Best of Class: Good Indicators Require Good Quality Data

Best of Class: Good Indicators Require Good Quality Data: The geneses of a new era creating a different awareness, understanding and application of measuring student achievement through multiple me...

Good Indicators Require Good Quality Data

The geneses of a new era creating a different awareness, understanding and application of measuring student achievement through multiple measures of “learning progress” is upon us.  Yet, do we have the capacity, competence, and confidence to pivot from a single metric defining achievement to several data points that create a mosaic of learner growth?

Our educators will be challenged in many ways to suspend practices to prepare for end of grade or end of course tests.  A renewed focus on the deeper, more enduring habits of learning will emerge.  This focus, if done right, will provide learners multiple ways to construct, apply, connect, collaborate, co-author, co-create, and demonstrate their learning.  The evidence of this focus, I offer optimistically, should be a collaborative effort through the lenses of learners, teachers, and learner guardians.

My best hopes are both the love of learning and the love of teaching will be fanned back into full flame.

Suspending practices is one thing, for many educators, they will have to learn a new set of skills to create a culture of growth replacing a culture of testing.  To do so will require making intentional shifts in thinking as well as doing.  With respect to monitoring and measuring the progress of learning, a seismic shift from focusing and fixating on lagging indicators (data) of learning to leading indicators of learning growth must take place.  This is easier said, than done.

Lagging or trailing indicators of learning have been, for the most part, easier to collect, easier to access and easier to report.  As has been proven, however, just because they are easy and accessible doesn’t translate into effective, authentic, or data of most worth.

By way of an analogy, would you ever drive a car relying on the rear view mirror?  Hardly! Focusing on where you were doesn’t improve where you are or where you’re going. Further, the rearview mirror provides little, if any, feedback on the quality of the drive or driver. Yet, this is the dominant practice in education. 

We know the rearview mirror is helpful as are our side mirrors when making certain maneuvers albeit changing lanes, passing, or backing up to name three.  Though teetering on the obvious, the dominant view while driving is looking forward with anticipatory scanning from left to right.

Automotive technology has advanced to include monitoring, analyzing, and measuring not only our car’s performance but the actual driving and performance of the individual driver.  The vehicle is constantly and consistently informed by analytics that adjust and adapt to the driver in real time.  Further, automotive analytic intelligence provides warning signals and in some vehicles will actually override the braking or turning controls of the vehicle if unsafe conditions are detected and anticipated. The abilities of these technologies to interrupt, disrupt and prevent accidents is nothing short of amazing not to mention will save lives. 

In a like manner, the teaching and learning frontier, inspired by a new breed of technology, equips educators with data that provides both information and insight to adjust, adapt, and apply instructional strategies personalized to the learner - in real time.  Robust intelligent analytics that learn from the learner as the learner is learning is the “gold standard” application of adaptive technology.  Similar to automotive technologies, the key to both, depends on the immediacy and accuracy of data.

To authentically and intelligently monitor and measure leading indicators of learning growth rather than relying on the trailing or lagging indicators is contingent upon a commitment to authentically interrupt, disrupt, or prevent the failure to learn – this, too, will save lives.  To do so will depend on several factors. 

Next, the factors for immediacy and accuracy of data: the first step to prevent the failure to learn.