Super Early Bird Fee
Register and Pay
by 29 Jan ’21
Early Bird Fee
Register and Pay
by 26 Feb ’21
Regular Fee
Register and Pay
after 26 Feb ’21
Non Singapore-registered companies $1,795 (SGD) $1,995 (SGD) $2,095 (SGD)
Singapore-registered companies (fees include 7% GST) $1,920.65 (SGD) $2,134.65 (SGD) $2,241.65 (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 26 Feb 2021, a full refund will be given with a 10% administrative charge. For cancellations received in writing before 8 Mar 2021, a 50% refund will be given together with the event documentation. There will be no refunds for cancellations received after 8 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?  

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 2-day 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) projects and be exposed to the practical applications of LA. Discover how to tap on built-in reports on LMS such as Moodle and perform advanced data collection. Conduct data exploration and modelling with Python. Tell compelling and informative stories with effective data visualisation. Uncover good questionnaire designs and practice processing qualitative data.


Unique Features

    • Led by Dr Patrick Tran, Educational Developer at the UNSW Canberra
    • Explore Moodle, a popular LMS and unlock built-in reports and other LA plugins available
    • Experience advanced data extraction, exploration and modelling on Python with the assistance of Juypter Notebook
    • Deep dive into qualitative data and semi-structured text data analysis
    • Get a tour of and practice with tools such as:
      • Python
      • Microsoft Excel
      • Microsoft Power BI Dashboards
      • Tableau
      • NVIVO

Benefits of Attending

  • Understand key features in defining and implementing various LA projects
  • Extract learning data from built-in reports in Moodle and tap on ad-hoc reports and plugins
  • 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 best practices when designing learner questionnaires and conducing factor analysis
  • Perform analysis on student surveys and highlight common themes sourced from data
  • Distribute effectively visualised information and data on interactive mobile dashboards
  • Hear guiding principles, themes and processes when building and managing a LA project
 

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
• Gamification
• 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.

 

Agenda

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

  • 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 is involved 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: Learning Management System (LMS) tools for LA

    This session provides a quick overview of LMS in general, and Moodle as the most popular LMS in particular. We will explore the power of built-in reports that allow extraction of learning data as well as supported customisable communication with learners. Advanced ad-hoc reports and tools are demonstrated as an intervention tool as well.

    • LMS from the viewpoint of learner, instructor and administrator
    • A quick tour of Moodle as the most popular LMS
    • Use built-in reports for basic data extraction and communication
    • Use ad-hoc reports and other LA plugins for advanced data collection and intervention
  • Session 3: Advanced data extraction with Python

    This session introduces powerful Python packages for web harvesting, a technique to extract data from webpages. You will code your first web crawler program scraping course data from the LMS and other online systems.

    • A quick tour of Python programming language
    • The basics of the Web and HTML
    • Build your first web crawler in Python: system login and data extraction
    • Extract LMS data on course contents and activities
  • Session 4: Data exploration and modelling with Python

    This session introduces basic data structures and techniques used to manipulate data sets. We will learn how Python can help with loading, processing and visualizing 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 programmable data visualisation with Python
    • Classical analysis: hypothesis testing and statistical modelling
    • Advanced predictive modelling: popular algorithms
  • Session 5: Visual analytics: tools and techniques

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

    • Introduction to visual analytics
    • Design data visualizations with Tableau: interactive graphs and simulation
    • What story is your data telling?
    • Online dashboard with MS Power BI: LA mobile apps
  • Session 6: Analysing qualitative data

    This session covers a very important source of learners’ data: self-reported data or feedback, containing both quantitative data and semi-structured text data. You will learn factor analysis techniques and how to design a good questionnaire ready for your research. We then discuss thematic analysis techniques and the powerful NVIVO software that can be used to extract common themes emerging from the data.

    • Factor analysis and questionnaire design
    • Design and run your first learners’ experience survey
    • Thematic analysis with NVIVO
  • Session 7: 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