Category Archives: how teachers teach

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.

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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. What also occurs as a consequence of exaggerated propaganda for a technology is not only a critique of existing system but a further erosion of public trust in that institution. The two go together: hype for a new technology will solve problem in system; that problem underscores serious limitations in existing institution; public disappointment and faith in system diminishes.

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.

 

 

 

 

 

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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.

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

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How To Do Adaptive Learning Right (Keith Devlin and Randy Weiner)

Keith Devlin is (@profkeithdevlin) Co-founder & Chief Scientist at BrainQuake and a mathematician in the Stanford University Graduate School of Education. Randy Weiner (@randybw15) is Co-founder & CEO at BrainQuake, a former teacher, & Co-founder and former Chair of the Board at Urban Montessori Charter School in Oakland, CA.

This opinion piece appeared in EdSurge, June 30, 2016.

As one variant of the saying goes, if your strength is using a hammer, everything can look like a nail. Examples abound in attempts to use new technologies to enhance (if not “transform”, or even “disrupt”) education. Technologists who have built successful systems in other domains—and who frequently view education as just another market in which to apply their expertise—often doom their project to fail at the start, by adopting a narrow and outdated educational model.

Namely, they see education as the provision of facts, techniques, and procedures to be delivered and explained by instruction and then practiced to mastery. Their role, then, is to bring their technological prowess to bear to make this process more efficient. In most cases they can indeed achieve this. But optimizing a flawed model of education is not in the best interests of our students, and from a learning outcomes perspective may make things worse than they already are.

In the case of adaptive learning, education commentator Audrey Watters gave examples of how things can go badly wrong on her blog. “Serendipity and curiosity are such important elements in learning,” she asks. “Why would we engineer those out of our systems and schools?” More recently, Alfie Kohn provided another summary of the numerous reasons to be skeptical of education technology solutions.

Watters’ bleak future will only come to pass if the algorithms continue to be both naïvely developed and naïvely applied, and moreover, in the case of mathematics learning (the area we both work in) applied to the wrong kind of learning tasks. Almost all the personalized math learning software systems we have seen fall into this category. But there is another way—as our work, and a thorough review by a third-party research organization—has shown.

We both work in the edtech industry and have a background in education. One of us is a university mathematician who spent several years on the US Mathematical Sciences Education Board and is now based in Stanford University’s Graduate School of Education, the other an edtech veteran who is a former teacher and who co-founded Urban Montessori Charter School.

We are both very familiar with the common “production line” model of education, and recognize that it not only appeals to many (perhaps most) technologists, but in fact is a system that they themselves did well in. But collectively, the two of us have many years of experience that indicates just how badly that approach works for the vast majority of students.

Last year, with funding from the Department of Education’s Institute for Educational Sciences, our company, BrainQuake, spent six months designing, testing and developing an adaptive engine to supply players of our launch product, Wuzzit Trouble, with challenges matched to their current ability level. We were delighted when classroom studies conducted by WestEd showed that the adaptive engine worked as intended (i.e., kept students in their zone of proximal development), straight out of the gate.

We developed the game based on a number of key insights accumulated over many years of research by mathematics education professionals that should be applicable to all edtech developers—even those who are not building math tools.

Experience Over Knowledge

First, the most effective way to view K-8 education is not in terms of “content” to be covered, acquired, mastered (and regurgitated in an exam) but as an experience. This is particularly (but not exclusively) true for K-8 mathematics learning. Mathematics is primarily something you do, not something you know.

To be sure, there is quite a lot to know in mathematics—there are facts, rules, and established procedures. Imagine the skills expected of a physician. None of us, we are sure, would want to be treated by someone who had read all the medical textbooks and passed the written tests but had no experience diagnosing and treating patients. And indeed, no medical school teaches future physicians solely by instruction, as any doctor who has gone through the mandatory, long, grueling internship can attest.

In the case of math, the inappropriateness of the classical, instruction-practice-testing dominated model of education has been made particularly acute as a result of the significant advances made in the very technology field we are working in. (Advances we wholeheartedly applaud. Our beef is not with technology—we love algorithms, after all—but with applying it poorly.) In today’s world, all of us carry around in our pockets a device that can execute almost any mathematical procedure, much faster and with greater accuracy than any human. Your smartphone, with its access to the cloud (in particular, Wolfram Alpha), can solve pretty well any university mathematics exam question.

