Tag Archives: school reform

Charter Schools’ 25th Anniversary: Why This Reform Has Lasted (Part 2)

In investigating school reforms that have taken place over the last century and a half, I have divided them into incremental and fundamental changes (see here and here). Incremental reforms are those that aim to improve the existing structures of schooling; the premise behind incremental reforms is that the basic structures are sound but need improving to remove defects. The car is old but if it gets fixed it will become dependable transportation. It needs tires, brakes, a new battery, and a water pump–incremental changes. Fundamental reforms are those that aim to transform, to alter permanently, those very same structures; the premise behind fundamental reforms is that basic structures are flawed at their core and need a complete overhaul, not renovations. The old jalopy is beyond repair. We need to get a completely new car or consider different forms of transportation–fundamental changes.

If new courses, new staff, summer schools, higher standards for teachers, and increased salaries are clear examples of enhancements to the structures of public schooling, then the introduction of the age-graded school (which gradually eliminated the one-room school) Progressive educators’ broadening the school’s role to intervene in the lives of children and their families (e.g., to provide medical and social services) in the early 20th century, and more recently the introduction of charter schools in the 1990s are examples of fundamental reforms that stuck.

The platoon school, classroom technologies from film and radio to laptops and tablets, project-based learning, and charter schools, however, are instances of attempted fundamental change in the school and classroom since the early 20th century that were adopted, incorporated into many schools, and, over time, either downsized into incremental ones or slipped away, leaving few traces of their presence. Why did some incremental reforms get institutionalized and most of the fundamental ones either became just another part of the “system” or simply disappeared?

Some scholars have analyzed those hardy reforms that survived and concluded that a number of factors account for their institutionalization (see here and here).

They enhance, not disrupt existing structures. Many prior reforms added staffing, particularly specialists, to deal with the variety of children that attended schools. Separate teachers for children with disabilities, math and reading teachers, counselors to help children pick courses to take and to prepare for college and the job market. Similarly, additional space for playgrounds, lunchrooms, and health clinics enhanced the school program. charters remain age-graded schools just like traditional neighbors. Moreover, after 25 years some charter school heads are working out cooperative arrangements with district school boards that help one another (see here and district_charter_collaboration_rpt).

They are easy to monitor. These reforms were visible. They could be counted (e.g., hot lunches, health clinics, year-round schools, and charters. Such easy monitoring gave taxpayers evidence that the services were being rendered and changes had occurred.

They create constituencies that lobby for continuing support. New staff positions such as special education teachers and counselors created demands for administrators and supervisors to monitor their work. Newly certified educators, imbued with a fervent belief in their mission, argued for more money. Consider that the spread of charter school and competition for state funds flowing to school districts created interest groups (e.g., charter advocates) that lobbied donors and state officials for more funds. Commercial interests serving new programs (e.g., for-profit cyber schools, vendors of computer products) championed charters. Finally, parents persuaded by the influence of the services and programs on their children joined educators to create informal coalitions advocating the continuation of these reforms.

This answer to the question of why some reforms stick has a superficial neatness that omits some reforms that fail to fit nicely into the above categories (e.g., desegregation of schools since 1954). Moreover, there is a static quality implied in the notion of reforms that attained  longevity, that is, such reforms were incorporated into public schools and remained as they were as if frozen in time. Those fundamental reforms that became incrementalized and stuck, however, continued to change as they adapted to ever-shifting demands and resources.

Studies of non-school organizations offer richer clues that go beyond the crisp, static answers suggested here. For example, the theories of Robert Merton, Philip Selznick, Alvin Gouldner, and their students produced numerous studies of organizations founded in the heat of reform movements whose original goals have been transformed over decades although their names remain the same. The imperative for organizational survival vibrates strongly in Selznick’s (1949) analysis of the Tennessee Valley Authority, and Mayer Zald and Patricia Denton’s (1963) investigation of the Young Men’s Christian Association.

Other studies, closer to public schools, also document adaptability in organizations founded to end social ills. These institutions maintained their professed goals yet shifted in what they did operationally in order to survive. David Rothman’s (1980) analysis of 19th century reformers’ inventions of rehabilitative prisons, juvenile courts, and reformed mental asylums records the painful journey of institutions established in a gush of zeal for improvement of criminals, delinquents, and the mentally ill; within decades, the reformers ended up pursuing scaled-down goals that maintained the interests of those who administered the institution. Barbara Brenzel (1983) analyzed a half-century’s history of the first reform school for girls in the nation (State Industrial School for Girls in Lancaster, Massachusetts). Here, again, the initial goals of reforming poor, neglected, and potentially wayward girls through creating family-like cottages and separating younger from older girls gave way to goals that stressed order and control.

The point is that there are institutional reasons why some reforms are like shooting stars that flare and disappear and some reforms stick. Organizational and political reasons (e.g., vague and multiple goals, innovations that fit existing structures, are easy to monitor, and have active constituencies) explain how schools and districts adapt their goals, structures, and processes to an uncertain, ever changing environment to incorporate new ideas and practices.

