A Different Story for Mathematics

From our parents and teachers, we learn that, when performing a mathematical or arithmetical task, the result is a right answer or a wrong answer to a question posed by an adult. We learn that our answers result from a process and that there is only one process we can use to produce them. We learn that every question has an answer. We are pleased when our work produces a right answer and pleases the adult who asked the question and we are distressed when our work produces a wrong answer and displeases that adult. We conclude as young children that Arithmetic is rigorous, deductive, computational and useful. As our parents, teachers and peers reinforce this conclusion throughout the first twelve years of school, we internalize and preserve it. It becomes bedrock to our view of the world and the places of things, events, and actions in it.

Neither History, Fiction, Poetry, Music, Physics, Chemistry, Biology nor, well, any subject is presented exclusively in this way. Sure, each field has its jargon to learn, its principles to apply and its recipes to follow. But, we learn in every field other than Math that authors embed their stories in context and tell them in several ways, teachers employ many devices and draw from many resources in their pedagogy. They tell the stories of their subjects as fields of inquiry that have evolved with civilization and like civilizations. They battle about the course of history, the nature and limits of fiction, music or poetry, or the proper use of its elements (character, plot, narrative voice, rhyme, meter, tone, key, scale, theme) the fundamental laws of matter and energy, the material composition of things or the building blocks of living things. Our teachers and parents demonstrate and encourage experimentation and discovery in every area—except Arithmetic and Mathematics.

During our lives, some of us find by chance a few corners of Arithmetic that are fun: Rubik’s Cube, Sudoku, magic squares, and ciphers among them. Others among us discover that Mathematics is quite different from Arithmetic and attempt to cross the divide between them. But, crossing this divide is arduous; everyone who has attempted this crossing knows this. Many of us decide that it’s too arduous, reverse course, and return to the safety and certainty of the Arithmetic and Mathematics we know, to the Island of Conclusions. The remaining few travelers complete the crossing and seek roles for themselves in this new land. What do they find there? Why is this crossing so arduous?

They find a vast network of communities of people, each of whom inhabits some area in an open, limitless landscape that we can characterize as an evolving field of inquiry (a field of evolving inquiry, perhaps). Some communities inhabit developed areas; some inhabit territories on the frontiers of Mathematics and others inhabit undeveloped territories beyond such frontiers. Each generation of inhabitants, called Mathematicians, explores, settles and develops land beyond its frontiers and discovers new relationships, new networks, within the developed territories. This story is the story of Mathematics; Arithmetic and school Maths are chapters in it.

We make the crossing to Mathematics, the field of inquiry, from Arithmetic, the body of knowledge, arduous by failing to prepare our students for it. If they need to cross an ocean, they must build the boats, learn to sail them and learn to swim. If they need to cross a desert, they must build the wagons or find and train the camels, and learn to navigate by the night sky, the position of the Sun, or by compass. But, we the communities of educators and parents, have failed to provide our students and children the boats, wagons, camels, or compasses or to show them how to build, train or use them. We’ve given them boats; they need ships. We’ve taught them how to use a calculator for Arithmetic and to replicate algorithms for Algebra, Geometry, Trigonometry and Calculus to find unique answers to manufactured problems. But, that is not enough. They need to learn how to recognize patterns and describe them with precision. They need to learn how to experiment with mathematical objects to discover patterns in their behavior. They need to learn to associate freely among concepts that appear similar or disparate, seeking connections they haven’t discovered, yet. For, although Mathematics is rigorous, deductive, computational and useful, it is, like any other subject, experimental, inductive and inferential. By framing it as the former alone, we hide the latter aspect of its nature.

Nearly all students learn to calculate, deduce and apply arithmetical and mathematical tools readily enough to get them through the days of their lives. But, the mathematics required for daily living is even more elementary than the reading required for it. Barcodes and computers total our groceries purchase and we use a plastic card to pay; no computation is required. Most daily math tasks are counting tasks. Calculators, hand-held or online, can calculate mortgage payments and their elements and other tasks that require more than counting. Students learn enough math for their daily living before they complete the seventh year of school. Demonstrations of and practice at applications of Algebra, Geometry, Trigonometry, Matrices, Vectors or Calculus convince our students that these tools are useful, indeed. But, such demonstrations and practice don’t convince them that they will ever use those tools. Mathematics’ usefulness in the world fails to motivate students to learn it after they’ve completed the sixth year. For the first three or four years, they needed only the approval of adults to motivate learning it; its usefulness didn’t matter to them. Once they’ve learned enough of the math they need to use daily, they lose interest in learning any further uses. Teaching them, in subsequent years, for example, how to use techniques in solving systems of linear equations in linear programming applications does not suffice to impel them to learn either how to solve systems of linear equations or how to apply those techniques in the world. The answer, “Because it’s useful,” to the question, “why do we have to learn this stuff?” is true, but it fails to stimulate students’ interest and impel them to learn such techniques and their applications. Only a few students will use them, and those students are unaware of their futures and don’t know, yet, that they will. WYSIATI (What You See I What There Is) dominates their perceptions and their conceptions.

