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A Madness to Our Methods
On Major Problems and Grand Challenges, Part 2
Before I continue, it's worth noting that all four bloggers I found writing on this topic are white males. (And I am, too.) If this doesn't bring to mind a grand challenge for the future of math education, I don't know what should.
Robert Talbert: Grand Challenges for Mathematics Education
Robert's first suggestion is to develop an open curriculum for high school and early college. Sure, we've had many curriculum projects, but I can't say I've seen many that try to seamlessly span high school and college. It makes me realize that textbook companies typically package things in ways that align with the jurisdictions of district decision-makers, but there's really no reason it has to be that way.We currently have some open curriculum projects that might give us a start on this challenge, such as the Mathematics Vision Project out of Utah and the EngageNY materials from New York. I say "give us a start" for two reasons: neither set of materials are very mature (and thus quality can be suspect) and such a project should plan for the evolution and improvement of the materials over time.
Side story: I was having dinner this summer with a retired mathematics education professor and she was telling me about her experiences volunteering to help tutor kids at a local high school. Our conversation went like this:
Her: "I didn't recognize the materials they were using, but they're a mess. It's something they found online and I don't know who put it together, but it looks like different people wrote adjacent lessons and never talked to each other, because there were big jumps from one topic to another with no explanation."
Me: "Let me guess. Are the materials from New York?"
Her: "No, Utah."
Me: "That was my second guess. And your guess about different people writing different lessons without much coordination is a very good guess of what probably happened."
Robert's second and third challenges involve the creation and use of concept inventories for mathematics, like the force concept inventory (FCI) for physics. I hear this get discussed occasionally and I'm aware of some efforts for inventories in calculus and statistics, but they aren't nearly as well recognized or used as the FCI. What's the advantage of having these inventories? They tend to make for great pre-post tests for a course or to judge if a particular teaching approach is better for students' conceptual understanding. Last week I attended a talk by Stephen Pollock who talked about his work in physics education research and the improved results we're getting in CU's physics program. The FCI played a key role in that progress, as it allowed professors to self-monitor their courses and compare their results to others who were attempting to improve their teaching. These kinds of standardized assessment tools could be equally useful and powerful in mathematics departments, especially when used in a self-monitoring sort of way instead of the all-too-common external-and-top-down-accountability-enforcing sort of way.
Robert's last recommendation is to have a preprint server for math education research. As he notes, this is a road we've tried to go down before and we didn't get very far. I don't think the problem has nearly as much to do with policy or categories of the arXiv as it does with the lack of a "preprint culture" in mathematics education. What I learned in those previous preprint discussions, and in my observations as a developing scholar, is that math educators regularly and happily share work in progress — with a select group of people. In math ed, there doesn't seem to be widespread faith in anything like Linus' Law, the open source software dictum that says, "With enough eyeballs, all bugs are shallow." I think the math wars led to a lot of distrust, and some of it is very rational. It's safer to only share preliminary work with a few scholars who share similar methods and theoretical frameworks, and then refine the work after peer review before publication in a journal whose readership is likely to understand the work. Maybe it shouldn't be this way, but to move forward we're going to have to confront some of these beliefs.
Patrick Honner: My Grand Challenge for Mathematics Education
Patrick described in some detail a single grand challenge: "Build and maintain a free, comprehensive, modular, and adaptable repository of learning materials for all secondary mathematics content." It's worth reading his post and the comments. This challenge hits close to home for me because it touches on my own research, including the difficulty of coordinating distributed curriculum development and the infrastructure needed to support the customization of curriculum.I've always been intrigued by the concept of "modular and adaptable" curriculum materials. Personally, I thought I did my best work as a teacher when I offloaded my curriclum to a high-quality textbook that I'd been trained to use. That's an anathema to many math teachers who take improvisation of curriculum to be a sign of quality teaching. (It's not, by the way. There can be good and bad improvisation, just as there can be good and bad offloading.) I tried writing my own curriculum for a while and found it exhausting and ineffective. In a couple hours per day, I just couldn't create from scratch anything that I thought was as good as the texts coming from university-based curriculum teams with decades of experience and millions of dollars of funding. Go figure. I got better results when I leveraged the rigor and coherence of a text that integrated topics, contexts, tools, and routines across its lessons and units.