And that is why charter schools will be around for the next quarter century.





Filed under Reforming schools

Algorithms in Use: Evaluating Teachers and “Personalizing” Learning (Part 2)

In Part 1, I made the point that consumer-driven or educationally-oriented algorithms for all of their mathematical exactness and appearance of objectivity in regression equations contain different values among which programmers judge some to be more important than others.  In making value choices (like everyone else, programmers are constrained by space, time, and resources), decisions get made that have consequences for both teachers and students. In this post, I look first at those algorithms used to judge teachers’ effectiveness (or lack of it) and then I turn to “personalized learning” algorithms customized for individual students.

Washington, D.C.’s IMPACT program of teacher evaluation

Much has been written about the program that Chancellor Michelle Rhee created during her short tenure (2007-2010) leading the District of Columbia public schools (see here and here). Under Rhee, IMPACT,  a new system of teacher evaluation has been put into practice. The system is anchored in The “Teaching and Learning Framework,”  that D.C. teachers call the “nine commandments” of good teaching.

1. Lead well-organized, objective-driven lessons.

2. Explain content clearly.

3. Engage students at all learning levels in rigorous work.

4. Provide students with multiple ways to engage with content.

5. Check for student understanding.

6. Respond to student misunderstandings.

7. Develop higher-level understanding through effective questioning.

8. Maximize instructional time.

9. Build a supportive, learning-focused classroom community.

IMPACT uses multiple measures to judge the quality of teaching. At first, 50 percent of an annual evaluation was based upon student test scores; 35 percent based on judgments of instructional expertise (see “nine commandments”) drawn from five classroom observations by the principal and “master educators,” and 15 percent based on other measures. Note that policymakers initially decided on these percentages out of thin air. Using these multiple measures, IMPACT has awarded 600 teachers (out of 4,000) bonuses ranging from $3000 to $25,000 and fired nearly 300 teachers judged as “ineffective” in its initial years of full operation. For those teachers with insufficient student test data, different performance measures were used. Such a new system caused much controversy in and out of the city’s schools (see here and here)

Since then, changes have occurred. In 2012, the 50 percent of a teacher’s evaluation based on student test scores had been lowered to 35 percent (why this number? No one says) and the number of classroom observations had been reduced. More policy changes have occurred since then (e.g., “master educator” observations have been abolished and now principals do all observations; student surveys of teachers added). All of these additions and subtractions to IMPACT mean that the algorithms used to judge teachers have had to be tweaked, that is, altered because some variables in the regression equation were deemed more (or less) important than others. These policy changes, of course, are value choices. For a technical report published in 2013 that reviewed IMPACT, see here.

And the content of the algorithms have remained secret. An email exchange between the overseer of the algorithm in the D.C. schools and a teacher (who gave her emails to a local blogger) in 2010-2011 reveal the secrecy surrounding the tinkering with such algorithms (see here). District officials have not yet revealed in plain language the complex algorithms to teachers, journalists, or the general public. That value judgments are made time and again in these mathematical equations is clear. As are judgements in the regression equations used to “personalize learning.”

Personalized Learning algorithms

“The consumerist path of least resistance in America takes you to Amazon for books, Uber for transportation, Starbucks for coffee, and Pandora for songs. Facebook’s ‘Trending’ list shows you the news, while Yelp ratings lead you to a nearby burger. The illusion of choice amid such plenty is easy to sustain, but it’s largely false; you’re being herded by algorithms from purchase to purchase.”

Mario Bustillos, This Brand Could be Your Life, June 28, 2016

Bustillos had no reason to look at “personalized learning” in making her case that consumers are “herded by algorithms from purchase to purchase.” Had she inquired into it, however, she would have seen the quiet work of algorithms constructing “playlists” of lessons for individual students and controlling students’ movement from one online lesson to another absent any teacher hand-prints on the skills and content being taught. Even though the rhetoric of “personalized learning” mythologizes the instructional materials and learning as student-centered, algorithms (mostly proprietary and unavailable for inspection) written by programmers making choices about what students should learn next are in control. “Personalized learning” is student-centered in its reliance on lessons tailored to ability and performance differences among students. And the work of teachers is student-centered in coaching, instructing, and individualizing their attention as well as monitoring small groups working together. All of that is important, to be sure. But the degree to which students are making choices out of their interests and strengths in a subject area, such as math, they have little discretion. Algorithms rule (see here, here, and here).

Deeply embedded in these algorithms are theories of learning that seldom are made explicit. For example, adaptive or “personalized learning” are contemporary, high-tech versions of old-style mastery learning. Mastery learning, then and now, is driven by behavioral theories of learning. The savaging of “behaviorism” by cognitive psychologists and other social scientists in the past few decades has clearly given the theory a bad name. Nonetheless, behaviorism and its varied off-shoots drive contemporary affection for “personalized learning” as it did for “mastery learning” a half-century ago (see here and here). I state this as a fact, not a criticism.