Mathematics is a body of knowledge, a compendium of results (truths) developed over at least seven millennia. Its method of verification is most rigorous of the methods of the sciences and it’s facts live longest of them. Logical deduction plays a unique role in its practice. Yet, Mathematics is more than a static body of knowledge. It’s an open, evolving field of inquiry into the nature of numbers and their relationship to the world. Its practitioners, Mathematicians, pursue answers to questions that arise in their endeavor to locate and understand the fundamental elements of the mathematical universe and explain their existence and behavior because such questions interest and challenge them and because they feel themselves continue to grow and learn, which they like to do. A Mathematician enjoys feelings similar to the feeling an author feels when he’s written a satisfying paragraph or an effective stanza or to a musician when he’s executed a passage “perfectly” or his performance jells with an ensemble.

Importance and applicability are reasons necessary to the pursuit of mathematical knowledge, and they are reasons necessary to the pursuit of any and all other knowledge. But, they are insufficient. In this regard, too, Mathematics is like all other fields of inquiry. We pursue our inquiries into them because we enjoy the process, and our success in discovering and answering questions amplifies this enjoyment a thousand times. The pursuit of mathematical knowledge is interesting, gratifying and creative. These factors impel practitioners to practice. These factors will impel students to learn to practice it, too.

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Three Myths of Mathematics and Mathematics Education

To understand current Math teaching practice, IWY (I-do, We-do, You-do), we need to recognize at least three myths about Mathematics and Mathematics education—stories about what it is, whether and how it differs from other fields of inquiry, why we need to learn it, and how we learn it:

  • Myth 1: Mathematics is unique among all fields of inquiry in its structure and practice. Mathematics is deductive and rigorous—and nothing else;
  • Myth 2: Logical rigor and deductive method—and nothing else—must frame Mathematics pedagogy;
  • Myth 3: Mathematics is a tool for use in the construction of other things such as buildings, rockets, theories and submarines—and is nothing more.

Math is different, right? There’s always a unique, right answer to every problem. Math is rigorous: you can always verify whether your answer is correct or incorrect with absolute certainty. When you read a topic section in a Math textbook or an academic paper, you encounter definitions, first, then theorems and their proofs. The theorems use the definitions to assert some claims about the world. These theorems follow by strict rules of formal logic from other theorems that were proven previously. A machine could, in principle, prove the theorems in your textbook. New research results are always published in essays in academic journals and rarely included (as new results) in textbooks. Although there is little or no exposition in an academic paper, there is almost always some exposition in a textbook, usually pictures, diagrams or graphs of a situation, or examples of algorithms, or all of them. The pictures, diagrams or graphs are heuristic devices; they motivate and depict the concept you’re supposed to learn to help you retain them for use. The examples are heuristics, too. They show you how to use the algorithms you learned to solve problems posed in the book, that is, how to do the homework problems. By telling the story of Mathematics only in this way in books, classrooms, and lecture halls, textbook authors, publishers, professors, and teachers present an incomplete picture of it. They create the myths that: Mathematics consists of definitions, theorems, proofs, and problems; theorems are deduced rigorously from other theorems by strict, formal rules of logic; and these theorems are deployed as algorithms to solve problems—and nothing else.

