Professional Learning + Data = analysis student learning – Part 1
Algorithms by Kevin Dooley CC BY
I am grappling with the direction of my professional growth and defining a SMART goal for the next academic year. The focus of course is Student Learning in Physed (and Wellness/Health). I have moved to the International School of Beijing (ISB) and as with any school, there are expectations of professional growth and the Middle School at ISB have decided to do this in two different ways.
Firstly we are being asked to “develop a goal that shows growth in your ability to use data to provide a more personalized learning experience for your students” and secondly we are being asked to set a guiding question that can be answered through Peer observations with the focus on “de-privatizing our classroom instructional practices because we have a lot to learn from each other.”
Peer observation makes some sense to me. I have read a little about this from other PE specialists – with the focus to look introspectively at your own teaching and to see what patterns of behavior you use (are you arms always crossed?/ do you smile? Are you speaking clearly?) and also more openly at the content of your lesson and activity time vs talking time as well as feedback (are students actively taking away the feedback and making change to the work that they are doing?) or looking at your approach to English as an Additional Language (EAL) and Learning Support (LS).
I will admit that I am nervous about being videoed, and actually wonder if I could ask for someone to video me on the fly (so I don’t know that it is happening) to avoid me possibly being superficial. I am also nervous as I am at a new school with new peers and new students and new spaces to learn. But as this is a requirement and the focus is on opening up our learning to others and sharing our practices I will think carefully about who to work with to ask to watch the video with me to ‘observe’ and be honest about things that could be stronger or more effective. I will write down my own biases first – what I think (or hope) I will see so that I am able to see my own biases and find out exactly where things could be stronger!
Developing a goal around student learning AND use of data has me pondering, reading and asking lots of questions. Of course we, as teachers, collect a lot of data in our lessons, and after discussion with others this week and reflecting on this it is clear that I do make decisions about what to teach next based on what I see – I make alterations or change individual or team challenges based on what I see my students doing and how they change their game play based on the ideas and experiences we are creating together. I discuss students’ learning with relevant colleagues (EAL or LS or with other teachers about behaviours or coaches about athleticism or parents about injuries) and use these data collections for planning and monitoring students – but today I came away wondering if all of the data I collect comes down to feedback for growing student learning? Or am I trying to answer easier questions (substituting an easier one in its place?) What if the feedback and data I gather is more an answer about planning lessons or booking resources rather than about quality student learning?
Questions that I am pondering when it comes to student learning and data:
What is it that I want to learn about in relationship to my students learning?
How can I grow student learning in my subject?
What impact do I have/ students self awareness have on learning? Where should my priorities lie?
How can I measure student change (affective/cognitive/game play) in relation to the feedback?
What feedback would be best for the students and in my new environment?
What formative assessment can I use and possibly measure the outcome on summative assessment?
Which of my PLN would be go-to for ideas or sounding boards?
Of the previous PD I have completed, what could I draw from (eg. use of visible thinking routines or formative assessment ideas from Dylan Wiliam) that could be part of data collection now?
I am currently reading ‘Thinking Fast and Slow‘ by Daniel Kahneman. This book is one of the most challenging and brain assaulting books I have ever read, in Kahnemans’ terms it is a System 2 book and is not a light pre-bedtime read (there is a summary of the book here). This reading and thinking/discussion is changing many of the ways in which I am seeing and questioning everything – from how to conduct an effective meeting to how to interview for a job (or to interview candidates for jobs) and also making me think very hard about assessment as well as planning my lessons. I have only just touched the surface of all the amazing ideas about how our brains think and function and plan to devote the better part of my year to learning more and questioning my practice based on these ideas.
However, the biggest ‘aha’ moment for me when thinking about my teaching and student learning, so far, has been the use of simple algorithms. Can you simply decide what is important – the development of a useful algorithm without any statistics evidence? This equally weighted formula based on existing work or on common sense can often be “a very good predictor of significant outcomes.” An example given in the book is from Dawes:
frequency of lovemaking minus frequency of quarrels = marital stability
If I look at things from a PE perspective there are a few things that I am currently thinking about:
Active time in games – competent bystanding = student learning
Skills + Game Sense + Personal/Interpersonal skills = student learning
I feel this sums up exactly how I see, teach and would like students to learn in PE at the moment. I recently was fortunate to share ideas with Rick Baldock at the PhysEdSummit and we discussed the big three models of PE currently in use – TPSR; Sports Education and Game Sense models. I feel that all three of these are represented in these simple formulas. I would really like to now consider what my SMART goal will be out of these ideas and thinking. I look forward to sharing and discussing ideas with others in the field, particularly when considering the focus on data collection for student learning conversations.