With advances in compiling and analyzing masses of data by powerful computers, the age of the algorithm is here. As consumers, these rules govern choices we make in buying material goods and, as this post claims, in evaluating teachers and “personalized learning.”







Filed under school reform policies, technology use

Consumer Choice in Schooling: Algorithms and Personalized Learning (Part 1)

“The consumerist path of least resistance in America takes you to Amazon for books, Uber for transportation, Starbucks for coffee, and Pandora for songs. Facebook’s ‘Trending’ list shows you the news, while Yelp ratings lead you to a nearby burger. The illusion of choice amid such plenty is easy to sustain, but it’s largely false; you’re being herded by algorithms from purchase to purchase.”

Mario Bustillos, This Brand Could be Your Life, June 28, 2016


I wish I had written that paragraph. It captures a definite feature not only of our consumerist-driven society but also in recent school reform (e.g., the growth of charter schools and expanded parental choice). I also include the media hype and techno-enthusiasm for “personalized learning.” The centerpiece of any form of “personalized learning” (or “adaptive learning“) is algorithms for tailoring lessons to individual students (see here, here, and here). What Bustillos omits  in the above article about the dominance of consumerism driven by algorithms is that regression equations embedded in algorithms make predictions based on data. Programmers decide on how much weight to put on particular variables in the equations. Such decisions are subjective; they contain value judgments about the independent and dependent variables and their relationship to one another. The numbers hide the subjectivity within these equations.

Like Facebook designers altering its algorithm so as to direct news tailored to each Facebook consumer “to put a higher priority on content shared by friends and family,” software engineers create different versions of  “personalized learning” and insert value judgments into the complicated regression equations with which they have written for online lessons. These equations are anchored in the data students produce in answering questions in previous lessons. These algorithms predict (not wholly since engineers and educators do tweak–“massage” is a favored word–the equations) what students should study and absorb in individualized, daily, online software lessons (see here).

Such “personalized” lessons alter the role of the teacher for the better, according to promoters of the trend. Instead of covering content and directly teaching skills, teachers can have students work online thereby freeing up the teacher to coach, give individual attention to students who move ahead of their classmates and those who struggle.

Critics, however, see the spread of online, algorithmic-based lessons as converting teaching to directing students to focus on screens and automated lessons thereby shrinking the all-important role of teacher-student relationships, the foundation for social, moral, and cognitive learning in public schools. Not so, advocates of “personalized learning” aver. There might be fewer certified teachers in schools committed to lessons geared to individual students (e.g., Rocketship) but teachers will continue to perform as mentors, role models, coaches, and advisers not as mere purveyors of content and skills.

As in other policy discussions, the slippage into either/or dichotomies beckons. The issue is not whether or not to use algorithms since each of us uses algorithmic thinking daily. Based on years of experiential data we have compiled in our heads (without regression equations) step-by-step routines just to get through the day (e.g., which of the usual routes to work should I take; how best to get the class’s attention at the beginning of a lesson). Beyond our experiences, however, we depend on mathematical algorithms embedded in the chips that power our Internet searches Internet, control portions of our driving cars and operate home appliances.

The issue is not that algorithms are value-free (they are not) or data rich (they are). The issue is whether practitioners and parents–consumers of fresh out-of-the-box products–come to depend automatically on carefully constructed algorithms which contain software designers’ value judgments displayed in flow charts and written into code for materials and lessons students will use tomorrow. Creators of algorithms (including ourselves) juggle certain values (e.g., favorite theory of learning, student-centered instruction, small group collaboration, correctness of information, increasing productivity and decreasing cost, ease of implementation) and choose among them  in constructing their equations. They judge what is important and select among those values since time, space, and other resources are limited in creating the “best” or “good enough” equation for a given task. Software designers choose to give more weight to some variables over others–see Facebook decision above. Rich, profuse data, then, never speaks for itself. Look for the values embedded in the algorithmic equations. Such simple facts are too often brushed aside.

What are algorithms?

Wikipedia’s definition of an algorithm is straight forward: a sequence of steps taken to solve a problem and complete a task. Some images make the point for simple algorithms.











Or if you want a Kahn Academy video to explain an algorithm, see here.

Complex algorithms

Most algorithms are hardly simple, however. Amazon’s proprietary algorithms on searches and popularity of books, for example, are unavailable to the public yet are heavily leaned upon by advertisers, authors, and consumers (e.g., also Amazon’s  algorithmic feature that appears on your screen: “customers who viewed this also viewed….”).  Among school reformers interested in evaluating teachers on the basis of students’ test scores, algorithms and their complex regression equations have meant the difference between getting a bonus or getting fired, for example,  in Washington, D.C. . And for those “personalized learning” advocates eager to advance student-centered classrooms,  algorithms  contain theories of action of what-causes-what that tilt toward one way of learning. In short, software designers’ value judgements matter as to what pops out at the other end of the equation. and then is used in making an evaluative judgment and an instructional decision.

Part 2 will look at values in algorithms that evaluate teachers and customize learning.