This story of Mathematics became the dominant narrative of Mathematics as the result of cognitive processes developed to promote our survival as a species over millions of years of evolution by trial and error. WYSIATI combines with representation, cognitive ease, and repetition to embed this myth in our memories and enable its instant, subconscious recall. WYSIATI is an acronym for What You See is All There Is. It is a slogan for the fact that the associative machine—our minds operating in “current awareness only” mode (which is 99% of the time)—dominates our awareness at any and all given moments; we are “hardwired” by evolution to jump to conclusions based on limited information. If the only story about Mathematics that you’ve been told is that it is rigorous, deductive, and computational, you will jump to the conclusion that it is only rigorous, deductive, and computational. This jump occurs early in school, in the first grade when we learn to add and before we’ve learned to reflect on and filter our immediate experience. Jumping to the Island of Conclusions is easy. But, you can’t jump back and there are no bridges or boats; you must swim back to the mainland, which is hard, so you are unlikely to try. Soon, we develop a stereotype of Mathematics that we use to represent it in all of our thoughts and emotions about it. This stereotype is reinforced by the repetition of IWY pedagogy over the first twelve years of school. It becomes the easy representation that we invoke subconsciously whenever we think about Mathematics or perform Mathematical tasks. By the end of first grade, we use this stereotype to represent Math throughout the school years and for the remainder of our lives, unless and until we learn another story and stereotype with which to replace it. Go on, try it! Try to think of Mathematics in some other way. How did you do?

The myth that Mathematics consists only of definitions, theorems, proofs, and problems (applications) frames our pedagogy and thereby limits our practice to stocking our student’s inventory of facts and algorithms to use them. We employ IWY as the default pedagogy because we believe this myth. We learned this convenient, easy representation of Mathematics from our professors, teachers, and textbooks, and we use it in our classrooms and lecture halls. It “worked” for us; it should work for everyone, shouldn’t it? We want our children, our students, to succeed, don’t we? After all, success is good; it reinforces what they’ve learned. Practice makes perfect. Homework is the opportunity to practice and, thereby, perfect and make permanent what they’ve learned. Solve the problems, achieve success, feel good about what you’ve done and who you are. Of course, we use heuristics to motivate topics. Thus, we use the rectangle to illustrate the commutative property: A rectangle’s area is the product of its length and width; the order of multiplication doesn’t matter (32 = 8 x 4 = 4 x 8). Or, we use a pair of scissors to illustrate the Hinge Theorem: the length of the side of a triangle is proportional to the measure of the angle opposite it; in a triangle, the side opposite a 60º angle is longer than the side opposite a 30º angle (√3 times longer). The hinge of the scissors represents the vertex of the relevant angle and the opening between the tips of the blades represents the side opposite the angle. As you open the scissors, the angle between the blades increases and the distance between their tips increases; as you close them, the distance between the tips shrinks as the angle between the blades decreases. We intend such heuristics to point to the path to understanding and retention, but they don’t.

Perhaps the most surprising and wonderful fact about Mathematics is its fruitful application to nearly every aspect of the world we inhabit. Its applicability is integral to the prevailing story of Mathematics. Applicability would,  of course, be integral to any story of Mathematics. But, its role in this story is to play the sole reason to learn and do Mathematics. We learn Mathematics because it’s useful. Its value lies in its instrumentality. In at least one widely used high school textbook, at the beginning of each section there is a short list: what the section is about, what the student is going to learn how to do, and why the student should learn it. The third item in this list is, invariably, “It’s useful.” And, naturally, the section includes examples of its use in the world. Thus, we learn algorithms to perform such counting tasks as totaling the cost of a basket of groceries or the addition to your house or the size of your farm, or to perform such engineering tasks as determining the thrust required for a rocket of a given size to achieve escape velocity. We also learn algorithms to help us tell which algorithm(s) to apply to which circumstances. We call it “cookbook math” or “engineering math”. Our students—our children—do not meet “real” Mathematics until they’ve completed a twelve-to-fourteen-year apprenticeship that fails utterly to prepare them for this meeting.

So, Mathematics is, according to these three myths, in fact, and indeed, a body of knowledge consisting of definitions, theorems, their proofs and the algorithms that we use to apply those proofs to solve problems. Its value and, therefore, our motivation to learn it lie in is instrumentality. Together, these three myths comprise the meta-myth of Mathematics.

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Our Brains and Learning: What we’ve learned that has revolutionized our beliefs about the brain and learning

 

School teachers in all types of schools, private tutors, tutoring companies, military instructors, coaches, performance arts teachers, and all other types of teacher/instructor use the teaching method known as “I-do, We-do, You-do” (“IWY”): I show you how to do it; we do it together; you do it without my help, whatever “it” might be. We check the outcome of you doing it without my help. If the outcome is acceptable, we consider ourselves successful—you at learning, me at teaching. Martial arts, performance art, trade-school, factory, business and professional-school instructors employ this method as the prevailing method. Students and parents expect their private tutors to use this method almost exclusively; they expect the tutor to show the student how to apply the theory he or she learned in class that day or week.