With enough effort, however, Patrick's recommendation could lead to a set of materials that are both modular and coherent. I've always seen these in opposition, a sort of "textbook paradox." I speculate that teachers who value being able to adapt and improvise with their curriculum will resist or find ineffective those textbooks built around coherence. It's relatively straightforward to replace a lesson in a very traditional textbook that relies on an isolated set of examples and practice problems. But for reform-based materials, such as IMP, CPM, and Everyday Math, skipping around in the textbook can lead to trouble. Saxon texts, for that matter, with their use of "incremental development," should make a teacher think twice before skipping or improvising a lesson. Thus, the paradox: teachers who want to improve the quality of their curriculum materials probably have an easier time adapting materials that are lower quality to begin with, but if they start with higher-quality materials, adaptation can sacrifice coherence and make adaptation more difficult.
Adaptation can still be done with any curriculum, but it takes skill. Currently, that skill must come almost entirely from the teacher, as the texts aren't smart enough to know what you've been skipping. Take Patrick's challenge far enough, however, and maybe we could have a curriculum that is smart enough to know what you've used and not used. Imagine a statistics curriculum that automatically modifies tasks to use a preferred data set, or a system that reminds you that you should probably include a lesson and practice with mean absolute deviation prior to teaching standard deviation. Or, for algebra, imagine a system that let you decide whether to teach exponential functions before or after quadratics, with the curriculum being smart enough to recommend appropriate modeling tasks. When I helped a school pilot Accelerated Math in 1999 and used the exprience as my student teaching action research project, I really thought we were on the cusp of a wave of "smart curriclum" that would help build coherence into teacher-adapted curriculum. We're not there yet, but a challenge like the one Patrick describes could get us much closer.
David Wees: Grand Challenge for NCTM
David's grand challenges focuses more on people than materials: "Develop a comprehensive, national professional development model that supports the high quality mathematics instruction they have been promoting for many years." ("They" refers to NCTM.) David breaks this challenge into bullet points around the development and scaling of "core practices."I'm a firm believer in this idea. I get resistance from those who love the creative and spontaneous aspects of teaching, but I think that learning to teach should involve the learning and practicing of key teaching practices. Thankfully, there are some very good people working in this area. Until recently, their efforts were somewhat scattered and referred to with such names as "high-leverage practices" or "ambitious teaching." Thankfully, at AERA this past spring, many of the heavy hitters doing this work came together to address the need for a common language around these practices and supporting their development and use. For a good idea of what a list of core practices might look like, check out the Teaching Works project from the University of Michigan. I have a hard time finding anything on that list that doesn't seem essential to quality teaching, and it reminds me that the list is really the easy part. The real work comes in developing those practices in preservice and inservice teachers, and I'm glad that David had his mind on that development when he articulated his grand challenge.
Bryan Meyer:
@NCTM ...space for them to study their own teaching practices in collaboration with their colleagues.
— Bryan Meyer (@doingmath) August 9, 2014
Bryan's challenge isn't math-specific but it could help a lot of math teachers. Our expectations for teacher collaboration exceed our opportunities, and changing this involves a lot of people and resources. In some countries there are limits to how many student contact hours a teacher can have because they are expected to be collaborating with or observing other teachers for several hours each day. What if we did that in the United States? We'd have to seriously rethink our resources. Suppose you currently teach six periods a day with about 24 students in each class. What if you only taught four periods with 36 students in each class, and you had the extra two periods to work with other teachers to ensure your instruction in those four periods was better? (For those of you who already have 36 students in your classes and are working out even larger classes in your heads, I'm sorry.) Or, instead of changing class sizes, what if salaries were lowered to accommodate the hiring of extra teachers?While these questions suggest difficult choices, they do seem like questions that could be answered with adequate research, and maybe there exists some research already that could help us answer them. Still, research in education isn't always very effective at changing school cultures or how resources are allocated. I don't want to sound too pessimistic, but I'm thinking that Bryan's challenge is going to have to focus as much on understanding and developing cultures of collaboration amongst teachers as it would scheduling and resource allocations.