Filed under how teachers teach, school reform policies, technology use

Hype on Steroids: Self-Driving Cars and School Technologies

A full week of mainstream and social media swept across the nation about the death of a Tesla car owner killed in Florida using the self-driving option. With the auto-pilot function turned on, the Tesla driver collided with a tractor-trailer and became the first known fatality in the industry’s surge to produce self-driving cars. Google and Tesla and 30 other companies (e.g., Honda, Ford, GM,Toyota) compete for what is hyped as the “next big thing”; such cars, they claim, will “disrupt” the century-old personal transportation market.

A Morgan Stanley Blue Paper announced in 2013:

Autonomous cars are no longer just the realm of science fiction.They are real and will be on roads sooner than you think. Cars with basic autonomous capability are in showrooms today, semi-autonomous cars are coming in 12-18 months, and completely autonomous cars are
set to be available before the end of the decade

Tesla’s founder, Elon Musk said the self-driving function on the Tesla meant that “[t]he probability of having an accident is 50 per cent lower if you have Autopilot on” …. “Even with our first version, it’s almost twice as good as a person.”

Skeptics have tossed in their two cents (see here and here; for rebutting skeptics, see here) but when it comes to questioning new technologies in U.S. culture, skeptics are alien creatures.

While the hype pumping up self-driving cars can lead to accidents and deaths, no such serious consequences accompany promoters of technological innovations who have promised increased teacher efficiency, improved student achievement, and the end of low-performing schools for the  past half-century.  Need I mention that Google has a “Chief Evangelist for Global Education?”

Nothing surprising about hype (even when  injected with steroids)  in a consumer-driven, highly commercial society committed to practicing democracy. Hype is hype either for self-driving cars or for school technologies. Parsing the hyped language and images becomes important because real-life consequences flow from these words and pictures.


Consider these advertisements championing new technologies since the 1950s.





Over-stated claims are  commonplace when it comes to pumping up the benefits of the “next big thing.” Early adopters of new technologies discover the bugs in new hardware and software soon enough.  Glitches, however, seldom dissuade this crowd from peering around the corner for its replacement.

Does hype serve any social and political purpose other than to stimulate consumers to buy the product? I believe it does.

1. Over-the-top statements strengthen the popular belief that change is “good” for individuals and society overall. Not only is change “good” for Americans but in the technology industry and culture of school reform, change morphs into improvement. In Silicon Valley argot, “making the world a better place,” means a new product, a new service, a new app will improve life (a parody of this oft-repeated phrase can be seen here)

Equating change with improvement is a cognitive error. Surely, an improvement implies a change has occurred but because the change has happened, improvement does not necessarily follow. A moment’s thought would quickly squelch equating change with improvement. Stepping on a scale and seeing that you have gained five pounds while on a low-carb diet is clearly a change but not, in your view, an improvement. Think of a divorce in a family. The spouse initiating the divorce sees the split as a change for the better but for the others involved including children, few would see it as an improvement with two homes, living with different parents or weekend visits. Change occurs constantly but improvement is in the mind of the beholder.

Consider whether a new app that has a “smart” button and zipper that alerts you if your fly is down or another app that locates rentable yachts are improvements to one’s life (see here). To those individuals who buy and download these apps they appear as improvements promising a better life but to others, they appear as trivial indulgences that hardly make the “world a better place.”

School reformers who believe that changes lead to improvements in teaching and learning, for example, often refer to gains in student test scores, increases in teacher productivity (i.e., less time to do routine tasks), and other measurable outcomes as evidence of  better schooling. Reformers holding divergent values (e.g., higher civic engagement, student well-being), however, would differ over whether test scores, et. al. are improvements. Quite often, then, the definition of improvement depends upon who does the defining and the values they prize.

2. Hype over new technologies raises questions about the existing institution’s quality.  Consider current health care where millions still lack health insurance, emergency rooms are over-crowded, wait time to see specialists physicians increases, and patients get less and less time when they do see their doctors. Hyping the “next big thing” in medical technology becomes a direct criticism of existing health care. Think of “hospital in a box,” or patient kiosks placed in pharmacies, where ill people go to the kiosk for video conferencing with one or more doctors about what ails them. Such new technologies raises implicit questions about access to adequate health care and to what degree the relationship between doctor and patient is important in improving health.

Or consider the thousands of lives lost on the nation’s roads to accidents and human error in driving. Self-driving cars, once prevalent on the nation’s highways will, promoters claim, dramatically reduce the 32,000 deaths in car accidents while increasing worker productivity since with self-driving cars owners can complete other tasks that heretofore would have not been done. Self-driving cars raises anew questions about the lack of adequate public transportation and a society committed to one-person-per car.

And hype for technological innovations in schools for “personalized” or “adaptive” learning pictures the existing system as factory-like  whole-class, age graded, teacher-dominated instruction that ignores, even neglects individualized lessons, student-centered learning, and reconfigured classrooms.