IWY is endemic because it’s successful. It has been successful among sentient species since their origination and will continue to be successful. Its hallmark features are a demonstration, guided or supervised practice, and independent practice and application. It is the “tried-and-true” method to teach the rudiments of how to do nearly anything: to hunt an antelope, grow corn, dig a well, assemble a bicycle, dance the samba, play the clarinet, build a chair or frame a house, wire a kitchen, wire a lamp, fly an aircraft, sail a boat, drive a car, assemble a rifle, learn kung fu, or assemble a computer. Through experience—trial and error—we enhance and perfect the techniques we learn through training. Practice makes it permanent; experience, if we survive the trials (think of flying a small aircraft in bad weather, or climbing your first 100-foot rock wall without gear), perfects it. The ancient paradigm of this process is the guru instructing his or her pupil.

The invention of new technologies, of economical methods to produce them, and their subsequent, broad deployment in the physical, biological and social sciences have enabled researchers in the cognitive, brain, language and learning sciences to investigate the links between physical processes and behavioral outcomes more deeply and more broadly than was possible before their invention and deployment. In particular, “MRI”—Magnetic Resonance Imaging; digital signal processing; digital audio and visual recording; Wi-fi and the Internet; the capacity to consume, digest and restructure vast quantities of data; and machine learning capabilities have enabled insightful research into crucial causal connections and correlations between brain processes and their outcomes in human behavior.  Since the year 2000, we’ve learned a lot about the relationship between our minds and our brains.

The “London Black-Cab Driver” study exemplifies this point. In the early 2000s, scientists chose to study three hundred London black-cab drivers for brain changes as the drivers took years of complex spatial training. MRI and related technologies enabled these researchers to measure changes to the brain without killing the subjects or drilling through their skulls. To qualify as a black-cab driver, applicants must learn 25,000 street intersections and 20,000 landmarks. They are tested for their knowledge of the city. Applicants take two to four years to complete the course (and acquire The Knowledge, as it’s called) and fail the test an average of four times. The drivers in this study were (and still are) mature adults, whose brains were believed to have completed their development prior to undertaking the course. Researchers found that at the end of the course, the hippocampus in the drivers’ brains had grown significantly (as much as 20%). Additional, independent studies confirmed this result. This result and its confirmation revolutionized learning science. Before the publication of the first black-cab studies in 2006, scientists believed that we are born with a capacity to learn that was fixed at birth to reach a genetically determined maximum. This capacity, they believed, is distributed among individuals according to the normal probability distribution; its measure is the “IQ”—Intelligence Quotient score on either of two specific tests. Since the Black-Cab study result, they know that one’s learning capacity is not fixed at birth, that the brain remains plastic throughout our lifespan, and its plasticity is a function of the extent and nature of its use. Our capacity to learn throughout our life is, if not unlimited, undefined and undefinable.

Neuroscientists and psychologists have learned from other studies and experiments that:

  1. The brain can continue to grow and develop after it reaches physical maturity.
  2. Challenges and mistakes stimulate brain growth and formation of new connections;
  3. Repetition and success do not stimulate growth and new connections;
    To confirm this hypothesis, the “black-cab” research team performed the same study on London bus drivers, and the team found no significant brain growth among the bus drivers. They attributed this stasis to the lack of challenges for the drivers: they drove the same, assigned route every day. Once they had learned the route, there was no more stimulation of the kind that compels the brain to grow: no more mistakes or failures.
  4. The brain will return to its original, mature state if growth activities cease;To test the permanence of this growth, the same researchers studied cab drivers as they retired. They found that among those who stayed active intellectually, the brain growth they experienced as drivers did not change; among drivers who did not stay active, the brain shrank to its size and complexity before they took the driving course.
  5. Specific behavioral and operational capabilities are located in specific regions of the brain, i. e., that a cartography of the brain is possible (and underway);
  6. The development of the frontal cortex lags development of the cortex by several years;
    (This phenomenon, by the way, goes a long way toward explaining the “strange” behavior of your adolescent children, if you are parents.)
  7. Working memory is tiny: it is capable of holding 4, perhaps, 5 chunks of information at once:
    Try to repeat any spoken, random, nine-digit number sequence backward; then, try to repeat one forward. Repeat this experiment with the sequence 123456789 (or any sequence of consecutive integers) . Compare the result.  You will have completed the third task accurately and failed at the first two.

