Data literacy for educators
A world-leading programme on identification, analysis, interpretation, and use of data that will help school leaders make school improvement decisions based on robust educational data, while teachers will be able to align teaching strategies to needs in the classroom.
Data is everywhere and helps governments, businesses, and individuals in making informed decisions. Schools collect and store data in different areas. So how do we ensure that school data and education statistics help to improve learning? Needless to say, it is imperative that school leaders and teachers are able to identify different types of data available to them, as well as understand and interpret the data in meaningful ways.
ACER has designed a world-leading programme on identification, analysis, interpretation, and use of data. The programme will help school leaders make school improvement decisions based on robust educational data while teachers will be able to align teaching strategies to needs in the classroom.
This professional learning programme comprising three modules will be delivered over a period of nine weeks. Each module will be taught for three weeks, two hours every week, in a virtual live mode.
Module 1: three weeks
This is a basic module developing an understanding of data and, more specifically, educational data among practitioners. The course will enable education professionals to understand basic terms associated with data handling, appreciate the importance of data in supporting pedagogy, identify various sources of data and map them to their use in schools, and organise data in tables and graphs to draw conclusions.
At the end of this module, participants will be able to:
- appreciate the importance of data analysis in supporting pedagogy
- identify and evaluate the types and uses of data available in schools
- organise data in tables and graphs (bar graphs and histograms)
- draw conclusions or make predictions based on data.
Module 2: three weeks
This module will help practitioners understand how to analyse assessment data about student achievement using basic descriptive statistics and summarise them for specific purposes. The course will cover graphical and tabular representation of data.
At the end of this module, participants will learn basic statistics to represent and summarise data using:
- frequency distribution
- measures of central tendency
- measures of the spread of distributions.
Module 3: three weeks
This module on data analysis will enable practitioners to develop an understanding of various methods for scoring and ranking students. The course covers traditional techniques employed in Classical Test Theory (CTT) that still hold relevance in some aspects of reporting. An introduction to Item Response Theory (IRT) will be also provided towards the end of the course so that practitioners are able to accurately interpret assessment reports such as the National Achievement Survey (NAS). Different aspects of these methods will be discussed along with comparisons, advantages, and disadvantages.
At the end of this module, participants will have learnt basic statistics to compare and interpret student achievement using:
- standard scores or z-scores
- scale scores.
- No prior experience with data is necessary. The course is open to all educators (school leaders, early career teachers, and veteran teachers).
- Participants must have access to a laptop or computer with the following:
- Microsoft Excel 2013 or above
- high speed/broadband internet connection to attend live video calls and share screens during assignment sessions.
- This course entails certain exercises that require a computer and cannot be attended using a mobile phone.
- Participants should be able to undertake basic mathematics (numeracy) operations and use a laptop/computer.
- Some experience in Microsoft Excel and Word is preferable but not necessary.
Certificates will be awarded at the end of the entire programme to participants who satisfactorily complete all assignments.