A reverse poem is a poem that can send two very different messages, all depending on the direction it is read:  Read forward and the message is extremely negative, but reverse the same words, and the message is surprisingly positive (see “The Lost Generation” poem at http://www.youtube.com/watch?v=42E2fAWM6rA as an example).

Data can be like that.  When teachers approach student achievement data from one direction – a direction of obligation (because someone said looking at data was required) and weaponry (because the data are only used to judge and sort students into new groupings or locations of the school to be ‘fixed’)  – then the teams’ conversations are laborious, aggravating, and time-consuming at best.  When data conversations are navigated as a responsibility that must be managed, no one is happy.  The dreaded conversation is avoided until the requirement to submit results can no longer be avoided.  Everyone losses when data are used to make decisions that impact others but preserve the status quo.

• Learners lose:  Ultimately, the greatest tragedy with this approach is that teachers are using data to make decisions about kids; in this case there will always be losers and winners. The rank ordering system launches from a place of fixed mindsets in teachers and ultimately results in fixed mindsets in learners: students themselves develop a belief that they will always be whatever the system has dubbed them to be.  In such a case, neither category – losers or winners – will opt to take risks for fear of failure (Dweck, 2008).
• Teachers lose:  When data are use to rank order their learners, teachers may feel little ownership for the results, struggling learners are shuffled around in the school and throughout the schedule to get help elsewhere, and the instructional core remains unchanged.  In this system, with little change in classroom practice but an increase in data driven decision making, it only would make sense that the system at large would logically begin the process of rank ordering (categorizing) teachers.  Like students, teachers (winners or losers) will avoid taking risks in this system.
• Schools lose:  Learning is risky business and schools that use data in a manner that preserves the status quo will end up creating a system of risk-aversion.  Creativity and innovation are deflated.   Competition remains the norm, even though collaboration might be the preferred practice.

But reverse the poem (or data, in this case).  Play the scenario differently.  What if teachers explored student achievement data for the purposes of innovating their ‘response’ ability?  What if data mining led to conversations that were less about sorting learners into grouping or alternate spaces for interventions and more about improving practice?  What if teams explored data with the following questions in mind?

• What strategies did we use that generated our best results?
• Why were those strategies so successful?  What worked about them?
• How could we replicate what worked in new and different ways for students who are still missing the content or processes?
• What innovative or creative options do we have to alter our teaching practices so fewer and fewer learners require interventions once we are done with the initial instruction?
• What strategies are worth trying and how could we track the effectiveness of our new efforts?  What will success look like?  How can we guarantee that we all (students and teachers) ‘arrive’ at success?

Teams that address those kinds of questions while mining student achievement data feel energized and synergistic.  They love looking at data because they feel empowered and enlightened.  Their passion is catchy and their enthusiasm grows.  They alter beliefs and practices.  They build efficacy collectively and innovate teaching and learning collaboratively.  They develop shared response ability.