What that device cannot do, however, is take a real world mathematical problem and solve it. To do that, you need the human brain. In order to do that, the human brain has to acquire two things, in particular: a rich and powerful set of general metacognitive problem solving skills, and a more specific ability known as mathematical thinking (a component of which is known as number sense, a term that crops up a lot in the K-8 math education world, since the development of number sense is the first key step toward mathematical thinking).

Human Adaptivity

Another key insight that guided the design of our adaptive engine is that the main adaptivity is provided by the user. After all, the human being is the most adaptive cognitive system on the planet! With good product design, it is possible to leverage that adaptivity.

Most “adaptive” math algorithms will monitor a student’s progress to select the next problem algorithmically. But it is important that these puzzles allow for a wide range of of solutions and a spectrum of “right answers,” leaving the student or teacher in full control of how to move forward and what degree of success to accept. (Of course, such an approach is not possible if the digital learning experiences are of the traditional math problem type, where the problem focuses on one particular formula or method and there is a single answer, with “right” or “wrong” the only possible outcomes.)

Indeed, students still need to grasp the basic concepts of arithmetic, understand what the various rules mean, and know when and how the different procedures can be applied. But what they do not need is to be able to execute the various procedures efficiently in a paper-and-pencil fashion on real world data.

Today’s mathematical learning apps can—and should—focus on the valuable 21st century skills of holistic thinking and creative problem solving. The mastery of specific procedures should be skills that a student acquires automatically, “along the way,” in a meaningful context of working on a complex performance task—an outcome every one of us knows works from our own experience as adults.

Breaking the Symbol Barrier

Mastery of symbolic mathematics is a major goal of math education. But as has been shown by a great deal of research stretching back a quarter of a century, the symbolic representation is the most significant reason why most people have difficulty mastering K-8 grade level math—the all-important “basics.” Almost everyone can achieve a 98 percent success rate at K-8 math if it is presented in a natural-seeming fashion (for example, understanding and perhaps calculating stats at a baseball game), but their performance drops to a low 37 percent if presented with the same math problems expressed in textbook symbolic form.

Well-designed technologies that take advantage of some unique affordances of a computer or tablet can help obliterate this historical impediment to K-8 mathematics proficiency. Students should be able to explore problems on their own until they discover—for themselves—the solution. They don’t require instruction, and they don’t need anyone to evaluate their effort. Students should get instant feedback not in the form of “right” or “wrong,” but information about how their hypotheses varied from their actual experience and how they might revise their strategy accordingly.

An analogy we are particularly fond of is with learning to play a piano (or any other musical instrument). You may benefit greatly from a book, a human teacher, or even YouTube videos, but the bulk of the learning comes from sitting down at the keyboard and attempting to play.

What could be a better example of adaptive learning than that? Tune too easy? Try a harder piece. Too difficult? Back off and practice a bit more with easier ones, or break the harder one up into sections and master each one on its own at a slower pace, and then string them all together. The piano is not adapting. Rather, its design as an instrument makes it ideal for the learner to adapt.

A well-designed math tool should be an instrument on which you can learn mathematics, free from the Symbol Barrier. Now imagine we present a student with an orchestra of instruments.

We think this kind of approach is the future of adaptive learning in math and believe we, the edtech community, should choose to go beyond the “low hanging fruit” approaches to adaptive learning that the first movers adopted.

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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.

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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.”

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*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.

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Draining The Semantic Swamp of “Personalized Learning”–A View from Silicon Valley (Part 1)

No surprise that a catch-phrase like “personalized learning,” using technology to upend traditional whole group  lessons, has birthed a gaggle of different meanings. Is it  updated “competency-based learning?” Or “differentiated learning” in new clothes or “individualized learning” redecorated?  (see here, here and here). Such proliferation of school reforms into slogans is as familiar as photos of sunsets. “Blended learning,” “project-based teaching,” and “21st Century skills” are a few recent bumper stickers–how about “flipped classrooms?”– that have generated many meanings as they get converted by policymakers, marketeers, researchers, wannabe reformers, and, yes, teachers into daily lessons.

For decades, I have seen such phrases become semantic swamps where educational progressives and conservatives argue for their version of the “true” meaning of the words. As a researcher trained in history, since the early 1980s, I have tracked policies as they get put into practice in schools and classrooms.  After all, the first step in science is to observe systematically the phenomenon or as Yogi Berra put it: “You can observe a lot by watching.” The second step is to describe and tell others what was seen and explain it.

