The goal is to improve our education system to the point where every student meets minimum grade-level expectations in core academic subjects, or at-least that every student achieves one year of growth for one year of instruction. The No Child Left Behind Act of 2001 raised the bar. Prior to NCLB it was deemed acceptable that some students fail to meet minimum competency standards. Reauthorization of the act may change the ground rules but it will not abandon the every child goal.
Approaching the goal is going to require more than what has traditionally called "reform". Reform has been a misnomer as these change initiatives typically work within the existing culture and organizational structure. Learning organizations need proven methodologies to manage change, to refocus effort and structure on the new goal.
Here is a video reenactment of the presentation that David Lineberry and I made at the National School Boards Association conference this year on the subject:
Video: April 5, 2009 EASE Presentation at the National School Boards Association Conference
Friday, October 9, 2009
Friday, October 2, 2009
Learning organizations' transition to "analytics age"
One of the blogs I follow is Stephen Few's "Visual Business Intelligence". His September 21, 2009 post (http://www.perceptualedge.com/blog/?p=621) commented on a keynote presentation by Malcolm Gladwell, journalist and author of the books Outliers, Blink, and The Tipping Point, at SAS Institute’s Innovators’ Summit in Chicago.
The key point that Few wanted to highlight is that we have entered an "analytics age" in which the challenge we face in solving problems is less about uncovering hidden truths to inform decisions, and more about understanding the information we already have. He writes "...the major obstacle to solving modern problems isn’t the lack of information, solved by acquiring it, but the lack of understanding, solved by analytics."
In education however, the problem still lies in acquiring the right information and making it available to the right people at the right time. The problem is not in quantity of information, but in quality of the data being capture. A big part of data quality is relevance. In education an enormous amount of data is being captured that can support some higher-level policy decision-making. However, consistent daily measurement of student learning, the core business of schools, is generally not done in a way that can inform the most important processes within the system.
There are cultural and technical barriers preventing quality measurement of student learning. First is the cultural tradition that paints teaching as an art, not a science, and teachers as a kind of 'private practice' profession. In the old model each teacher develops quizzes and tests independently. Different instruments are used to measure student learning from one classroom to the next and different grading schemes are used, often comparing students to student on a bell curve rather than measuring students against standards. The data collected, even if captured electronically has little value for the kind of analytics that are needed. Even a single students progress cannot be reliably compared across grades.
The standards movement and adoption of promising organizational models, such as professional learning communities, are changing that tradition. Teachers are engaged in more formal collaboration and more open to common classroom/formative assessments. The recent work by 48 U.S. states on Common Core standards is also promising. When all educators in the U.S. have a common understanding of minimum grade level expectations for the core subjects of math and English language arts it will be more likely that consistent instruments of measurement will be used. This also helps solves a second problem, the technology and process for consistently capturing student learning data. Economies of scale will drive availability and adoption of technology that transparently captures student learning. The technology will eventually make it easy for teachers to transition to common assessments by reducing work and providing more valuable feedback to inform instruction.
The third challenge is to make the information that is collected on individual student learning available to the right people at the right time, and presented in a usable form. This brings us to what Stephen Few calls a transition from "information age" to "analytics age", or as Daniel Pink calls the "conceptual age". Few and others see statisticians playing the important role in this age, quoting Hal Varian, University of California, Berkeley professor and current Chief Economist at Google as saying “I keep saying the sexy job in the next ten years will be statisticians.” From Pink's point of view it is not specifically statisticians, but creative problem solving professionals.
I think they are right, but I also see a greater opportunity for learning organizations in empowering non-statisticians with the tools, skills, and processes that allow them to make information-driven decisions. The greatest positive impact can come when analytics and creative problem solving are embedded within teaching, learning, and leadership practices. Everyone cannot become a statistician. Instead the data visualization tools must cut through the noise and not only present the right information to the right people at the right time, but provide meaning within the context of the decision at hand.
Malcolm Gladwell and Stephen Few see that the challenge for solving modern problems is more in understanding and using an abundance of information that is readily available rather than finding hidden information. In education we have to first acknowledge that not all the right information is available in a usable form but that we are quickly moving toward an "age of analytics" when the right information will be available and become part of decision making at all levels, not just for statisticians.
The key point that Few wanted to highlight is that we have entered an "analytics age" in which the challenge we face in solving problems is less about uncovering hidden truths to inform decisions, and more about understanding the information we already have. He writes "...the major obstacle to solving modern problems isn’t the lack of information, solved by acquiring it, but the lack of understanding, solved by analytics."
In education however, the problem still lies in acquiring the right information and making it available to the right people at the right time. The problem is not in quantity of information, but in quality of the data being capture. A big part of data quality is relevance. In education an enormous amount of data is being captured that can support some higher-level policy decision-making. However, consistent daily measurement of student learning, the core business of schools, is generally not done in a way that can inform the most important processes within the system.
There are cultural and technical barriers preventing quality measurement of student learning. First is the cultural tradition that paints teaching as an art, not a science, and teachers as a kind of 'private practice' profession. In the old model each teacher develops quizzes and tests independently. Different instruments are used to measure student learning from one classroom to the next and different grading schemes are used, often comparing students to student on a bell curve rather than measuring students against standards. The data collected, even if captured electronically has little value for the kind of analytics that are needed. Even a single students progress cannot be reliably compared across grades.
The standards movement and adoption of promising organizational models, such as professional learning communities, are changing that tradition. Teachers are engaged in more formal collaboration and more open to common classroom/formative assessments. The recent work by 48 U.S. states on Common Core standards is also promising. When all educators in the U.S. have a common understanding of minimum grade level expectations for the core subjects of math and English language arts it will be more likely that consistent instruments of measurement will be used. This also helps solves a second problem, the technology and process for consistently capturing student learning data. Economies of scale will drive availability and adoption of technology that transparently captures student learning. The technology will eventually make it easy for teachers to transition to common assessments by reducing work and providing more valuable feedback to inform instruction.
The third challenge is to make the information that is collected on individual student learning available to the right people at the right time, and presented in a usable form. This brings us to what Stephen Few calls a transition from "information age" to "analytics age", or as Daniel Pink calls the "conceptual age". Few and others see statisticians playing the important role in this age, quoting Hal Varian, University of California, Berkeley professor and current Chief Economist at Google as saying “I keep saying the sexy job in the next ten years will be statisticians.” From Pink's point of view it is not specifically statisticians, but creative problem solving professionals.
I think they are right, but I also see a greater opportunity for learning organizations in empowering non-statisticians with the tools, skills, and processes that allow them to make information-driven decisions. The greatest positive impact can come when analytics and creative problem solving are embedded within teaching, learning, and leadership practices. Everyone cannot become a statistician. Instead the data visualization tools must cut through the noise and not only present the right information to the right people at the right time, but provide meaning within the context of the decision at hand.
Malcolm Gladwell and Stephen Few see that the challenge for solving modern problems is more in understanding and using an abundance of information that is readily available rather than finding hidden information. In education we have to first acknowledge that not all the right information is available in a usable form but that we are quickly moving toward an "age of analytics" when the right information will be available and become part of decision making at all levels, not just for statisticians.
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