate data sources, prepare and clean the data for analysis. Acquire data analysis techniques including advanced statistical methods, sequential analytics and predictive modelling. Adopt visualisation methods and tools to make sense of data and learn how to translate data analysis into learning interventions.


Hands-on Practice Exercises

    • Defining problem statements to be addressed
    • Mapping your classroom’s learning design and analytics
    • Aligning learning analytics application with problem statements
    • Cleaning up data from Learning Management Systems (LMS) using Excel
    • Apply advanced statistical models to a learning task dataset on Excel
    • Using assessment data to predict student success
    • Creating data visualisation using Excel and Tableau
    • Developing a story from your data
    • Designing feedback messages for students using OnTask
    • Building an implementation and evaluation plan

    Unique Features

    • Led by Linda Corrin, Associate Professor for Transforming Learning at Swinburne University, Australia
    • Adopt free and paid tools including Excel, Tableau, SPSS, OnTask
    • Work with LMS data from Blackboard, Canvas, Loop
    • Practice on your institution’s dataset and receive personalised feedback

    Benefits of Attending

    • Understand and unpack the different levels of learning analytics: Institutional, Course and Task
    • Gain insight into a wide range of applications of learning analytics to support student learning
    • Define key problem statements to address and how to align specific applications and interventions
    • Uncover different sources of data available and how to access, integrate and prepare them for analysis
    • Learn advanced statistical methods, sequential data analytics and predictive modelling
    • Perform predictive modelling on assessment data to predict student success on Excel
    • Make sense of your data with a range of visualisation methods and tools for effective storytelling
    • Takeaway strategies to translate data analysis into learning and teaching interventions
    • Hear how to employ various implementation tools and systems within institutions
    • Build an implementation and evaluation plan for your learning analytics practice
 

Workshop Leader

Linda Corrin

Associate Professor, Transforming Learning,

Swinburne University of Technology, Australia

Associate Professor Linda Corrin is the Academic Director, Transforming Learning at Swinburne University of Technology, Melbourne, Australia. She has more than 18 years’ experience working in higher education providing support for educational technology, curriculum development, and assessment as well as teaching in the fields of education, business and IT. Linda holds bachelor’s degrees in Law and Information and Communication Technology (University of Wollongong), a Postgraduate Certificate in Learning and Teaching in Higher Education (University of Roehampton, London) and a PhD in Education (University of Wollongong).

 

Her research interests include learning analytics, students’ engagement with technology, feedback, and learning design. Currently, she is working on several large research projects exploring how learning analytics can be used to provide meaningful and timely feedback to academics and students. Linda is co-founder of the Victorian and Tasmanian Learning Analytics Network and a co-ordinator of the ASCILITE Learning Analytics Special Interest Group. She is regularly invited to talk about learning analytics around the world and has received several awards for her research in the field, including the inaugural 2018 Emerging Scholar award from the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE).

Who Should Attend

Lecturers, Teachers, Professors, Academics looking to implement learning analytics in the classroom. No prior experience in data analytics is required.

 

Agenda

  • Registration: 8.30am • Workshop: 9.00am – 5.00pm
    Morning, afternoon refreshments & lunch will be served at appropriate intervals.

  • Session 1: Understanding Learning Analytics

    This session will provide an introduction to learning analytics, including how learning analytics is defined, the evolution of the field, and the different levels at which it can be used in educational institutions. It will cover:

    • What is learning analytics?
    • Exploring the different levels of learning analytics (case studies):
      • Institutional (macro): Retention reporting and prediction
      • Course (meso): Student Relationship Engagement System (SRES)
      • Task (micro): Monitoring online discussions
    • Understanding the educational context and availability of data
    • Activity: Defining problem statements/questions to be addressed using learning analytics
    • Recognising the importance of pedagogy in interpreting learning analytics and designing interventions
    • Activity: Mapping learning design and analytics
  • Session 2: Applications of Learning Analytics

    There are many ways in which learning analytics can be used to support student learning. A range of these will be explored in this session through a series of case studies including:

    • Student retention
    • Curriculum design
    • Adaptive learning
    • Feedback
    • Writing analytics
    • Multi-model analytics
    • Assessment analytics
    • Activity: Aligning applications with problem statements
  • Session 3: Accessing and Integrating Data

    With the increase in the use of technology in learning and teaching there is a greater amount of data available for analysis. The challenge to educators is to determine which of these data are useful for understanding and enhancing student learning. This session will consider the data sources available for learning analytics and how these can be accessed and integrated.

    • Types of data available
    • Bringing together multiple data sources
    • Preparing data for analysis
    • Ensuring good data governance
    • Activity: Working with data from learning management systems (using Excel)
  • Session 4: Analysis Methods for Learning Analytics

    As the field of learning analytics evolves the range of analysis techniques also expands. In this session we will explore some of the key ways of working with student data including:

    • Basic statistics (using Excel)
    • Activity: Exploring LMS data with Excel
    • LMS Tools (using Blackboard, Canvas, Loop)
    • Advanced statistical methods (using Excel)
    • Activity: Applying advanced statistical methods to a learning task dataset using Excel
    • Sequential data analytics
    • Predictive modelling (using SPSS or Excel)
    • Activity: Using assessment data to predict student success (Excel/SPSS)
  • Session 5: Visualising and Making Sense Of Data

    When communicating the outcomes of data analyses, it is important to consider how the data can be most effectively visualised. This session will explore a range of visualisation methods and tools that can be used to make sense of data.

    • Types of visualisation
    • Visualisation design
    • Activity: Designing a visualisation (using Excel)
    • Traps to avoid when visualising data
    • Storytelling with data
    • Activity: Developing a story from your data
  • Session 6: Designing Interventions based on Learning Analytics

    The translation of data analysis into learning and teaching interventions is often challenging. This session will revisit the importance of learning design in designing interventions and explore different ways interventions can be delivered.

    • Linking analyses with learning design
    • Common types of interventions
    • Case study: Using OnTask to deliver feedback to students
    • Activity: Designing feedback messages for students using OnTask
  • Session 7: Implementing and Evaluating Learning Analytics

    While the field of learning analytics presents many exciting opportunities to use data to improve learning and teaching, many institutions are still in the early processes of implementing tools and systems within institutions. This session will explore some useful frameworks for the implementation of learning analytics as well as how learning analytics can be evaluated to ensure quality and impact.

    • Learning analytics implementation frameworks
    • Case study: Sheila Framework
    • Evaluating your learning analytics analyses/visualisation
    • Activity: Building an implementation/evaluation plan for learning analytics