Parting Thoughts
While it may have been personally beneficial for me to put a couple thousand words into a grand challenge I thought about on my own, I realize that our best hopes for meeting a grand challenge come when we share and push each other's ideas. As a student of curriculum and instruction, I find much to like in Robert and Patrick's thoughts about curriculum and David and Bryan's thoughts about instruction. There's some really meaty stuff there.I've also tried to think about what wasn't mentioned as a challenge. Nobody said, "I really think we need to better understand how students think about ratio/functions/number/proof/etc." While people are hard at work on such questions, I don't think there's any widespread perception that a lack of research in specific areas of student mathematical understanding is what is holding us back. (If there's a challenge I should be writing about, it's about the dissemination and use of this information.) I'm also happy to see that people weren't writing challenges involving new sets of academic standards. It's rather unfortunate that so much energy is being put into debating Common Core when it seems quite likely that standards account for little of the variability in student outcomes. We have a list of stuff we want students to learn. Fine. I'm ready to focus more of our efforts on the learning, not the list.
Lastly, to touch briefly on the challenge I hinted at near the top of this post, I didn't see any equity-focused grand challenges. I think I speak for Robert, Patrick, David, and Bryan when I say we all believe in achieving equitable participation and outcomes in mathematics education. Then again, we can't just say that and expect equity to come about by accident. There are elements of each challenge mentioned that could be used to promote equity, but it's going to take a more explicit focus than we've given it. In fact, maybe the first step is to significantly change the representation implied when I say "we." It seems simple enough, but privilege has a way of producing thoughts of "for" and "to" instead of "with," and that's a challenge for the kinds of people and organizations who pose challenges.
Teaching Statistics: Textbook Considerations
I have the pleasure of teaching an undergraduate basic statistics class this fall for the third consecutive year. It's not a class I had any specific preparation to teach, but I've tried to make up for that by becoming familiar with some of the statistics education literature, bolstering my content knowledge (although I doubt it will ever be as wide or deep as I'd like), getting access to good resources, and being mindful of the needs of my students.
First, it would help to know a little bit about the course. Most strikingly, the class only meets once a week on a Thursday from 4:30 to 7. If you're used to teaching 180-day school years, you really have to wrap your head quickly around the idea that you're only going to see these students 15 times before finals. Also, despite the class being taught in the School of Education, it's not required of any education students. Instead, the class consists mostly of students from two majors: Sociology and Speech, Language, and Hearing Sciences. Honestly, most of them admit to avoiding math classes, but they usually need the stats class to apply for graduate school. As for the content of the course, here is how it is described in the university catalog:
Introduces descriptive statistics including graphic presentation of data, measures of central tendency and variability, correlation and prediction, and basic inferential statistics, including the t-test.
And that's it. As someone who works almost daily with the Common Core State Standards, building a course around such a sparse description would be quite a challenge, especially for a first-time instructor. When I talked to Derek Briggs about teaching the course, he advised that I use his preferred text, Statistics by Freedman, Pisani, and Purves. I'd recently used Agresti and Finlay's Statistical Methods for the Social Sciences for my qualitative methods courses, and while that book suited me pretty well, I was open to something different so I ordered the Freedman text for my class.
In hindsight, the Freedman text was fine, and the Agresti text would have been fine, too. Both were decently well-written and had plenty of problems to assign, but that's the thing — I was looking for a text that offered considerably more than explanations followed by problem sets. I really wanted something that supported students working together in groups during class, making sense of the material as we went along.
One book that had gotten my attention was Workshop Statistics: Discovery with Data by Rossman and Chance. I recognized Beth Chance's name immediately from some of the stats education literature I'd read, and felt good that this text would offer what I was looking for. I used the text last year and was not disappointed, and will be using it again this year. Below is a summary of some of the reasons I like Workshop Statistics.