    In the first experiment, each number was a chunk of information unrelated to the other numbers (except by the fact that they were numbers). In the second experiment, the principle, “n + 1” linked them, thereby, creating a single chunk of information. You could have used 2, 4, 6, 8, 10, 12, 14, 16, 18. Given the pattern, principle or rule, “2n”, you could repeat any sequence of nine such numbers easily; it’s one chunk instead of nine.

  8. Total long-term memory and recall capacity is undefined and undefinable;
  9. We learn and retain more when we collaborate;
  10. We retain more when we test ourselves and each other frequently—take “test” in its broadest sense;
  11. Practice makes permanent—use it or lose it (this principle holds for bad and good behaviors, sound and unsound beliefs, sound technique and unsound technique);
  12. Mindset matters.  People with growth mindsets—people who “know” that their capacity to learn and grow is not defined and delimited at birth—learn more than people with fixed mindsets—people who believe they are born “smart,” “average,” or “stupid.” People with growth mindsets feel better about themselves and are more optimistic about their prospects than people with fixed mindsets.

None of these facts are specific to learning Mathematics, Arithmetic or any other subject, learning domain, or field of inquiry. This generality implies that learning Mathematics and Arithmetic is no different from learning English, Geography, Physics, Chemistry, Music, Dance or Carpentry. It suggests, too, that, considered as a field of inquiry, Mathematics is similar to other fields in some ways essential to our  capacity to learn it.

Chemistry, Physics, Biology, Psychology and their related interdisciplinary subjects are experimental areas of inquiry. Progress and learning in them result from trial and error guided by hypotheses. The literary and performing arts are experimental, too. Progress and learning in those areas result from trial and error guided by hypotheses. Progress and learning in Mathematics result from experimentation—trial and error—guided by hypotheses. Mathematics is an experimental science, too. In the sciences, humanities and arts, we anchor the ladder of abstraction on the apparent world of mid-size objects. In Mathematics, we anchor this ladder on the Natural Numbers—the positive whole numbers. Like practitioners in other fields, mathematicians observe the behavior of numbers, attempt to codify it, and when they succeed in understanding and codifying the behavior they observe, they build new structures on those numbers and observe their behavior as well. Physicists develop theories to describe, predict and explain the behavior of matter and energy; psychologists develop theories to describe, predict and explain the behavior of individual humans; sociologists develop theories to describe, predict and explain the behavior of people in large groups. Mathematicians develop theories to describe, predict and explain the behavior of numbers.

Why, then, is the experience of learning and teaching Mathematics distinct from the experience of learning and teaching other subjects? Why is Mathematics detested by nearly every student in the schools and avoided like the plague at college or university? Is there something about numbers that makes them less accessible or more intimidating to us than planetary motion, light, people, cars, or bridges, than words, paragraphs, and stories, or than sounds, music notation, and sonatas? How can knowing these twelve facts about the science of learning inform our experiences of learning and teaching Mathematics?

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I-do, You-do, We-do: An essay on teaching mathematics – 1

 

 

In a typical mathematics classroom, the student desks are arranged in proscenium theater fashion. From the front of the room, the teacher presents definitions of new terms, new theorems, proofs of these new theorems, and demonstrations of how to apply them to example situations. The teacher writes a mathematics problem on the board, solves it, and asks for questions from the class. After this interlude for questions, the teacher writes a new problem on the board, then leads the students step-by-step through the algorithm used commonly to solve it. Teachers vary this process according to their preferences, but it is essentially the same for all who use it. Next, the teacher writes another problem or two on the board and instructs the students to solve them without guidance. After a short time, the teacher gives the solution and asks for questions. If time permits, the teacher assigns homework, the students begin working, and the teacher strolls around the room responding to student requests for help. This method is called, “I-do, we-do, you-do.” It is the prevailing method of teaching mathematics in the secondary schools. Most teachers vary their methods, but, even among those who do, most of them use it most of the time. Although the community of math education researchers with which I’m familiar recommends different methods, many, probably most, math teachers still lecture, demonstrate solutions to homework problems and test student recall in the same way we did in most math classes for the past century.

By this process, the teacher programs students to execute algorithms. Which algorithm to execute depends on the data fed into the students. A teacher’s lesson plan consists of four stages:

  1. Present the material—load the program for students to compile;
  2. Test the program—run it with sample input data to test the students’ compilation of it by working through a problem or two with them;
  3. Debug the students’ compilers—walk through the program step by step to find compilation errors;
  4. Input new data for processing—assign classwork and homework. The students run their new program(s) in class, first, then, at home to check their compilations for efficacy.

