|22 Jul 2019||
||$660 (see description)||Register|
|7 Oct 2019||
This course is a foundation level professional learning program focused on developing teachers’ expertise in using and interpreting different types of data in a school context. It is designed for teachers and school leaders who wish to build solid shared understandings about the kinds of data used in schools, the different ways in which data can be represented and what they can tell teachers about student learning.
Recommended prior learning
This foundation course has no recommended prior learning. However, it is recommended that users of the PAT assessment suite access the PAT webinars prior to starting this course.
Intended learning outcomes
Participants will be able to:
- Identify and critically evaluate the types, purposes and uses of data available in schools
- Summarise and represent data, including assessment data
- Interpret and represent tabular and graphical representations of data in reports.
AITSL Professional Standards for Teachers
This course focuses on Professional Standard 5 at the Graduate and Proficient levels.
Professional Standard 5.4. Interpret student data.
AITSL Professional Standards for Teachers
This course focuses on Professional Standard 5: Assess, provide feedback and report on student learning, particularly Professional Standard 5.4. Interpret student data.
This course is targeted to the teaching levels indicated in the table below.
|Graduate appropriate for this level||Proficient appropriate for this level||Highly accomplished||Lead|
Demonstrate understanding of assessment strategies, including informal and formal, diagnostic, formative and summative approaches to assessing student learning.
Use student assessment data to analyse and evaluate student understanding of subject/content, identifying interventions and modifying teaching practice.
|Work with colleagues to use data from internal and external student assessments for evaluating learning and teaching, identifying interventions and modifying teaching practice.||
Coordinate student performance and program evaluation using internal and external student assessment data to improve teaching practice.
Course in detail
The course is delivered in online mode and consists of 7 Modules and 2 learning assurance tasks (a multiple choice quiz and a data manipulation task). The modules are interactive and participants work through examples and activities to build skills and knowledge.
Module 1: Introduction to data
This is a gentle introductory module that defines key terms and concepts. Topics include, items and values, data collection, and a data collection scenario.
Module 2: Data in schools
This module gives examples of school data and how to record, organise, examine, make inferences and test hypotheses with data, and shows how you can focus on your purpose for using the data available to you.
Module 3: Assessment and measurement scales
This module introduces the four scales of measurement, how to summarise data with each scale of measurement, and includes case studies of school assessment data, and psychometric modelling.
Module 4: How can data be represented?
This module focuses on organising data to tell a story. There are two case studies, representing data in tables, and demographic variables. Topics covered include measures of central tendency, mean, median and mode, measure of spread, different shapes of normal distributions and using standard deviation to analyse distributions.
Module 5: What data can tell us about…
This module investigates educational reports and focuses on the questions to consider when reading data. It includes how to find meaning from locally and externally generated reports. It looks at school and system level data, how to interpret ICSEA and national and international data.
Module 6: Comparing data – your school and beyond
This module focuses on comparing data locally and globally, comparing data mathematically and visually, and comparing statistical samples. It introduces an independent research activity relevant to your work context.
Module 7: Data for research
In this module, participants learn how to build on the knowledge gained in this course to further data analysis expertise. It suggests pertinent questions to ask when researching, and the factors that influence learning outcomes.
Learning assurance tasks
A multiple choice quiz and a data manipulation task.
On successful completion of this course, participants will receive an ACER certificate of achievement.
Time and duration
20 hours of online learning over approximately 10 weeks.