Over the past few months, I have visited eight schools and 17 teachers in “Silicon Valley,” that near-mythical stretch of the Bay area in Northern California encompassing San Jose, San Francisco, and Oakland and their environs. I went into schools and classrooms that administrators, policymakers, researchers, and others identified for me as “best cases,” or exemplars of integrating use of technology into daily lessons. Many, but not all, told me that they had integrated technology into their lessons to “personalize learning.”

The questions I asked myself while observing a class was simply: What are teachers and students doing when computer use is integrated into a lesson? Toward what ends is such use aimed?

Teachers and principals invited me to observe.  There were no tours or group visits. I went to each school and talked with principals, various teachers, and read online documents describing the school. I sat in 90-minute lessons, listened to students in and out of class–even shadowing a student at one school for a morning–and took copious notes.  I sent drafts of my classroom observations to teachers to correct any errors in facts that I made. Then I published accounts of my observations  in my blog in March, April, and May 2016.  Although I am far from finished in this project, now is the time for that second step (see above). I need to make sense of  what I saw at the epicenter of technological optimism. So this is an initial pass at figuring out what I saw as I sloshed through the semantic swamp of  “personalized learning.”

The “Personalized Learning” spectrum

When I visited the schools, administrators and most teachers told me that they were “personalizing learning.” What I saw, however, in classrooms and schools was a continuum of different approaches–which I call the “personalized learning spectrum”–that encompassed distinct ways of implementing technology in lessons to reach larger purposes for schooling. Let me be clear, I value no end of the spectrum (or the middle) 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.

At one end of the continuum are teacher-centered lessons and programs within the traditional age-graded school using behavioral approaches that seek efficient and effective learning to make children into knowledgeable, skilled, and independent adults who can successfully enter the labor market, thrive, and become adults who help their communities. These approaches (and ultimate aims for public schools) have clear historical underpinnings dating back nearly a century.

At the other end of the continuum are student-centered lessons and programs that seek student agency and shape how children grow cognitively, psychologically, emotionally, and physically. They avoid the traditional age-graded arrangements that they believe have deadened learning for over a century. Their overall goals of schooling are to convert children into adults who are creative thinkers, help out in their communities, enter jobs and succeed in careers, and become thoughtful, mindful adults. Like the other end of the spectrum, these approaches have a century-old history as well.

And, of course, on this spectrum hugging the middle are hybrids mixing behavioral and cognitive approaches aimed at turning children into adults who engage in their communities, are creative, thoughtful individuals who succeed in the workplace.

Such a spectrum has been around for many decades with different names such as “Progressive-to-Traditional,” “Teacher-centered to Student-centered, etc. A glance at the rear-view mirror about the history of these continua helps me make sense of what I saw in my observations..

Looking back a century

What today’s reformers promoting “personalized learning” have to remember are their yesteryear cousins among Progressive reformers a century ago. Then, these reformers wanted public schools to turn children and youth into thoughtful, civically engaged, whole adults. Those early Progressives drank deeply from the well of John Dewey but ended up following the ideas of fellow Progressive Edward Thorndike, an early behaviorist psychologist and expert in testing.*

If one wing of these early progressives were pedagogical pioneers advocating project-based learning, student-centered activities, and connections to the world outside of the classroom, another wing of the same movement were efficiency-minded, “administrative progressives,” who admired the then corporate leaders of large organizations committed to both efficiency and effectiveness–Standard Oil, U.S. Steel, General Motors. Thorndike at Columbia University’s Teachers College, Ellwood P. Cubberley at Stanford and other academics, in alliance with the new field of educational psychology, borrowed heavily from business leaders. They counted and measured everything in schools and classrooms under the flag of “scientific management.” They reduced complex skills and knowledge to small chunks that students could learn and practice. They wanted to make teachers efficient in delivering lessons to 40-plus students with the newest technologies of the time: testing, film, radio. They created checklists for teachers to follow in getting students to learn and behave. They created checklists for principals to evaluate teachers and checklists for superintendents to gauge district performance including where every penny was spent.

A century ago, this efficiency-minded, behaviorist wing of the progressive movement overwhelmed the pedagogical progressives passionate about students developing and using a range of cognitive and social skills. Thorndike trumped Dewey.

Now in 2016 behaviorists and believers in the “whole child” wear the clothes of school reformers and educational entrepreneurs. They tout scientific studies showing lessons tailored for individual students produce higher test scores than before, or that project-based learning creates independent, creative, and smart students.