The next day, everyone checks their results. For those students with no results or with error messages and given enough time the teacher checks the program for errors, tries to fix them and the students run the debugged program to check its efficacy: Repeat debugging their compilers and programs until all or “enough” students produce acceptable output and receive no more error messages. No one knows what the students whose programs don’t need to be debugged do during this period.

Consider the process known as “solving for the unknown” or “solving for x.” To present this method, the teacher writes a specific example on the board at the front (or side or rear) of the room and determines the value of the variable. If her example is “x + 3 = 11,” we subtract 3 from both sides of the equal sign and produce the statement, “x = 8.” To enable this algorithm to handle more complex data, we call another subroutine. Given, “2x + 3 = 11,” we call two subroutines, the subtraction and division subroutines: 1) “subtract 3 from 2x + 3 and from 11 to obtain, “2x = 8”; 2) divide 2x and 8 by 2 to obtain, “x = 4.” For some students, the latter algorithm becomes the algorithm for solving problems involving subtraction and division; the former algorithm becomes a special case of the latter (i. e., when the coefficient of x is “1”). They have one program (composed of two routines) to call; the students who don’t realize that the two simple algorithms are special cases of a single algorithm still have two routines to call.

Because everyone’s working memory is tiny, students acquire and store these algorithms in their long-term memories. These algorithms become more complex and more numerous during the school year. To use them, students must first locate them in long-term memory, then choose an appropriate algorithm to apply to a particular problem, third, call it into working memory, and finally apply the algorithm correctly. As they learn more—acquire more algorithms—this process of applying their knowledge becomes more difficult exponentially. To mitigate or cope with this accumulation of algorithms, teachers have three options: do nothing, letting their students figure out how to cope with it; repeat IWY often with small variations, thereby imitating the practice and rehearsal used (successfully) in sports and performance arts; or, introduce new algorithms to automate the choice process, such as, “If ‘2x’ appears as a line in the derivation”, “then, ‘Get ‘division algorithm’ appears as a line in the derivation.” This second level of programming can be as complex and sophisticated as we wish. The question in the classroom is who programs whom? Mathematicians and logicians learned in the twentieth century that most of mathematics can be produced or reproduced, depending on your philosophical perspective, by algorithms from a tiny number of symbols, sentences, and rules. As students journey through their various mathematics classes, subjects and topics in their schools (by “school” I mean grades K-12), some students are more proficient at assembling the routines and subroutines they learn into programs they can apply to a wide spectrum of problems; some students are proficient at collecting these algorithms and identifying appropriate situations for their use; some students are proficient at both processes and some are proficient at neither process. At which of these a given student is proficient appears to be serendipitous or a combination of factors that are difficult to control. Eventually, the education system rewards those students who learn to simulate the behavior and produce the output of computing machines best. The “best” students are identified and their rewards are distributed solely according to the results of such tests. The tests don’t differentiate the processes the students use. The best students continue to accumulate and apply more of the algorithms they are fed, and commercial and academic societies esteem their accomplishment. The best of them become, in effect, carbon-based, biological computers. Or, they become mathematicians, physicists, or artists.

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A Plague of Adverbs?

Adverbs placed in front of verbs irritate me as a reader and someone who writes. I realized that this was the prime source of my irritation as a result of my reflections and research on the admissibility of the split infinitive. Until its 1993 edition, the Chicago Style Manual (“CSM”) forbade using split infinitives categorically (or, “categorically forbade” or “forbade categorically”?). Strunk & White, among others, advise against it, unless the result would be an awkward circumlocution that would arrest the flow of ideas for the reader. In its 1993 edition, the CSM relegated all discussion of the topic to a footnote, in which the authors noted their prior prohibition and current approval only in “judicious” contexts.

The split infinitive has been used by careful writers, including those esteemed or elevated to “best” status by other careful writers and careful readers, for the entire history of Modern English. I’m sure I’ve failed to notice its use in Hume, Mill, Locke, or some list of eminent contemporary philosophers who write in English and translations of works by philosophers who don’t write in English. I’m sure I’ve failed to notice its use by journalists, essayists, historians, physicists, mathematicians or literary critics or any other genre.

I haven’t mentioned writers of fiction as a group. Decisions about grammar, syntax, punctuation and style are subordinate to decisions about story and voice. Pynchon, for example, uses adverbs as a principal instrument of his orchestra. His use of them doesn’t irritate me or slow me down; they’re essential to the voices of his narrators. T. H. White and Richard Powers don’t use them in that way.

