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.
Wednesday, June 10, 2009
20th Century Methods for "21st Century" Teacher Education
The June 1, 2009 special report from eSchoolNews on 21st-Century Teacher Education , that was supposed to highlight progress being made in teacher pre-service programs, unfortunately exposes the truth about the lack of progress.
Before even reading the article the tag line raises some concerns, "Responding to a key need, schools of education are ramping up efforts to prepare future teachers to integrate technology into their instruction." On the surface this may look like progress, but I read "ramping up efforts" as code for "just getting started".
Even more concerning is this notion that what is needed in the 21st century is for teachers to "integrate technology into their instruction". I read this as "force technology into existing instructional practices" rather than leverage technology to facilitate fundamental improvements in how student learning is done and how it is supported by educators. The article confirmed my concerns, leading with the example of a teacher using a SMARTboard. The SMARTboard is the kind of technology that easily integrates into 19th century pedagogy.
Rather than learn how to inject technology into the 19th century model educators should be preparing to contribute to a model of education where The Student is the Class, a model that takes advantage of technologies for virtual learning, online simulation, individualized learning plans, adaptive assessment of learning, and educator-learner collaboration. More important than a focus on technology is the need for teacher preparation programs to facilitate changes in methodology that move from the 19th century classroom model to that of a learning community that does Whatever It Takes to support individual learners.
Before even reading the article the tag line raises some concerns, "Responding to a key need, schools of education are ramping up efforts to prepare future teachers to integrate technology into their instruction." On the surface this may look like progress, but I read "ramping up efforts" as code for "just getting started".
Even more concerning is this notion that what is needed in the 21st century is for teachers to "integrate technology into their instruction". I read this as "force technology into existing instructional practices" rather than leverage technology to facilitate fundamental improvements in how student learning is done and how it is supported by educators. The article confirmed my concerns, leading with the example of a teacher using a SMARTboard. The SMARTboard is the kind of technology that easily integrates into 19th century pedagogy.
Rather than learn how to inject technology into the 19th century model educators should be preparing to contribute to a model of education where The Student is the Class, a model that takes advantage of technologies for virtual learning, online simulation, individualized learning plans, adaptive assessment of learning, and educator-learner collaboration. More important than a focus on technology is the need for teacher preparation programs to facilitate changes in methodology that move from the 19th century classroom model to that of a learning community that does Whatever It Takes to support individual learners.
Monday, May 11, 2009
Emergency Room Visit – A Model of Individualized Care -- Lessons for Education
An unplanned trip to the hospital on May 7th, 2009 gave me some insight as to how an organization can be optimized to meet the needs of individuals. This is in contrast to the organizational model of educational organizations, and the cultural inertia that resists full support of individualized learning.
What I noticed first from my emergency room experience was the number of people involved in my care. I was served directly by nine different care providers in just the first half-hour or so. The actions of each care provider were informed by the work the others had done, coordinated, and focused on my individual needs just long enough to provide the needed care.
I was amazed at the efficiency with which multiple ‘specialists’ each provided just the right care at just the right time with just the right information to support their decisions and actions. My time was limited with each specialist, but each encounter added personalized value to my care. And each did so in a way that made my experience as comfortable as possible.
The multiple care providers did not need to spend a lot of time talking about my case, or debating decisions, the criteria for key decisions had been pre-established within standard processes and informed by best practices. For example, the triage nurse didn’t have to think twice about calling for an electrocardiogram. The decision had already been made that the EKG would be done based on the circumstances. The decision was built into a well-thought-out, well-established process, that has been trained upon, and has become part of the organizational culture. I am convinced that if I entered that hospital on a different day, with different staff on duty, the decisions made, and the care I received would not have been different.
The cost in time and money for doing the EKG had been minimized by assigning a specialist with a mobile cart able to move quickly to the patient, do the test, and move on. While the technician did the test, the triage nurse was able to finish entering comments in my record then shift focus to another case.
In this case, person-time is more valuable than the equipment and consumables. The efficient system makes it easy to reduce risks, the risk of not doing the test, missing or delaying a diagnosis, or wasting the time of a (higher paid) physician if the test wasn’t available when needed later.
My quality of my care was dramatically improved because someone, somewhere, figured out that employing a separate EKG technician, and assigning that person to device on a mobile cart, is more efficient than that triage nurse or someone else doing that job at a different location. And not only did someone come up with the idea. The practice of having a roving EKG technician has been tested, measured, and proven to be an effective practice. That EKG technician may have been cross-trained to perform other duties but was available at the moment the triage nurse called.