What exists now is a re-emergence of the efficiency-minded “administrative progressives” from a century ago who now, as entrepreneurs and practical reformers want public schools to be more market-like where supply and demand reign, and more realistic in preparing students for a competitive job market. Opposed are those who see schools as places to create whole, knowledgeable human beings capable of entering and succeeding in a world far different than their parents faced. The struggle today is between re-emergent, century-old wings of educational progressives. It is, then, again a family fight.

Part 2 will place some of the classroom lessons and schools I observed and have documented elsewhere along that continuum.

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*A current dust-up between Progressives and Conservatives over school reform (see Rick Hess’s summary of back-and-forth bloggers here) misses entirely the intra-reformer struggle among Progressives a century ago and how the conservative, efficiency-driven wing (e.g., Edward Thorndike, et. al.) of those early Progressives triumphed over the liberal, student-centered, reconstructionist wing (e.g., John Dewey, George Counts, et. al.) who sought to make  schools student-centered and agents of societal reform. David Tyack tracked this split fully in The One Best System and with co-author Elisabeth Hansot in Managers of Virtue. The split that Hess and others see today is hardly new. It is a resurgence of that old struggle among Progressives but now reincarnated in an age of standards, testing, and accountability. The  split among current school reformers is over  both equity and efficiency with one wing labeled “Progressives” and the other “Conservatives.” Current “Progressives,” imbued with social justice, want schools to be agents of social and political change and student-centered. They use both behaviorist and cognitive approaches to “personalize learning.”  “Conservatives” want contemporary reform policies (e.g., charters, standards and accountability) to be sustained because they advance equity and blend technology to create both student- and teacher-centered experiences. They, too, want learning to be “personalized” and create  both behaviorist- and cognitive-driven lessons.  Such clashes  track earlier differences among reformers a century ago. The conflict today, as then, tries to answer the age-old question: Is the job of public schools in a democratic and capitalist-driven society to solve larger economic, social, and political problems that the nation faces or focus on building whole human beings who can thrive and succeed in a highly competitive society?

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This Ed-reform Trend Is Supposed To Motivate Students; Instead, It Shames Them (Launa Hall)

Nearly all reform policies have consequences, intended and unintended, regardless of how well-meaning, empathic, and mindful policymakers may be. The following essay of a former elementary school teacher illustrates the unintended consequence of a familiar reform-driven policy. A former teacher, Launa Hall lives in Northern Virginia and is working on a book of essays about teaching. This essay appeared in the Washington Post, May 19, 2016 

My third-graders tumbled into the classroom, and one child I’d especially been watching for — I need to protect her privacy, so I’ll call her Janie — immediately noticed the two poster-size charts I’d hung low on the wall. Still wearing her jacket, she let her backpack drop to the floor and raised one finger to touch her name on the math achievement chart. Slowly, she traced the row of dots representing her scores for each state standard on the latest practice test. Red, red, yellow, red, green, red, red. Janie is a child capable of much drama, but that morning she just lowered her gaze to the floor and shuffled to her chair.

In our test-mired public schools, those charts are known as data walls, and before I caved in and made some for my Northern Virginia classroom last spring, they’d been proliferating in schools across the country — an outgrowth of “data-driven instruction” and the scramble for test scores at all costs. Making data public, say advocates such as Boston Plan for Excellence, instills a “healthy competitive culture.” But that’s not what I saw in my classroom.

The data walls concept originated with University of Chicago education researcher David Kerbow, who in the late 1990s promoted visual displays to chart students’ progress in reading. Kerbow called these displays “assessment walls,” and he meant them to be for faculty eyes only, as tools for discussion and planning. But when that fundamentally sound idea met constant anxiety over test scores in K-12 schools across the United States, data walls leaked out of staff-room doors and down the halls. Today, a quick search on Pinterest yields hundreds of versions of children’s test scores hung in public view.

Diving Into Data,” a 2014 paper published jointly by the nonprofit Jobs for the Future and the U.S. Education Department, offers step-by-step instructions for data walls that “encourage student engagement” and “ensure students know the classroom or school improvement goals and provide a path for students to reach those goals.” The assumption is that students will want to take that path — that seeing their scores in relationship to others’ will motivate them to new heights of academic achievement. They are meant to think: “Oh, the green dots show my hard work, yellow means I have more work to do, and red means wow, I really need to buckle down. Now I will pay attention in class and ask questions! I have a plan!”

How efficient it would be if simply publishing our weaknesses galvanized us to learn exactly what we’re lacking.

