This year I have been focused on some driving questions that are driven by the MS leadership team at my new school. I have blogged a lot about this already. One area that has been a focus for me is about data that I collect on my students. Data collection for Physical Education can be arduous. What kind of data should I be collecting and how will I use this to guide student learning? I have been experimenting here with some professional feedback loops to enable me to see if my formative feedback is accurate (am I talking to students about skills and tactical strategy with informed feedback to see improvement?) and to measure growth from pre-unit assessment to post-unit assessment.
I am trying to collect data across different types of learning. This kind of data used to drive my evidence of student learning in the MYP in four areas. Knowledge; skills/strategy; ability to plan and use planning to guide performance and finally social and personal skills. The problem I see is that these four areas are not so neatly split in my standards based grade book. I am reporting to parents and students on three PE areas and three non-graded behavioral focus points now. The PE ones are Movement Concepts (skills, strategy and knowledge); Active Living (working to potential in each lesson to be active) and Health (only health related standards here, not practical work). Overwhelming the evidence I am collecting is in the first strand – Movement Concepts. But there is no differentiation between skills, tactical work (application) and tactical work (knowledge and understanding). So the data I am collecting is informing me about student learning but is all mixed up into one final strand grade – but doesn’t truly tell the student or parent where the student is excelling (are they awesome at skills and lacking in tactical application? are they awesome at coaching tactical play but can’t execute it? are they very knowledgable about game play but struggle to apply it? are they awesome at skills and knowledge and strategy?) I wonder whether reporting in a different way (break down of this one strand) perhaps give a more detailed picture of where there are strengths and weaknesses? or if this discussed in a comment to parents?
We also report on Student as a Learner – three areas that I am currently collecting evidence as lesson observation but could potentially be much more explicit in our units to add strength to the our personal and social education. The three reported strands are: attitude, responsibility and collaboration. Clearly these match up to PE in a major way! These are not graded but rather we give information about these to parents so they can see if their children are working well with others, can really take responsibility for themselves and their learning and have a healthy attitude to learning and growth mindset.
I have been working to collect a variety of data – from video footage, notes taken on students (rotational each lesson to try and capture anecdotes on each student and observations that correlate to the assessment standards I am working with); I am also completing pre/post lesson data. I use Visible Thinking Routine tests (Claim/Support/Question) and What do you Notice/ Wonder? type exercises to see what we are noticing in our work or in a peer performance and not infer or bias their observations. We have used worksheets/ peer assessments and feedback loops and others in the quest for really honing into student learning.
I am sure many of you have the same as I do – so much data but maybe a growing sense that perhaps I am not sure what I should do with it next. I know that I second guess myself a lot, this is part of professional growth and reflection on what is best practice.
I started the DataWise course last year but didn’t make the time to complete it. I am really working hard to complete it this semester as I am finding it to be rich when it comes to having student data and consider how best to use it to create experiences that will lead to more effective student learning. The course is an online MOOC and is run out of Harvard as an EdX course. There are 8 steps in the course and each one has taken me about an hour to work through. There are a lot of resources you can look through or purchase as additional reading or thinking about the different steps.
Takeaway 1: Building a Collaborative Team
The first takeaway I have is that the importance to build your team and be very explicit about the norms of the group is vital for success. If your team has just one person who feels undervalued or is not aware of what is expected or isn’t buying into the collaboration process then you will not have a high functioning team. Once you have a high functioning team then you can begin to work together to find the patterns and trends in your school/team data.
Takeaway 2: Build a common language
Data can be complex and overwhelming. How do you discuss data with your team or division? Maybe we are all assuming we know and have all the answers but in reality what I see in the data is vastly different to what you see. The ability to discuss data and to have a common understanding of what data can and can’t tell us is really important. I realised that the data that we may be sharing is not useful if we don’t have a big enough sample size.
I feel like we are in a constant state of review in my department which is hard work and I think morale can be lost as it can seem endless. I can see that it would be useful for us to look at the standards we are looking to assess and to explicitly discuss the assessment tasks so that each of us is more aware of what the task is asking of us (common language and expectation across the team) and then to moderate our student work more regularly to discuss whether the work met the standard and so validate the assessments we are doing. This would require less ego and more collaboration with the department (and more time probably initially).
Takeaway 3: Choose multiple data points and find a story
If your school has a vision around literacy then you need to know how your division or grade is going to choose data that can now triangulate literacy data and then analyze the data to find a story about literacy in your students. It is important that we display our data in a way that allows us to read it and discuss it. This may mean that we have to simplify challenging charts or graphs to allow everyone to read them. Everyone in the team needs to work with this data to identify and discuss it – firstly we need to Notice things in the data and then Wonder about this which can lead to discussion and patterns that possibly a single teacher wouldn’t necessarily pick up.
I am left at the moment wondering what kind of data would be useful for me (and my department) to collect and use as a multiple data sample that might help us to start to see a story or pattern and in turn then help us to carefully decide where there are areas that need to be addressed for more effective learning. Not every student needs to be scoring or performing poorly for us to choose an area of focus, as long as the extra instruction in our chosen focus question would enhance everyone, then you should go for it!
Takeaway 4: Dig into the data and investigate a question and identify a learner-centered problem
After you have the data, had your conversations the idea is to come up with a question that is very direct and specific that will channel your energy as a group. If you have focused on literacy and looked at data around different test scores, teacher-student conversations, classroom work then you should have a picture of literacy in your grade. You might have several areas that you want to focus on – organisation of writing/ spelling/ grammar etc but you should choose one area and focus just on that for your group to collaborate on. If the focus is too broad you will not have a sense of direction of discussion and ultimately instruction for your students.
Takeaway 5: Student data can drive instructional choice
For me I feel I am using data in my lessons to make decisions about when to change activity or intervene or modify a game but I am not sure that I am collecting data that is looking at specifically what we are teaching and whether we have some deficits based on the learners we see every day. I am still not sure how to talk to the team about this – and where to begin but I will finish the course first to have the full picture before I delve into this next round of data driven conversations.