I was served directly by the following health care professionals during my brief four-hour emergency room stay:
1. Triage registration desk clerk (with desktop computer)
2. Triage nurse (with desktop computer)
3. EKG technician (with mobile cart)
4. Aide, for transport to room
5. ER intern for initial diagnosis/ test (with wall-mount computer in hospital room)
6. ER nurse assist with test sample
7. Registration clerk (with mobile cart)
8. X-Ray technician (with mobile cart)
9. Medical technician (training to be an EMT) for blood sample and to set up I.V.
10. Aide, for comfort ...blanket, ice chips
11. Room attendant for trash and biohazard removal
12. RN to administer medication via I.V.
13. Doctor to confirm diagnosis and treatment plan (used in room computer)
14. Nurse for discharge (computer at nurse’s station to print discharge instructions and patient sign-out)
15. Emergency waiting room information desk clerk -- provides service to waiting family members (desktop computer to look up patient admittance info) …and parking validation.
I realize that there are countless others that I didn’t see that made it possible for me to receive the care that I did. Even though these many professionals may not see each other, they were all able to focus on my care informed by a common information system and common set of procedures and professional practices.
It is worth noting that I felt like I was being better cared for by many professionals in short intervals than I would have if I had one jack-of-all-traits person with me for extended periods of time. More importantly, the actual quality of my care was better because specialists were employed and because the processes established made it easy for those specialists to use effective practices for each step in my care.
There is a critical need to transform institutions of learning into organizations optimized to meet individual learner needs. The medical model is a good place to start in defining the “Learning Care System” to replace our outdated education system.
What I noticed first from my emergency room experience was the number of people involved in my care. I was served directly by nine different care providers in just the first half-hour or so. The actions of each care provider were informed by the work the others had done, coordinated, and focused on my individual needs just long enough to provide the needed care.
I was amazed at the efficiency with which multiple ‘specialists’ each provided just the right care at just the right time with just the right information to support their decisions and actions. My time was limited with each specialist, but each encounter added personalized value to my care. And each did so in a way that made my experience as comfortable as possible.
The multiple care providers did not need to spend a lot of time talking about my case, or debating decisions, the criteria for key decisions had been pre-established within standard processes and informed by best practices. For example, the triage nurse didn’t have to think twice about calling for an electrocardiogram. The decision had already been made that the EKG would be done based on the circumstances. The decision was built into a well-thought-out, well-established process, that has been trained upon, and has become part of the organizational culture. I am convinced that if I entered that hospital on a different day, with different staff on duty, the decisions made, and the care I received would not have been different.
The cost in time and money for doing the EKG had been minimized by assigning a specialist with a mobile cart able to move quickly to the patient, do the test, and move on. While the technician did the test, the triage nurse was able to finish entering comments in my record then shift focus to another case.
In this case, person-time is more valuable than the equipment and consumables. The efficient system makes it easy to reduce risks, the risk of not doing the test, missing or delaying a diagnosis, or wasting the time of a (higher paid) physician if the test wasn’t available when needed later.
My quality of my care was dramatically improved because someone, somewhere, figured out that employing a separate EKG technician, and assigning that person to device on a mobile cart, is more efficient than that triage nurse or someone else doing that job at a different location. And not only did someone come up with the idea. The practice of having a roving EKG technician has been tested, measured, and proven to be an effective practice. That EKG technician may have been cross-trained to perform other duties but was available at the moment the triage nurse called.
I was served directly by the following health care professionals during my brief four-hour emergency room stay:
1. Triage registration desk clerk (with desktop computer)
2. Triage nurse (with desktop computer)
3. EKG technician (with mobile cart)
4. Aide, for transport to room
5. ER intern for initial diagnosis/ test (with wall-mount computer in hospital room)
6. ER nurse assist with test sample
7. Registration clerk (with mobile cart)
8. X-Ray technician (with mobile cart)
9. Medical technician (training to be an EMT) for blood sample and to set up I.V.
10. Aide, for comfort ...blanket, ice chips
11. Room attendant for trash and biohazard removal
12. RN to administer medication via I.V.
13. Doctor to confirm diagnosis and treatment plan (used in room computer)
14. Nurse for discharge (computer at nurse’s station to print discharge instructions and patient sign-out)
15. Emergency waiting room information desk clerk -- provides service to waiting family members (desktop computer to look up patient admittance info) …and parking validation.
I realize that there are countless others that I didn’t see that made it possible for me to receive the care that I did. Even though these many professionals may not see each other, they were all able to focus on my care informed by a common information system and common set of procedures and professional practices.
It is worth noting that I felt like I was being better cared for by many professionals in short intervals than I would have if I had one jack-of-all-traits person with me for extended periods of time. More importantly, the actual quality of my care was better because specialists were employed and because the processes established made it easy for those specialists to use effective practices for each step in my care.
There is a critical need to transform institutions of learning into organizations optimized to meet individual learner needs. The medical model is a good place to start in defining the “Learning Care System” to replace our outdated education system.