That late night when I got out my markers and drew the charts, I rationalized that it was time to drop all pretenses. Our ostensible goal in third grade was similar to what you’d hear in elementary schools everywhere: to educate the whole child, introduce them to a love of learning and help them discover their potential. We meant that wholeheartedly. But the hidden agenda was always prepping kids for the state’s tests. For third-graders, Virginia has settled on 12 achievement standards in reading and 20 in math, each divided further into subsections. And once blossoms were on the trees, we were just a few weeks from the exams that would mark us as passing school or a failing one. We were either analyzing practice tests, taking a test or prepping for the next test. Among the teachers, we never stopped talking about scores, and at a certain point it felt disingenuous not to tell the kids what was really going on.

I regretted those data walls immediately. Even an adult faced with a row of red dots after her name for all her peers to see would have to dig deep into her hard-won sense of self to put into context what those red dots meant in her life and what she would do about them. An 8-year-old just feels shame.

Psychologists Todd Kashdan and Robert Biswas-Diener point out in their book “The Upside of Your Dark Side ” that while some uncomfortable feelings can be useful, shame is not productive. Guilt, they say, can encourage people to learn from their mistakes and to do better. In contrast, “people who feel shame suffer. Shamed people dislike themselves and want to change, hide, or get rid of their self. ”

It also turns out that posting students’ names on data walls without parental consent may violate privacy laws. At the time, neither I nor my colleagues at the school knew that, and judging from the pictures on Pinterest, we were hardly alone. The Education Department encourages teachers to swap out names for numbers or some other code. And sure, that would be more palatable and consistent with the letter, if not the intent, of the Family Educational Rights and Privacy Act. But it would be every bit as dispiriting. My third-graders would have figured out in 30 seconds who was who, coded or not.

The data walls made it harder for me to reach and teach my students, driving a wedge into relationships I’d worked hard to establish. I knew Janie to be an extremely bright child — with lots of stresses in her life. She and I had been working as a team in small group sessions and in extra practice after school. But the morning I hung the data walls, she became Child X with lots of red dots, and I became Teacher X with a chart.

Of course, I tried to mitigate the shame she felt. I let her loudly sing a song she made up, and I made time for one of our conversations on the playground. Did my efforts at reconnection help? Maybe a little. But she still had all those red dots for everyone to see.

It’s hard to find research that supports public data walls. In fact, studies suggest that rather than motivating students, they may be detrimental. “Evaluation systems that emphasize social comparison tend to lower children’s perceptions of their competence when they don’t compare favorably and cause them to engage in many self-defeating cognitions and experience considerable negative affect,” according to Carole Ames, a leading scholar of social and academic motivation and a professor emeritus at Michigan State University.

In an article published in March in the journal Educational Policy, Julie Marsh, Caitlin Farrell and Melanie Bertrand warn against data walls and similar practices that stress competition and achievement rather than meaningful learning. They note that federal education policy has “long emphasized status measures of student achievement (i.e., proficiency) and assumed that public reporting of information on performance, coupled with consequences, will motivate individuals to work harder and differently to improve performance.” Now, they observe, that focus on achievement and mistaken assumptions about motivation have trickled down to the classroom. Their study of six middle schools found that “many well-intentioned teachers . . . appeared to be using data with students in ways that theoretically may have diminished the motivation they initially sought to enhance.”

And consider exactly who is being shamed by data walls. Janie is part of an ethnic minority group. She received free breakfast and lunch every school day last year, and some days that’s all she ate. Her family had no fixed address for much of the year, and Janie, age 8, frequently found herself the responsible caretaker of younger siblings. That’s who is being shamed.

And do you see those neat rows of green dots on the chart? If you haven’t already guessed, they belong to children whose families have the resources for new shoes and fresh fruit and a little left over for a modest vacation from time to time, children whose parents attend teacher conferences with their forms not only signed but stapled to a list of questions about how to help with homework.

When policymakers mandate tests and buy endlessly looping practice exams to go with them, their image of education is from 30,000 feet. They see populations and sweeping strategies. From up there, it seems reasonable enough to write a list of 32 discrete standards and mandate that every 8-year-old in the state meet them. How else will we know for sure that teaching and learning are happening down there?

But if we zoom in, we see that education actually happens every weekday, amid pencils and notebooks, between an adult and a small group of youngsters she personally knows and is deeply motivated to teach. Public education has always been — and needs to be still — a patchwork of ordinary human relationships. Data walls, and the high-stakes tests that engender them, aren’t merely ineffective, they break the system at its most fundamental level. They break the connection between a teacher who cares and a kid who really needs her to care.

