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.
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 of the most worrisome parts about Big Data in schools is the unethical targeting of teachers based on single data points — often misused. It’s therefore imperative that specialists, well-versed in statistics, are present for data based conversations. Here’s a great example: (more…)