Context Continuity
In the front matter of the book, Workshop Statistics contains a list of activities by application — in other words, they've categorized all the problems by context and indexed exactly where those contexts get used. The list of related problems appears again with each problem in the text (inset in picture above), so it's easy for me or my students to refer back or forward to where that context appears. I believe in teaching mathematics rooted in context when possible, so I found this an especially helpful way of finding problems that might be relevant or interesting to the students in my class.
Preliminaries
Every topic (lesson) in the text opens with some preliminary questions. Some involve data collection, which is great, but at the very least it gives students an opportunity to consider a question and how we might answer it. If Dan Meyer has made anything clear, it's that we shouldn't teach math as finding answers to questions that nobody has bothered to ask.
In Brief
The end-of-topic summary certainly isn't unique to this text, but the "You should be able to" statements are very handy for writing objectives for standards-based grading. (I hope to write about my SBG approach in a future post.)
Online Supports and Simulations
Besides both online instructor and student resources, the text uses a number of custom applets that often really help illustrate some of the concepts in the course. Some are Java, but a number have been converted to JavaScript for use on more platforms. I've avoided having students use software beyond a spreadsheet, and some of these applets have saved us from having to purchase SPSS (expensive!) or trying to use R (steep learning curve!).
Activities
The in-class activities use some interesting contexts and support groups working together. If anything they can be a bit over-scaffolded, but that relieves me from having to lecture much and I can spend most of my time going group-to-group in the classroom and dealing with questions more intimately.
Overall
There are a number of smaller things that I'm fine with, although they aren't deal-makers or deal-breakers. The pacing of the text is good — if we cover about two topics a week, we finish the text and pretty much everything one would expect in a basic statistics course. The order of the topics is sensible, too. Typically, it makes sense to put descriptive statistics before inferential statistics, and to work from one-variable stats to two-variable stats. This book is no different. Some texts put linear regression earlier, and where probability should land in a book seems to be negotiable. The placement of those topics in this book is fine for this course and the progression from topic to topic was very manageable.
Other than my first day activity, I haven't written much about teaching stats, but look for me to change that this semester.
RYSK: Dewey's The Child and the Curriculum (1902)
In my last RYSK post, I joined some other math teachers in discussing Richard Skemp's Relational Understanding and Instrumental Understanding (1976). Skemp's is a classic article that wrestles with a duality; in Skemp's case, the distinction between math for procedural skill versus a deeper mathematical understanding. For this meeting we turned the clock back further to Dewey's The Child and the Curriculum (1902), another classic article struggling with a duality in learning.
As D. C. Phillips (1998) noted in his review of The Child and the Curriculum, Dewey had a particular passion for dualisms, addressing more than three dozen of them in Democracy and Education (1916) alone. As Skemp and many others have shown, dualisms can be a starting point towards building a more nuanced understanding, as "neither is the world divided into a series of polar opposites, nor is it one" (Phillips, 1998, p. 404). Somewhere in between the opposites and the same lies the understanding many of us seek.
The Child and the Curriculum presents a particular dualism that very much persists to this day: should education be rooted in content, or in the needs and wants of the child? In Dewey's time, the push for a content focus was seen in the report by The Committee of Ten, not totally unlike how we currently push for content with documents like the Common Core State Standards. Dewey, like Skemp, also uses the metaphor of the map, using it to describe the logical versus psychological ordering of subject matter. Again, we still struggle with this duality today; last November Jere Confrey remarked at a conference, "There are some parts of the common core standards that I would express as mathematicians’ thought experiments," meaning we often guess how mathematical understanding is developed based on the structure of the mathematics instead of research on how children actually learn. These, of course, are not opposites, but they aren't the same, either.
Most of our discussion used Dewey as a prompt for thinking how Dewey's words more than a century ago frame modern challenges in education. (Reading Dewey seems particularly good for this kind of activity.) I was joined by +Nik Doran, +Bryan Meyer, +Nat Banting, and +Chris Robinson was feeding us ideas in the chat as we went along.
I plan to have more of these discussions, and hope we can get into some literature that really addresses research in math education versus some of these more theoretical or philosophical pieces. If you have suggestions for articles to read, please add them and vote them up or down in Google Moderator!