3. Hype shrinks the time to show results to immediately. Most software products in the educational arena, for example, take time for teachers and students to grasp, understand, and use them in lessons. Education proceeds by short not long steps. Hyping these products leave the distinct impression that unless the desired result hasn’t happened in a few months then someone (note the beginnings of blame) has failed to do it right. And it ain’t the software developer.

4. Software and hardware developers come to believe their own hype. The cliche of “drinking the Kool-Aid–applies here and such self-deception occurs. And when it does, CEOs of start-ups and other companies start making short-cuts to get products into schools and stores. Those short-cuts increase software glitches, highten arguments with consumers of the products, and diminish faith in the innovation.

These outcomes of hype are not justifications for its ubiquity. They  help me understand the role that it (and its cousin, “magical thinking”) perform in U.S. society.







Filed under how teachers teach, school reform policies, technology use

Using Computers To Transform Teaching and Learning: The Flight of a Butterfly Or a Bullet?*

As regular readers of this blog know, I have embarked on another project examining “best cases” of teachers, schools, and districts integrating computers into daily activities.  After four months of classroom observations, interviews with teachers and principals, and much reading I have begun to think of this project as a possible book. Much remains to be done, however, before it becomes one. In the fall, I will visit more classrooms and schools to do observations and interviews. I will do more reading of national surveys, case studies, and rigorous inquiries into what teachers and students do with devices. But the makings of a book are there in my mind.

So here is part of a proposal that I have sent to a publisher to see if they are interested. Subsequent posts will elaborate on other parts of this book proposal.

Overview and Rationale for Proposed Book

For over 30 years, I have examined the adoption and use of computers in schools (Teachers and Machines, 1986; Oversold and Underused, 2001, Inside the Black Box, 2013). I looked at the policy hype and over-promising accompanying new technologies in each decade. The question I asked was: what happens in schools and classrooms after the school board and superintendent adopt a policy of buying and deploying new technologies to improve schooling? This is the central question for any reform-minded policymaker, entrepreneur, parent, and practitioner because if teaching practices fail to change in the desired direction embedded in the policy then the chances of any changes in student performance are diminished considerably. Thus, in pursuing the issue of changes in classroom lessons in books, articles, and my blog, I moved back and forth between adopted policies for using computers, their classroom implementation, and shifts in teaching practices.

I described and analyzed computers in schools and classrooms across the U.S. including the highly touted Silicon Valley in the San Francisco Bay area. I tracked how these advocates and donors were often disappointed in how little school and classroom practice changed, anemic results in student achievement, and uncertainties in getting the right jobs after graduation, given the claims accompanying these devices and software.

There have been, however, occasional bright spots in individual teachers thoroughly integrating laptops and tablets into their practice and moving from teacher- to student-centered classrooms. And there were scattered instances of schools and districts adopting technologies wholesale and slowly altering cultures and structures to improve how teachers teach and students learn. I documented those occasional exemplars but such instances of classroom, school, and district integration were isolated and infrequent.

What slowly became clear to me over the years of studying the use of computers to improve how teachers teach and students learn and attain the overall purposes of public schooling is that policymakers have avoided asking basic questions accompanying any policy intended to reshape classroom practice. I concluded that those questions and their answers are crucial in understanding the role that computers in schools perform when it comes to teaching and learning.

This conclusion is behind my writing this book.

Reform-driven policymakers, entrepreneurs, researchers, practitioners, and parents have sought substantial changes over the past three decades in classrooms, schools, and districts to transform schooling while improving student outcomes. Yet, too often, they either avoided the inevitable steps that need to occur for such changes to materialize in schools or hastily leap-frogged over important ones. Four simple questions capture the essential steps in going from adopted policy to classroom practice.

  1. Did policies aimed at improving student performance get fully, moderately, or partially implemented?
  2. When implemented fully, did they change the content and practice of teaching?
  3. Did changed classroom practices account for what students learned?
  4. Did what students learn meet the intended policy goals?

These questions apply to innovations aimed at improving student academic performance such as creating small high schools and launching charter schools to states and districts adopting Common Core standards, competency-based learning and project-based teaching. Most importantly for this book, these questions pertain to making new technologies from laptops to hand-held devices not only accessible to every student but also expecting teachers to regularly use computers in lessons.

The questions emphasize the critical first step of actually implementing the adopted policy. Policies are not self-implementing. They require resources, technical assistance, staff development, and administrators and teacher to work together. This is especially so for teachers who are gatekeepers determining what enters the classroom door.

So without full or moderate implementation of a policy aimed at improving student performance, there is not much sense in pursuing answers to the other questions. Evidence of putting the policy into classroom practice is essential to determining the degree to which a policy is effective (or ineffective).

Once evidence of a policy’s implementation in schools and classrooms is available then the question of whether teaching practices have changed arises. This question gets at the nexus between teaching and learning that has been taken for granted in U.S. schools since the introduction of tax-supported public education nearly two centuries ago: Change teaching and then student learning will change. This is (and has been) the taken-for-granted belief driving reformers for the past century. Determining the degree to which teaching practices have changed in the desired direction and which have remained stable is essential.

