Rules for analysing your student learning data @ysreadthis

Rules for analysing your student learning data



Data causes teachers to react in many strange ways. Many teachers gleefully dive into reams of data, poring through pages intent on finding every nuance, detail and wrong answer. Some put their head in the sand, hiding their data in the dark recesses of a filing cabinet, never to be seen again. Some teachers parade their data through staff rooms, while others run for the hills, avoiding the judgment, the accusing glances and worst of all, the pity.

However it doesn’t need to be like this; and it shouldn’t be. Data analysis is an essential tool in the kitbag of every educational leader, and every teacher should be fully trained in how to collect, analyse and use student learning data to improve their teaching and learning outcomes.

Data should also be used respectfully. It should provoke inquiry, interest and collaboration not fear, pride or envy.

To ensure you have an effective approach to student learning data* in your school, follow these key rules:

Know your purpose
What is the purpose of looking at this particular data? Is it formative, summative or diagnostic? If it is exam data, what exactly do you want to find out? Do you want to find out your students’ scores, or how they performed on particular topics or types of questions? The types of data you are looking at will likely help identify your purpose, but it is an important step in the analytical process to clearly identify a purpose, before beginning the analysis. If need be, write your purpose down at the top of your page to remind you.

Start with the positives

It’s a good rule in life, but when examining student data you should always start by looking for the positives. Is there a general trend? Has a struggling student performed well? Was a certain question answered particularly well? By looking for the positives first, you are far more likely to look at the rest of the data with an open mind, and gain constructive benefit from other sections. If you begin by seeking out everything that went wrong, you probably won’t make it much further past there.

Look for patterns

Visual patterns are quite often easily identifiable in student learning data. Whether analysing exam data, student trackers or spreadsheets, it is often easy to identify clear patterns or trends. With the myriad of data programs now available, it is easy for any teacher to quickly and accurately visualize data in tables, graphs and pivots. Once you have been able to identify visual patterns, the hard part is drawing conclusions as to what these patterns mean. If you have a clear purpose for examining the data, these conclusions are easier to draw.

Don’t get bogged down in detail

If you are going to strain to find meaning in every single digit of your data, there is no point in compiling it in the first place. You may as well just read the pile of tests, essays or exams again. By collating data you are sacrificing the detail within the student work, in order to find greater meaning. So don’t spend half an hour trying to figure out why a 97 should have been a 98, look for the trends and patterns instead.

Work with a mentor or coach

This is important for two reasons – expertise and self-confidence. Sometimes you need the help of an expert, mentor or a coach to just make meaning of your data. Ask for help, there will always be a leader in your school who can help you make meaning of the data. The second reason is self-confidence. Sometimes, no matter how much you look at your data, you can’t see the positives; all you can see is underachievement. This is where a coach is important, to help guide you to see the positive results and keep things in perspective.

Be respectful

As a school, when examining data respect should be a high priority. The last thing you want is for teachers to be scared off analysing data forever by a non-respectful colleague or unfair data analysis process.  Leaders should employ protocols to examine data, in order to keep judgment out of the process and professional learning at the centre. If need be, a code of cooperation should also be employed to keep staff working professionally.

Student learning data is pivotal in the improvement of schools and schools systems, however it’s relatively fast implementation in schools has led to lots of mistakes in its deployment. Schools should ensure they have clear processes and protocols in place, ensuring its correct use.

Colin Burke – February 2013

*This article focuses primarily on analysing student learning data. Other types of data such as survey and qualitative data will be discussed in another post.


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