But, I do notice adverbs placed in front of verbs, so, I notice split infinitives, which are a frequent, perhaps unnecessary, byproduct of this placement. Some of examples of this placement:

  • He quickly ran to his child.
  • She slyly slipped him a piece of paper with her phone number on it.
  • To better understand the statement, she read…
  • In cooking recipes, “quickly stir”, “slowly fold”, “finely chopped”, “lightly beaten”, “coarsely chopped”, etc. (you get the picture).
  • To more clearly depict the devastation…

Rather than, “quickly ran”, why not “darted”, “dashed”, “sprinted”, or, simply, “ran”? Instead of “very large”, why not “huge” or “enormous”? Instead of “to better understand”, why not “to understand” or “to understand better”? Why not, “onions, chopped finely” instead of “finely chopped onions”? Why use “more clearly depict” at all; “to depict” suffices when the context specifies the medium of depiction. Why does this practice irritate me? What does it say to me about the writer? What impact does it have on the reader?

First, “to better understand” and “to more clearly depict” represent the writer’s introduction of adverbs to state a conclusion that the reader should reach on his own by reading the piece. Second, they introduce a comparison that inserts context between the compared objects, thereby making the comparison weaker or less visible. Or, they introduce a comparison without providing both sides of the comparison: “better understand” than what or when, or “more clearly depict” using than what medium than some other medium? Advertising prose and political speech are rife with this practice: “Papa John’s pizza: better ingredients, better pizza”; “we believe that our approach provides greater opportunities for our constituents”. Introducing comparative terms without the compared objects, acts or events is, at best, lazy or, worse, disingenuous.

Third, writers who rely on this practice evidence, in their approach to writing, sloth, inattention to detail, lack of appreciation for the value of concision, and ignorance. Writers who don’t invest time and energy in finding a colorful or precise verb to convey meaning are less energetic or diligent than those who do. They don’t use a Thesaurus or Dictionary (which includes synonyms and alternatives). English is replete with colorful, precise verbs and adjectives that don’t need modifiers to attain clarity or impact. On the contrary, frequent use of adverbs weakens their prose, reduces its impact and impedes the reader’s interest and understanding. Adverbs function like a sauce: too much obscures the flavor, texture, and appearance of the dish; too little changes the texture of the dish without enhancing its flavor.

Fourth, many writers place adverbs in front of verbs to increase the impact or importance of the modified verb. For example:

  1. “To more clearly depict the devastation wrought by the flood, she displayed artifacts from and photos of the aftermath, photos of victims and recordings of their stories with translations rather than the written word alone.”“More clearly” is unnecessary, unless the point of the piece is to compare communication media. If this comparison were the point of the piece, he or she should use it after “flood” in order to emphasize the comparison as a buttress to his or her point. The placement in the example reduces the impact of the comparison, counter to the writer’s intent. If it were inserted there to buttress the clarity of the depiction only as described, it was superfluous.
  2. “To better understand its legal ramifications, she sent the contract to her lawyers for review.”This example appears harmless. Compare it to, “She sent the contract to her lawyer for review in order to better understand its legal ramifications.” In both cases, the writer wants to emphasize that she wants to understand the legal ramifications of the contract “better”. As the term of emphasis, “better” should be placed at the end of the sentence or the relevant clause. “To understand its legal ramifications better, she …” or “She sent … to understand its legal ramifications better.”

You should place the term of emphasis at the end of most sentences or phrases, if there is one. Its impact on the reader is greater and its relation to subsequent context is reinforced. You maintain the flow of ideas and the emotional content better than if you hide it in the interior of its sentence.

By using adverbs frequently and placing them in front of the terms they modify, writers add to their word count without increasing the impact, clarity or completeness of their prose, and they complicate their structures. Such complications interrupt the flow of their ideas, obscure their underlying logic and diffuses the emotional impact of their prose. Its effect is cumulative; no single sentence with an abundance of adverbs has much impact on this reader. But, a steady barrage of qualifiers wears me out, slows me down, and increases the degree of difficulty of the piece by creating uncertainty as to the writer’s intent or by diffusing his or her message. That is, they just get in the way of my grasp of the writer’s message and intent. Examples of this practice are ubiquitous and easy to find, but, would take pages to quote, which is inappropriate for this context.

