Super Early Bird Fee
Register and Pay
by 5 Feb ’21
Early Bird Fee
Register and Pay
by 5 Mar ’21
Regular Fee
Register and Pay
after 5 Mar ’21
Non Singapore-registered companies $795 (SGD) $895 (SGD) $1,095 (SGD)
Singapore-registered companies (fees include 7% GST) $850.65 (SGD) $957.65 (SGD) $1,171.65 (SGD)


Group Discount!
Enjoy 10% off when you register for 3 or more OR
Register for 5 for the price of 4

  1. Super Early Bird & 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. 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. Participation is limited only to registered delegate and strictly no sharing of access to the Virtual Workshop. Pacific Conferences reserves the right to terminate the attendance if this is violated.
  5. Bank charges & taxes are to be borne by registrants, if applicable.
  6. Full payment is mandatory upon registration for access to the virtual workshop.
  7. Fee includes event documentation limited to presentation slides only.
  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:


A replacement is allowed if registered participants are unable to attend. For cancellations received in writing before 5 Mar 2021, a full refund will be given with a 10% administrative charge. For cancellations received in writing before 15 Mar 2021, a 50% refund will be given together with the event documentation. There will be no refunds for cancellations received after 15 Mar 2021 or “no show” participants. However participants will receive a copy of the event documentation.

In the event of a cancellation, a refund will be made via the original mode of payment and based on the original amount we received. Refund is made based on the prevailing exchange rate and Pacific Conferences shall not be responsible for any foreign exchange currency losses.

Can you use Data to Optimise Student Learning?  
Today’s education increasingly relies on technology to optimize institutional operations and more importantly, to enhance teaching and learning. With the widespread and long-lasting effects of COVID-19, online learning away from schools is now more prevalent than ever. Teaching & Learning in today’s landscape is becoming more digitalised as new technologies are introduced. Data, is a game-changing by-product with digitalised teaching activities. But harnessing that data for key insights can be challenging.  

What data sources to gather and how to go about it? 
How to prepare data for analysis and run predictive models? 
How to extract insights from existing dashboards?  

Elevate Teaching & Learning with Basic Analytical Techniques 
Join us for this practical and hands-on 3-day virtual workshop to learn more about tools you can adopt to extract valuable insights on students. Gain an overview of a multi-stage approach to Learning Analytics (LA) and be exposed to wide-ranging practical applications of LA. Map out LA projects and goal settings and also identify potential problems along the path. Conduct data exploration and modelling with Python. Tell compelling and informative stories with effective data visualisation. Experience a visual, hands-on, drag-and-drop approach to designing graphs and data stories.  

Unique Features

    • Led by Dr Patrick Tran, Educational Developer at the UNSW Canberra  
    • Experience advanced data extraction, exploration and modelling on Python with the assistance of Juypter Notebook 
    • Explore comprehensive materials including references for futher reading, demonstration videos, practical workflows and customisable code templates  
    • Get a tour of and practice with tools such as:  
      • Python  
      • Microsoft Excel 
      • Microsoft Power BI Dashboards 
      • Tableau 
    • Be supported in your exercises with daily afternoon consultation sessions   

Benefits of Attending

  • Understand key features in defining and implementing various LA projects  
  • Learn basic concepts of LA and a practical framework to follow when analysing educational data 
  • Explore Python programming language and how to scrap key course data from LMS webpages 
  • Gain basic data wrangling techniques on Python such as loading, processing and visualising data 
  • Apply advanced predictive modelling and other popular algorithms on raw data 
  • Experience a visual, hands-on drag-and-drop approach to designing graphs and data stories  
  • Uncover online dashboards for visualisation of data to a non-technical audience  
  • Distribute effectively visualised information and data on interactive mobile dashboards  
  • Touch on critical issues with utilising LA such as Ethics, Data Privacy and Interpretation  
  • Hear guiding principles, themes and processes when building and managing a LA project  


A Fully Immersive Virtual Workshop Experience

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 Hear LIVE presentation on leading case studies and common challenges  Get real-time answers to your questions throughout the workshop  Interact with like-minded professionals for dynamic exchange of ideas In-depth sharing during
1-hour intimate group discussion on Day 3
 Gain access to full workshop materials and handout



Workshop Leader

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. 

Who Should Attend

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



  • Log-in Time: 8.50 am
    Day 1-2: 9.00 am – 1.00 pm(There will be short breaks allocated at appropriate intervals.)  / Optional Consultation Session 2.00pm – 3.00pm
    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 of LA and a practical framework to follow when analysing educational data. This is followed by a practical session on data exploration and predictive modelling with Python packages. There will be an optional support session at the end for the tutorial problems. 

  • Session 1: Understanding Learning Analytics (LA)

    This session explains what LA is and why it is widely used by institutions today. We will dive into what involves in defining and implementing LA projects. 

    • An overview of LA: the what and the why. 
    • Education data: everything about learners and courses. 
    • Recent developments and future outlook of LA. 
    • LA projects: problem identification and goal settings. 
    • A typical LA framework: how to approach your LA projects. 
  • Session 2: Data exploration and modelling with Python

    This session introduces basic data structures and techniques used to manipulate them. We will learn how Python can help with loading, processing and visualising data which makes up the first step of data analysis: data exploration and preparation. We then explore several modelling techniques, starting from classical models to more advanced machine learning algorithms.  

    • Introduction to data structures and basic data operations. 
    • Basic data wrangling and programable data visualization with Python. 
    • Predictive modelling: popular Machine Learning algorithms. 
  • Afternoon Consultation Session (2-3pm, GMT+8): Practice exercises and ask questions during this optional consultation session.  

  • Day 2: Learning Analytics (LA) in Action

    Day 2 begins with cutting-edge data visualisation systems, 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: Visual analytics: tools and techniques

    This session introduces intuitive yet very powerful specialised systems for visualizing your data. You will experience a visual, hands-on, drag-and-drop approach to designing graphs and data stories. This is a great way to present your analysis and findings to non-technical audience. You will then see how easily, and effectively visualised information can be distributed as interactive dashboards via a mobile app.  

    • Introduction to Visual Analytics. 
    • Design data visualisations with Tableau: interactive graphs and simulation.  
    • What story is your data telling?  
    • Online dashboard with MS Power BI: LA mobile apps.  
  • Session 4: LA in action

    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, 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: guiding principles, themes and processes. 
    • Final LA project. 
  • Afternoon Consultation Session (2-3pm, GMT+8): Practice exercises and ask questions during this optional consultation session. 

  • Day 3: Small Group Discussion Session

    Participants in small groups can will have a dedicated Q&A session to ask questions regarding the contents covered or get advice for their own LA projects.