Friday, March 20, 2009
Stewardship of Stimulus…What Will Education Agencies Learn from Private Enterprise?
It has been a while since I posted to this blog. That is in part because I’ve been waiting to see how the change in the U.S. presidency would change the education environment, and in part because I’ve been busy with some very interesting work. In October I posted thoughts on short-sightedness of bailing out financial institutions while failing to address the long-term needs of our education systems and workforce capacity. The world has changed a great deal since October 2008. An abundance of economic stimulus money is flowing to the public education system and federal priorities for improvement are tied to those funds.
The American Recovery and Reinvestment Act of 2009 is about to dump about $100 Billion dollars into the public education system. The federal government plans to release about 95% of the funds in the next six months. The money will be spent quickly, but the funding is meant to be more than a short-term stimulus. The hope is that this money and the federal priorities tied to it will make the U.S. education system better able to equip citizens for the globally competitive workforce with 21st century skills.
But with so much money coming so fast, and so many people trying to get their hands on it, there is a strong possibility that much of the money will not go to the lasting improvements that are needed. I don’t have a crystal ball, but I feel pretty confident in my prediction that in the coming months we will be hearing stories of fraud and improper use of taxpayer money. Recently public outrage has been over corporate executives receiving multimillion dollar bonuses from bailout money. Soon we can expect the outrage to shift to leadership of one or more public institutions that will have been found to have misused the stabilization funds. The private sector does not have a monopoly on greed.
Some intentionally improper use is inevitable when so much money is being handed out so quickly, and some of those stories will make headlines. What may not make headlines are many ways in which the funds will used. Some uses will be highly effective and deliver a good return on investment for tax-payer dollars. Other uses will be inefficient, wasteful, or not achieve intended results.
I hope that education industries don’t follow the example of some private sector bailout recipients, but when it comes to making sure that the money is used to its greatest potential there are a few things that education agencies can learn, and are learning, from private enterprise. Good stewardship requires more than good intentions. It also requires knowledge about how best to manage entrusted resources and a will to put that knowledge into action.
Management systems first developed in the private sector are being adopted by public sector education agencies to ensure that the effort of the organization is focused on the right things and that resources are used efficiently.
Harvard University and the University of Virginia have formed partnerships between their graduate schools of business and the graduate schools of education. These partnerships are working with state and local education leaders across the county put proven business methodologies into practice. Tools such as the balanced scorecard, process management, and project management oversight process are proving highly effective in improving the effectiveness and efficiency of education organizations.
Return on investment is different for education. The bottom line is not measured in dollars, but in student learning. The products of highly effective education organizations are well equipped students, ready to enter the global workforce and participate as contributing citizens in their communities, the nation, and the world.
The investment being made by the U.S. taxpayer through the stimulus funds should bring about long-term improvements, moving our 19th century education system into the 21st century. We look to the leaders of our public education agencies. Some will follow the bad examples from the private sector. Others put to work what can be learned from private enterprise, practice good stewardship, and maximize the return on this once-in-a-lifetime investment.
The American Recovery and Reinvestment Act of 2009 is about to dump about $100 Billion dollars into the public education system. The federal government plans to release about 95% of the funds in the next six months. The money will be spent quickly, but the funding is meant to be more than a short-term stimulus. The hope is that this money and the federal priorities tied to it will make the U.S. education system better able to equip citizens for the globally competitive workforce with 21st century skills.
But with so much money coming so fast, and so many people trying to get their hands on it, there is a strong possibility that much of the money will not go to the lasting improvements that are needed. I don’t have a crystal ball, but I feel pretty confident in my prediction that in the coming months we will be hearing stories of fraud and improper use of taxpayer money. Recently public outrage has been over corporate executives receiving multimillion dollar bonuses from bailout money. Soon we can expect the outrage to shift to leadership of one or more public institutions that will have been found to have misused the stabilization funds. The private sector does not have a monopoly on greed.
Some intentionally improper use is inevitable when so much money is being handed out so quickly, and some of those stories will make headlines. What may not make headlines are many ways in which the funds will used. Some uses will be highly effective and deliver a good return on investment for tax-payer dollars. Other uses will be inefficient, wasteful, or not achieve intended results.
I hope that education industries don’t follow the example of some private sector bailout recipients, but when it comes to making sure that the money is used to its greatest potential there are a few things that education agencies can learn, and are learning, from private enterprise. Good stewardship requires more than good intentions. It also requires knowledge about how best to manage entrusted resources and a will to put that knowledge into action.
Management systems first developed in the private sector are being adopted by public sector education agencies to ensure that the effort of the organization is focused on the right things and that resources are used efficiently.