Use adverbs sparingly. Find stronger or weaker verbs and adjectives and colorful nouns. If you must use an adverb (which you will), place it with care. The adverb you deem necessary is very important to your message, so place it with care after the verb or phrase it modifies. You will improve your prose in small increments, sentence by sentence. Your readers’ return on their investments in reading it will improve. Its effect on you and them will accumulate and the result will be readers who are more satisfied and more readers who will get your point.

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Skirmish at Sentence Break

I started on this post a while ago, intending to write a prescriptivist tirade about the integrity of the infinitive forms of verbs and how violation of this integrity is a serious crime. After doing a bit of research and reflecting more about the split infinitive and its properties that might irritate me, I determined that split infinitives, per se, are not the source of my irritation. Indeed, there are contexts in which the best course of action is to split an infinitive. Before I go any further, I should say that I realize, now, that none of the language usages that irritate me are violations of some natural law of grammar, syntax, usage or style. Nevertheless, they irritate me still, but prescriptivists’ claims about these subjects are even more irritating than the practices.

A post by novelist Lucy Ferriss on the blog, Lingua Franca, which is maintained on the web site of The Chronicle of Higher Education, titled, “To Space or Not to Space” impelled me to laugh aloud at the entire situation. Note that this post is about whether to single- or double-space before starting a new sentence after a period/full-stop. In this post, Ms. Ferriss cites two other blog posts (on other blogs), one by NY Times technology blogger, Farhad Manjoo (blogging for Slate at the time, titled “Space Invaders: Why You Should Never, Ever, Add Two Spaces After a Period”) and another by Robb Forman Dew on her web site. Apparently, this topic is incendiary. Manjoo, the technology blogger, takes the biblical position, that is, he cites the convention to insert one space after a period, which convention was agreed by a committee under the auspices of a Higher Authority, the typesetter’s guild, following centuries of rampant individualism by the members of said guild wreaked havoc on the publishing and printing industries (or so I’m told). Dew and Ferriss observe that those among us who have used a typewriter, viz., those over 40 years of age, learned to insert two spaces when typewritten manuscripts were in monospace type because sentence breaks were easier for typesetters to spot. According to Manjoo (and others cited by Ferriss), the introduction of variable-space fonts obviated any need for a second space after the period by rendering the single-spaced break between sentences visible (whereas, I suppose, it was invisible prior to this technological advance). Moreover for Manjoo, the single-space break is “aesthetically more pleasing.”

One result of my investigation, however non-scholarly it has been, is that I’ve discovered (yes, yes, many others discovered it before I did) a war between prescriptivist and evolutionist grammarians, stylists and writers that has raged for centuries. As an observer with a scientific perspective might expect, the evolutionists win all of the battles in this battle-without-end. Despite their losses and continuing retreat, the prescriptivists will never lose the war, at least by capitulation; they will continue to wage guerilla warfare at the fringes as they retreat still further into the wilderness. The battle of spacing is at one of those fringes and is not a battle, but a skirmish, fierce though the participants may be.

Another result is that I’ve identified most of the sources of my irritation, but I know only that some of them irritate me because some authority figure told me in my youth they should (the prime example of this is the split infinitive). I have yet to determine why other practices, such as passive voice, are irritants.

The final result, and probably most important and useful, is that I’m not irritated (or less so as the case may be) by so-called grammar, usage, style or punctuation errors. I am still irritated by prose, fiction or poetry that impedes the flow of ideas and my access to them. The battles between writing that impels versus impedes the reader’s grasp of the matter at hand are the major battles. The writer fights these battles with him or her self every day he or she writes. And, I will continue to double-space at sentence breaks without compunction, unless I’m preparing a manuscript for publication.

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The “Chicken(expletive) Club”

Surprise!

The Baseline Scenario

By James Kwak

The only “Wall Street” “executive” to go to jail for the financial crisis was Kareem Serageldin, the head of a trading desk at Credit Suisse, according to Jesse Eisinger in a recent article. Serageldin pleaded guilty to—get this—holding mortgage-backed securities at artificially high marks in order to minimize reported losses on his trading portfolio. 

Now if that’s a crime, there are a lot of other people who are guilty of it. In fact, a major premise of the federal government’s crisis response strategy was exactly that: allowing banks to keep assets at inflated marks in order to pretend they were solvent when they weren’t. FASB changed its rules in April 2009 in order to make it easier for banks to inflate their marks. And the Obama administration’s “homeowner relief program” was designed to allow banks to delay realizing losses on their mortgage loans by dragging out—but generally not preventing—foreclosures. (Remember…

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