The third question closes this circle of teaching producing learning by getting at what students have actually learned as a consequence of altered teaching practices. In the past half-century, policymakers have adopted measures of desired student outcomes (e.g., test scores, graduation rates, attendance, engagement in lessons). They assume that these measures capture what students have, indeed, learned. If teaching practices have changed in the desired direction, then changes in student outcomes (i.e., learning) can be attributed to those changes in classroom practices.

The final question returns to the immediate and long-term purposes of the adopted policy and asks for an evaluation of its intended and unintended outcomes. Immediate purposes might have concentrated on student test scores and graduation rates. Long-term purposes, the overall goals for tax-supported public schools, refer to job preparation, civic engagement, and producing independent and whole human beings.

These questions establish clear linkages between reform-driven policies and teaching practice. They steer this proposed book.

What if, however, policymakers, researchers, entrepreneurs, and parents looked not only at failed uses of classroom computers but also exemplary instances that have actually altered teaching practices to achieve policy ends? Examining how such “best cases” happened and their stability (or lack of it) might unlock the crucial next step of assessing changes in teaching practices and student outcomes.


*The sub-title is a quote used by Philip Jackson, Life in Classrooms (1968), pp. 166-167.


Filed under how teachers teach, school reform policies, technology use

Part 2: Draining the Semantic Swamp of “Personalized Learning” : A View from Silicon Valley

In Part 1, based on what I have seen in 17 teachers’ classrooms in eight schools, I tried to explain what I observed by offering a “personalized learning” continuum. As small as the sample is–I will continue with the project in the Fall and add more classrooms and schools–I wanted to take a first pass at making sense (for myself and readers) of what I saw in schools located at the heart of technological enthusiasm, Silicon Valley. Let me be clear, I value no end of the spectrum more than the other. I have worked hard to strip away value-loaded words that suggest some kinds of “personalized learning” are better than others.

This “personalized learning spectrum,” I pointed out, is anchored in the tangled history of school reform, the family fight a century ago among those Progressives who were efficiency-driven and behaviorist in their solutions to problems of teaching and learning and fellow Progressives who sought student agency,  growth  of the “whole child,” and democratic schooling solving societal problems. Both wings of educational Progressives tried to uproot the traditional whole-group, direct instruction model dominating public schools then and since.

The efficiency-driven, behaviorist wing of the Progressives was victorious by the 1930s and has largely dominated school reform since. Innovations appeared each decade trumpeting the next new thing that would make teaching and learning more efficient and effective. In the 1950s, it was “programmed learning machines” (launched by behavioral psychologist, B.F. Skinner); in the 1970s, it was “mastery learning” (anchored in the work of University of Chicago psychologist Benjamin Bloom) followed by “competency-based” learning in the 1980s. Each of these innovations, in different guises, continue to be found in schools in 2016. In all instances, past and present, psychologists and school reformers broke down knowledge and skills into its small, digestible parts so students could learn at their own pace through individualized lessons and teacher use of “positive reinforcement.” Extrinsic rewards from teachers and, later, software programs, guided students along paths to acquiring requisite skills and knowledge. Promoters of these innovations claimed that these approaches were both efficient and effective in getting students to acquire prescribed content and skills, graduate,  enter the labor market, and become civically engaged adults.

Challenges to this dominant approach to teaching and learning occurred periodically from the student-centered, “whole child” wing of the Progressives who championed project-based teaching, student participation, and collaborative learning to achieve the same desired ends. While these determined efforts to individualize lessons to match academic and ability differences among students and create more student agency in lessons and units rose and fell over the years (e.g., the 1960s, 1990s) incrementally increasing within more and more schools, the dominant efficiency-driven whole-group, teacher-directed approach prevailed.

What has happened recently, however, is that those efficiency-minded school reformers, filled with optimism about the power of new technologies to “transform” teaching and learning, have appropriated the language of “whole child” Progressives.  Imbued with visions of students being prepared for a world where adults change jobs a half-dozen times in a lifetime, these efficiency-minded reformers tout schools that have tailored lessons (both online and offline) to individual students, turned teachers into coaches, and where students collaborate with one another thus reflecting the changed workplace of the 21st century. Efficiency-minded reformers’ victorious capture of the vocabulary of “personalized learning”  has made parsing the present-day world of school policies aimed at making classrooms havens of “personalized learning” most confusing to those unfamiliar with century-old struggles over similar issues.

Now consider the “personalized learning spectrum,” my first pass at making sense of this world I observed in Silicon Valley. At one end are teacher-centered lessons and programs tailored for individual students to progress at that own speed. Such “personal” instructional materials and teaching seek efficient and effective learning using a behavioral approach rich in positive reinforcement that has clear historical underpinnings dating back nearly a century. At the other end of the continuum are, again, century-old efforts to create student-centered whole- and small-group lessons and programs that seek student agency and shape how children grow cognitively, psychologically, emotionally, and physically.  And, of course, on this spectrum hugging the middle of the range are hybrids mixing the two approaches. To get at this spread in 2016 within the eight schools I visited and 17 teachers I observed, I will give examples drawn from earlier posts on this blog and instances elsewhere in the U.S.  of each end of the spectrum and ones that inhabit the center.