Teaching the young wasn’t supposed to feel like this. When we imagine the ideal elementary school, we see walls covered with things the kids made. We see kids clustered around tadpoles and taking notes in crooked, exuberant handwriting. We hear “Oh, wow!” from teachers and children alike, and the murmur of many voices talking it over and figuring it out. We smell grass stains on sweaty little kids because they just ran in from a long romp outdoors. And why shouldn’t our elementary schools be what we wish for? The whole ideal of school, this particular means to pass our accumulated human knowledge from one generation to the next, is a construct we made up. Why not make it wonderful? Why not make it work?

We are failing our kids. The writing is on the wall.

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Confessions of a Luddite professor (Daniel Drezner)

Daniel W. Drezner is a professor of international politics at the Fletcher School of Law and Diplomacy at Tufts University. This post appeared in the Washington Post on April 28, 2016.

 

I had the good fortune on Wednesday to hear economist Robert Gordon talk about his magnum opus, “The Rise and Fall of American Economic Growth.” Gordon has a somber tale to tell. He argues that U.S. economic growth ain’t what it used to be, and that ain’t gonna change over the next 25 years. This is due to myriad headwinds such as demographic slowdowns, rising inequality, fiscal constraints, and — most important — the failure of newer technologies to jumpstart economic growth the way that the Second Industrial Revolution did.

It’s his last point — about the effect of information technology on productivity — that prompts so much fierce debate.  Economists are furiously debating whether the visible innovations in the information sector are leading to productivity advances that are going undetected in the current productivity statistics. On the one hand, the aggregate data suggests a serious productivity slowdown over the past decade. On the other hand, Google’s chief economist, Hal Varian, insists that “there is a lack of appreciation for what’s happening in Silicon Valley, because we don’t have a good way to measure it.”

Surely, there are sectors, such as higher education, in which technological innovations can yield significant productivity gains, right? All that talk about MOOCs and flipped classrooms and the like will make a difference in productivity, yes?

As an optimist, I’ve long resisted Gordon’s argument — but this is one area where I’m beginning to suspect that he’s right and Silicon Valley is wrong.

I’ve been teaching for close to 20 years now. During that time, the IT revolution has fundamentally transformed what I do on a day-to-day basis. It is massively easier for me to access data that helps inform my classes. The ability to use audio-visual methods to broadcast a video or audio to my students is much easier. I’ve Skyped in as a guest lecturer for numerous other colleagues. Course websites have made it far easier for me to communicate with my students, and for them to communicate with me. There is no denying that on some dimensions, technological change has made it much easier for me to do my day job.

And yet, over the past decade, I have also gone in a more Luddite direction. After having a laissez-faire policy on laptops in my classrooms for my first decade of teaching, I have pretty much banned them. I knew that taking notes by hand is much, much better for learning than taking notes on a computer (the latter allows the student to transcribe without thinking; the former forces the student to cognitively process what is worthy of note-taking and what is not), but I figured that was the student’s choice. The tipping point for me was research showing that open screens in a classroom distract students close to the screen. So I went all paternalistic and decided to eliminate them from my classroom. The effect was immediate — my students were more engaged with the material.

My lectures are pretty low-tech as well. I use videos in class on occasion, but I usually deploy them at the start and then start lecturing. Otherwise lights have to be dimmed and that’s an invitation for the student to tune out. Similarly, I don’t use Powerpoint for my notes — because that just invites the student to transcribe the points in the slide without thinking about them.

One could argue that Skyping in as a guest lecturer, or just broadcasting a superstar professor into other universities, could improve the quality of the classroom experience. But I doubt it. Speaking from experience, lecturing remotely is a radically imperfect substitute for interacting in the same physical space. A mediocre but in-the-flesh-professor still provides a superior education environment than a remote lecturer that one watches on a screen.

There has been one innovation over the past generation that has made my in-class teaching better. The whiteboard is way better than the blackboard to use. Otherwise, I have become warier of new technologies in the classroom.

Maybe this is just me being a Luddite, and, as digital natives, millennial professors will figure out how to properly exploit information technologies in the classroom. And outside of the classroom, I’m a pretty big fan of these new technologies.

But when it comes to higher education, I think Gordon is right and Varian is wrong. There are gains to be wrung from technological innovation — but they’re much more limited than Silicon Valley wants you to believe.

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