PTC NSWHomeGEMSProcessingStep 17: Data Processing

Step 17: Facilitating Data Processing

This step involves students in analysing and drawing conclusions based on their recorded data and their original questions.

These processes involve numeracy skills such as data organisation into tables, calculations including averaging, and construction of graphs to better enable identification of patterns or trends and relationships between variables. In this video teachers describe how literacy and numeracy skills can be developed through the program.


Expectations in data analysis for different schooling year levels

The following table summarises the position of the Australian curriculum: Mathematics with regard to teaching of data for years 1 to 7.

Continuum of data representation skills

Year Expectations


use or construct simple pctographs

identify links between lists, tables and pictographs


use tallying to record data and construct tables, picographs and bar and column graphs

use lists, tables and graphs of simple data to attempt to interpret and explain

identify that information remains the same even though representations may change


make predictions, carry out investigations about familiar situations to gather data and report the results

make and use tables, diagrams and graphs, including dot plots that have prepared baselines and identify the links between them

understand the importance of scale and equally spaced intervals on an axis


generate questions and use surveys to obtain data an use the results (including the use of ICT) to answer questions

use ICT while constructing, reading, interpreting and identifying links between tables and simple graphs that contain more complex relationships between data and symbols

idenitfy how a small sample size impacts on outcomes of data compared to larger numbers in trials of chance events using ICT


collect data over time to carry out an investigation into the relationship between variables, report results draw conclusions and justify them

begin to explore bivariate data over time (for example, identify that comparison of the growth of pea and bean plants over several weeks requires that points be plotted on identical axes)

use lists and dot plots to identify the mode and median

analyse and compare a range of data representations of specific situations 


construct, read and interpret tables and graphs including ordered stem and leaf plots and construct pie charts and other simple data displays including using technology

analyse and compare a range of data representations for specific situations

use repeated measurements to explore variation and error


use ICT and compare data sets using mean, median and range and show reasoning

collect univariate and simple bivariate data and use back -to-back stem plots and scatter plots to investigate questions


How to facilitate understanding around data tables, data averaging and graphing

The document Investigating Scientifically contains many suggestions for facilitating and progressing students' understandings in this area. Examples in the document have been specifically designed for primary school settings.

For information related to tables refer to pages 10-11

For information related to graphs refer to pages 12-14


What type of graph will we use to represent the data?

The type of graph that is generated from the data depends on the type of data. Data relating to one variable can be represented as pictographs, bar and column graphs and ordered leaf and stem plots. In 'fair test' investigations students are investigating the relationship between two variables (that is bivariate data). Line graphs are often used to represent this type of data.

Line graphs have two axes, horizontal and vertical or x and y axes. The independent variable is usually plotted on the x or horizontal axis and the dependent variable on the y or vertical axis.

Other features of a line graph:

  • title indicating what the graph represents
  • a grid which is used to plot the data


At this stage students will also analyse the data to determine whether the hypothesis was true or not true. Here are some examples of hypotheses and predictions.

Hypotheses and Predictions

Hypothesis Prediction if my hypothesis is true
The longer the windmill blades the greater the number of turns. Windmill A blades are longer than windmill B blades.  During five minutes of operation A will turnmore times than B.
The higher the concentration of peat in the soil, the more water will be retained. Five soil types will be tested: Soil A (100% soil), Soil B (100% peat), Soil C (75% soil, 25% peat), Soil D (50% soil, 50% peat), Soil E (25% soil, 75% peat).  When the same amount of water is poured into each soil, the soil with more peat will drain less/retain more water.
The larger the particle size of mulch, the less soil will be washed away. Soil covered with whole dried leaves compared with soil covered with pieces of dried leaves will have less soil washed away when watered with simulated rain.


Role of mentors

Data processing occurs between the second and third mentor visits. Mentors will be available to assist the students online with processing and analysing their recorded data.

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