At the behaviorist pole of the spectrum where skill-driven lessons tailored to differences among students cluster, different public schools and districts* drawn from across the nation exist such as  New Hampshire Virtual Learning CharterUSC Hybrid High School, and Lindsay Unified School District (California). While these examples inhabit the behaviorist end of the continuum they are not cookie-cutter copies of one another–USC Hybrid High School differs in organization and content from New Hampshire Virtual Learning Charter. Yet I locate these schools and districts at this end of the spectrum because of their overall commitment to using existing online lessons (or crafting their own) anchored in discrete skills and knowledge, aligned to the Common Core standards, and tailored to the abilities and performance of individual students. Even though these schools and programs have appropriated the language of student-centeredness and encourage teachers to coach individuals and not lecture to groups, even scheduling student collaboration during lessons, their teacher-crafted playlists that vary for each student accompanied by assessments of skills and knowledge locates them here. And, finally, these programs seek the ends of thoughtful, fully engaged, and whole human beings–the same as other programs along the spectrum. And they still operate within the traditional format of K-8 and 9-12 age-graded schools

In the middle of the spectrum would be classrooms, schools and districts that have blended learning models (there are more than one) combining personalized lessons for individual students and teacher-directed classroom lessons such as ones taught by Aragon High School Spanish teacher, Nicole Elenz-Martin  and second-grade teacher Jennifer Auten at Montclaire Elementary School in Cupertino (CA) into blends of teacher- and student-centered lessons. Urban Rocketship schools in Silicon Valley, for example, has students working on online math and literacy software geared to questions  students will face on tests. They sit in cubicles for part of the day followed by chunks of time spent in classrooms where teacher-directed lessons occur.

The middle school math program I observed called Teach To One located in an Oakland (CA) K-8 school has different “modalities” that place it also in the center of the spectrum as well, tilting a bit toward the behaviorist end with its numbered math skills that have to be mastered before a student moves on. Also in Oakland is James Madison Middle School using a rotational version of blended learning for its 6th through eight graders.

I would also include teachers in the two Summit Charter schools I observed (Summit Prep–here–and Summit Rainier–here) who combined project-based teaching, online readings and self-assessments, individual coaching and collaborative work within 90-minute lessons. While Summit schools in which I observed teachers had explicitly committed itself to “project-based learning,” the projects were largely chosen by the teachers who collaborated with one another in making these decisions for all Summit schools; the projects were aligned to the Common Core state standards. While choices were given to students within these projects for presentations, reading materials, and other assignments, major decisions on projects were in teachers’ hands (the nine classes I observed at Summit schools were, for the most part, lodged at the second stage  (see here) of putting project-based learning into practice. This is why I placed these teachers and schools in the center of the continuum.

Such schools mix competency-based, individual lessons for children in large cubicle-filled rooms with teacher-directed lessons, project-based teaching. Like those at the behaviorist end, these programs lodged in the center of the spectrum contain differences among them. Yet they all work within the traditional age-graded school organization.

At the pole opposite the behaviorist end is the student-driven, “whole child” set of arrangements that prize multi-age groupings, high student participation in their learning through working on projects that both student and teacher develop, cultivate cross-disciplinary linkages, and dispense with age-graded arrangements (sometimes called “continuous progress”). Many of these schools claim that they “personalize learning” in their daily work to create graduates who are independent thinkers, can work in any environment, and help to make their communities better places to live. There are less than two-score of these schools nationally.

For example, there is High Tech High in San Diego that centers its instruction around project-based learning (and, from documents and exhibits, seem to be at the fourth stage of PBL). The teacher-led Avalon charter school in St. Paul (MN) relies on PBL implementing it through individualized learning plans. The Mission Hill School in Boston (MA), The Sycamore school in Claremont (CA), the Open Classroom at Lagunitas Elementary in San Geronimo (CA), the Continuous Progress Program at Highlands Elementary in Edina (MN)–all have multi-age groupings, project-based instruction, and focus on the “whole child.” And there are private schools such as The AltSchool, a series of small private schools located in big cities and the Khan Lab School (Mountain View, California) fit here as well. They say that they, too, “personalize learning.” Like the clusters of programs at the other end of the continuum and in the middle, much variation exists among these schools harbored here.

In the few months I observed schools and classrooms in Silicon Valley, I saw instances of technology being integrated into lessons and school programs aimed at achieving larger purposes of public schooling mentioned above. However, I saw no examples among these schools of multi-age groupings, advanced stages of project-based teaching, and other features listed above for this end of the continuum.


These two posts are my first effort to make sense of the uses of technology to “personalize learning” that I have seen over the past few months in Silicon Valley schools and classrooms. Highly recommended to me as instances of teachers and schools that integrate technology seamlessly into their daily work, I found that their uses of technology to teach content and skills through “personalizing Learning” fell into types of programs that I could array along a continuum. All of these schools sought similar ends of producing graduates who could be problem-solvers, find jobs in a competitive labor market and become engaged in their communities.

