It is difficult to participate in any school-based professional development that does not include some conversation about data. This is especially true as teacher teams come together in data chats. An important aspect of collaboration among teacher and school teams, data chats allow for the review of student work and classroom practices. However, one worry that I hear from my colleagues is whether we have become data obsessed – so concerned with reviewing data that we lose the purpose for our data chats: to change classroom practice and improve student learning. An example of this may lie in the latest trend in data analysis, Big Data.
We have all experienced it: Amazon’s instant recommendations for the “just right” book to buy or a Netflix movie recommendation for the “perfect evening”. Can you imagine how much data Microsoft collects from its Xbox users? How do they do it? Big Data. According to the authors of “Big Data: A Revolution That Will Transform How We Live, Work and Think”, “big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationships between citizens and governments, and more”. As an example, the authors point to Google’s ability to predict the spread of the H1N1 flu virus in real time. By monitoring billions of search terms Google analyzes these terms using powerful computer algorithms looking for strong correlations (patterns). Now doctors and government officials can respond and react immediately instead of waiting two weeks (the traditional turnaround timeframe from the Center for Disease Control and Prevention).
As teacher leaders we need to ensure that our data chats stay productive and focused on our students. This doesn’t mean that there is not a place for Big Data; even though the above authors see the current usage of standardized testing data as misplaced: “letting the data govern us in ways that may do as much harm as good”. We should welcome any data that serves two purposes: 1) that adds value to student work samples and 2) is relevant to the question(s) that are the focus of our data chat.