Harvard University and the University of Virginia have formed partnerships between their graduate schools of business and the graduate schools of education. These partnerships are working with state and local education leaders across the county put proven business methodologies into practice. Tools such as the balanced scorecard, process management, and project management oversight process are proving highly effective in improving the effectiveness and efficiency of education organizations.
Return on investment is different for education. The bottom line is not measured in dollars, but in student learning. The products of highly effective education organizations are well equipped students, ready to enter the global workforce and participate as contributing citizens in their communities, the nation, and the world.
The investment being made by the U.S. taxpayer through the stimulus funds should bring about long-term improvements, moving our 19th century education system into the 21st century. We look to the leaders of our public education agencies. Some will follow the bad examples from the private sector. Others put to work what can be learned from private enterprise, practice good stewardship, and maximize the return on this once-in-a-lifetime investment.
Friday, October 31, 2008
Instant Decision Making and Optimized Learning
Last weekend I had the privilege of conducting a session at the Missouri School Boards Association annual conference. The session topic was the application of proven business practices and technology tools that can help education organizations achieve success.
A key-note speaker at the conference was, Mark David Milliron. I found it interesting that both of us pointed to the "Good to Great" findings of Jim Collins and his research team in our presentations, and both of us addressed the gap between innovation in schools and innovation in other areas of our would. However, Milliron really made clear how wide is that gap with two illustrations...
One illustration was the speed at which an Amazon.com reacts, with every click, to continually and instantly personalize the experience for every customer. e.g. When you buy or even view a book it instantly knows what "you may also be interested in..." and responds accordingly. ...Now imagine if we could support learning in that way, continually and instantly adapting to an individual learning styles, interests, and background knowledge informed by new data at every step in the learning process. That would be a big gap between an annual snapshot of data from a high stakes test that has too-little-too-late influence on delivery of instruction, and even then for groups of students, not individuals.
The other example was how quickly your credit card company can respond if your card gets into the wrong hands. Artificial intelligence programs continuously monitor patterns of use and will generate an alert leading to a from a call center half way around the world to check with you if your card has been stolen. Why can't we use this kind of innovation to trigger interventions for at-risk students? For example, if a certain number of absentees indicated that a student is at risk to drop out, why shouldn't continuously monitor absences and trigger an immediate intervention rather than looking at the data after the student has dropped out of school?
A key-note speaker at the conference was, Mark David Milliron. I found it interesting that both of us pointed to the "Good to Great" findings of Jim Collins and his research team in our presentations, and both of us addressed the gap between innovation in schools and innovation in other areas of our would. However, Milliron really made clear how wide is that gap with two illustrations...
One illustration was the speed at which an Amazon.com reacts, with every click, to continually and instantly personalize the experience for every customer. e.g. When you buy or even view a book it instantly knows what "you may also be interested in..." and responds accordingly. ...Now imagine if we could support learning in that way, continually and instantly adapting to an individual learning styles, interests, and background knowledge informed by new data at every step in the learning process. That would be a big gap between an annual snapshot of data from a high stakes test that has too-little-too-late influence on delivery of instruction, and even then for groups of students, not individuals.
The other example was how quickly your credit card company can respond if your card gets into the wrong hands. Artificial intelligence programs continuously monitor patterns of use and will generate an alert leading to a from a call center half way around the world to check with you if your card has been stolen. Why can't we use this kind of innovation to trigger interventions for at-risk students? For example, if a certain number of absentees indicated that a student is at risk to drop out, why shouldn't continuously monitor absences and trigger an immediate intervention rather than looking at the data after the student has dropped out of school?
Tuesday, October 7, 2008
Disrupting Class
The book Disrupting Class (Christensen, Claton; Horn, Micheal; Johnson, Curtis –McGraw-Hill, 2008) reflects aspects of the comprehensive “student-centric” solution that we address in Approaching 100% by 2014. It presents a compelling case about how and when disruptive innovation might cause our education system to reach a student-centric tipping point.
The projections by Christensen and his colleagues may be plausible. However, there are factors not included in the Disrupting Class analysis that may accelerate or decelerate such a transition. What we can say with some degree of certainty is that the shift to a student-centric model is inevitable, and will be required to approach the goal of 100% of learners achieving a minimum level of competency on core standards.
“At some point, administrators, school committees, and teachers unions will recognize that even without explicit administrative decisions ever having been made, student-centric learning will have become mainstream. The substitution curve analysis in Chapter 4 suggests that this will happen in approximately 2014 when online courses have a 25 percent market share in high schools”
(Christensen, p143)
The projections by Christensen and his colleagues may be plausible. However, there are factors not included in the Disrupting Class analysis that may accelerate or decelerate such a transition. What we can say with some degree of certainty is that the shift to a student-centric model is inevitable, and will be required to approach the goal of 100% of learners achieving a minimum level of competency on core standards.
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