I would appreciate comments from viewers about this first pass at understanding what I observed in these classrooms and schools. I welcome comments on the clarity (or lack of it), coherence (or lack of it) and helpfulness (or lack of it) of this spectrum in draining the semantic swamp of “personalized learning.”


*None of the 17 teachers I observed taught wholly online so I do not include any examples here. Many of the teachers I watched, however, used a combination of online resources, teacher-directed lessons, and small-groups to “personalize learning” while conveying prescribed knowledge and skills aligned to Common Core standards adopted by the state of California.


Filed under how teachers teach, school reform policies, technology use

Substance Beats Flash: District Superintendents and Minority Achievement Network (S. David Brazer and Robert G. Smith)

A former high school principal, David Brazer is Associate Professor (Teaching) and Faculty Director of Teaching Leadership Programs at Stanford University; former superintendent of the Arlington (VA) public schools, Robert Smith is Associate Professor of Educational Leadership at George Mason University. They co-authored Striving for Equity (2016).



The contemporary education reform climate seems to value flash over substance, grand ideology over hard work, and narrow quantitative impact over steady progress in nurturing environments. Lost in all this noise is the steady effort of school board members, superintendents, principals, teachers, students, and parents striving to make the most out of school-age years, from pre-school through high school graduation. The gross exaggerations of “the schools are failing” or “this will revolutionize education” are exposed by the deliberate, effective approaches to improving student achievement of 13 superintendents who tell their stories in Striving for Equity: District Leadership for Narrowing Opportunity and Achievement Gaps (Harvard Education Press, 2016). They pursued results rather than headlines.

Instead of chasing a “best practices” holy grail, these superintendents worked with their communities—both within and around their inner-ring suburban school districts—over long periods of time, following a series of steps that adhered to their commitment to equity and reflected their practical experiences as education leaders. They began by helping parents, teachers, and board members understand that inequities were embedded in their districts’ student outcomes. Most of them were ahead of their time, recognizing opportunity and achievement gaps long before these terms were widely used. Publicizing the data demonstrating achievement differences between the majority population and students with disabilities, in poverty, speaking languages other than English, or identifying as non-white helped these superintendents rally their communities to their gap-closing agendas.

Many of the tactics superintendents employed were pedestrian in nature—funding pre-school, unifying elementary curricula, and keeping their boards educated on the nature of the problem and district progress. Other actions were more complicated, such as providing professional development so that teachers were better equipped to implement curricular changes and reach changing student populations. Additionally, these superintendents also took substantial risks when they focused their professional staffs on understanding unintended bias and institutionalized racism, dismantled entrance requirements for their districts’ most challenging courses, or addressed poverty by taking programs into housing projects or working with health insurance companies to provide coverage for impoverished students. There were no magic bullets or secret sauces, just sensible policies and procedures that focused their agendas to narrow opportunity and achievement gaps for all of their districts’ students.

We identified this group of dedicated superintendents based on their membership on the Governing Board of the Minority Student Achievement Network (MSAN). MSAN, as the name suggests, is committed to success for students who stand outside the majority population on at least one dimension. All of the superintendents valued MSAN for its ability to bring together colleagues and their students from across the country to share ideas and stimulate each other’s thinking about how to address the persistent gaps that dog public education. They attribute many of their good ideas to stimulation that came from MSAN meetings, but no one believed that MSAN provided specific tactics to be implemented. Instead, sharing successes and setbacks and learning from peers, superintendents tended to focus on the principles undergirding shared initiatives rather than trying to replicate particular recipes or procedures. This approach led to innovations adapted and tailored to their specific school districts.

The common experiences of these superintendents highlight central challenges of the job for those who seek to have schools and school systems live up to their equalization potential in US society. The superintendents continually balanced competing demands on resources, shifting politics, and the need to demonstrate progress so that they could remain in their posts long enough to see the effects of their carefully crafted changes and programs. Not all of them stayed more than four or five years, but those who did were able to demonstrate important transformations in their school districts that were making a difference for students long marginalized in their systems. Their examples point to two important factors that are uncommon in public school districts today: 1) longevity in the superintendent role supports long-term improvement, and 2) meaningful reform is multi-faceted, requiring strategy, time, and resources to take hold.

Each of the 13 superintendents pointed with pride to major accomplishments that narrowed achievement gaps in their school districts. None, however, could claim to have closed any of the gaps they identified. Any educator who has tried at any level to help a child succeed will understand that moving the needle on achievement is a thorny, baffling process with many stumbling blocks. Long after the fad or reform du jour has passed from the scene, superintendents, teachers, principals, and other educators dedicated to equitable student outcomes will be chipping away at the gaps in their schools and districts, eventually eliminating minority status as a predictor of student achievement. These superintendents lead the way to that more promising future.


Filed under school leaders