One of the more difficult analyses that schools need to perform is comparing internal assessments to external assessments.
A novel builds character arcs before a climax; a comedian gives a setup before a punchline; and research papers place the results section before the discussions and conclusions section. It’s simple:
People make deeper connections when anticipation builds and finality is with-held.
Surveying is a powerful tool for uncovering perception data. Whether it’s surveying your teachers for morale or efficacy perceptions, your community for satisfaction perceptions, or your students for their perceptions on their own learning, almost every school employs some form of perception survey.
We may argue about survey design, or over specific items in the survey, but I think the bigger problem is in the analysis and representation.
Are you beginning down a data path at your school? Or do you already consider your school to be data rich? Then I have a question for you:
Where does most of your data analysis effort live?
The beauty of using a coding language like R or Python, is that you can customize virtually every aspect of your visualization. With an eye for design, you can portray loads of information in one graphic.
It’s a common question: are we grading equally? And while I don’t like the focus on marks, I can appreciate that calibrated grading ensures proper feedback, helps create a shared vision of the purpose of an assessment, and yes, even helps avoid some potentially heated discussions with parents.
One way to build positive data culture is to give teachers powerful data when they need it most. Too often teachers feel that their opinions go unheard; but backed by quantitative data and visualizations, they become much more difficult to ignore.
When it comes to presenting data, protocols for data, and the administrative use and sharing of data – there’s a lot to know. High level stats, privacy rights, statistical coding, and more… yet we ask professionals who are experts at lesson design to be aware of all of this and how to leverage data to measure effectiveness. I simplify it down to one simple rule:
Data should empower. Data should provide insights. Data should inspire more questions. Without a positive framework, capacity building, and expertise – data is often thought of as scrutinizing, overly subjective, and a burden.
So let me propose my model:
Over at YouCubed, Jo Boaler and the team have released the second edition of the “Week of Inspirational Maths” (WIM). The lessons promote mathematical growth mindsets, dispel discouraging myths about learning, and give engaging tasks with access points for all learners.