Working with data concepts
This data evidence is a rich part of KEP. We are data rich at our school and i do believe we over assess as well.
What and how we use data is so relevant to the success of outr akonga
Standards-based assessment allows us to make judgments about the level of an individual's learning with respect to shared benchmarks of expected performance, supported by
exemplars.
The reliability of an assessment tool is the extent to which it measures learning consistently. The validity of an assessment tool is the extent by which it measures what it was designed to measure.
An important part of a well-designed analysis is to be aware of the types of data that are available, so that the appropriate analytic techniques are employed, and inappropriate ones avoided.
The mean and the median are both measures of central tendency. Standard deviation (SD) is a widely used measurement of variability used in statistics.
In order to understand and analyse data from an assessment tool, you need to know the differences between the ways that different tools measure student achievement, and what that might mean for your analysis.
Norms are statistical representations of a population, for example PAT maths scores for year 6 males, or e-asTTle reading scores for year 9 Māori females.
A good way of presenting differences between groups or changes over time in test scores or other measures is by ‘effect sizes’, which allow us to compare things happening in different classes, schools or subjects regardless of how they are measured
Working with data topics
There are several ways by which quantitative data in the form of scores can be entered into a spreadsheet. Data can be downloaded from a digital assessment tool or student management system.
When working with data to analyse results and draw conclusions, it is essential that the data with which you are working is ‘clean’. This means that it is consistent, accurate and complete.
Graphs (also called charts) play an important role in data analysis. A graphic representation can make the relationship between sets of data much easier to understand.
Student achievement data is often reported for whole populations (for example: cohorts, year levels, whole class). This is called aggregate data.
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