RYSK: Skemp's Relational Understanding and Instrumental Understanding (1976)
This classic think piece from Richard Skemp, despite being now almost 40 years old, still gets a great deal of attention amongst mathematics educators. I'd somehow missed it in my own preparation, which I find surprising how much I've spent studying the math wars. Skemp describes two perspectives on understanding mathematics, one which he calls relational and the other he describes as instrumental. Relational understanding is related to what we might think of a "deeper" understanding, something that reflects how and why mathematics works and is applied. Instrumental understanding relates to those reliable and typically efficient procedures we apply to produce mathematically correct answers. The part of Skemp's article that really sticks out for me is his suggestion that "mathematics" might be used too broadly: "I used to think that maths teachers were all teaching the same subject, some doing it better than others. I now believe that there are two effectively different subjects being taught under the same name, 'mathematics'" (p. 91)
Today +Chris Robinson, +Joshua Fisher, +Nat Banting, +Nik Doran, and I (pictured left-to-right along the bottom) met via Google+ Hangout to discuss Skemp's article. We discussed examples of each kind of understanding, whether one is a subset or prerequisite to the other, and the various influences that lead us to emphasize one over the other, such as curriculum and assessment.
(Some research needs to be summarized, while some needs to be expanded on. That's my explanation for why it will likely take you longer to watch the video than to read the original article.)
I've been in discussions like this before and I always find them to be fascinating. Still, it seems difficult to really get at the root: What is mathematics, and how can and should our beliefs about mathematics change? There are also strong implications for curriculum design and learning theory, as finding the right balance in our approach to both kinds of understanding should lead to better student outcomes.
RYSK: Ball, Thames, & Phelps's Content Knowledge for Teaching: What Makes It Special? (2008)
My last two posts summarized the underpinnings of Shulman's pedagogical content knowledge and Deborah Ball's early work building upon and extending Shulman's theories. Now we jump from Ball's 1988 article to one she co-authored in 2008 with University of Michigan colleagues Mark Thames and Geoffrey Phelps, titled Content Knowledge for Teaching: What Makes It Special?
This article starts by looking at the 20+ years we've had to further develop Shulman's theories of pedagogical content knowledge (PCK). Despite the theory's widespread use, Ball and colleagues claim it "has lacked definition and empirical foundation, limiting its usefulness" (p. 389). (See also Bud Talbot's 2010 blog post and related efforts.) In fact, the authors found that a third of the more than 1200 articles citing Shulman's PCK
do so without direct attention to a specific content area, instead making general claims about teacher knowledge, teacher education, or policy. Scholars have used the concept of pedagogical content knowledge as though its theoretical founcations, conceptual distinctions, and empirical testing were already well defined and universally understood. (p. 394)
To build the empirical foundation that PCK needs, Ball and her research team did a careful qualitative analysis of data that documented an entire year of teaching (including video, student work, lesson plans, notes, and reflections) for several third grade teachers. Combined with their own expertise and experience, and other tools for examining mathematical and pedagogical perspectives, the authors set out to bolster PCK from the ground up:
Hence, we decided to focus on the work of teaching. What do teachers need to do in teaching mathematics -- by virtue of being responsible for the teaching and learning of content -- and how does this work demand mathematical reasoning, insight, understanding, and skill? Instead of starting with the curriculum, or with standards for student learning, we study the work that teaching entails. In other words, although we examine particular teachers and students at given moments in time, our focus is on what this actual instruction suggests for a detailed job description. (p. 395)
For Ball et al., this includes everything from lesson planning, grading, communicating with parents, and dealing with administration. With all this information, the authors are able to sharpen Shulman's PCK into more clearly defined (and in some cases, new) "Domains of Mathematical Knowledge for Teaching." Under subject matter knowledge, the authors identify three domains:
- Common content knowledge (CCK)
- Specialized content knowledge (SCK)
- Horizon content knowledge
And under pedagogical content knowledge, the authors identify three more domains:
- Knowledge of content and students (KCS)
- Knowledge of content and teaching (KCT)
- Knowledge of content and curriculum
Ball describes each domain and uses some examples to illustrate, mostly from arithmetic. For my explanation, I'll instead use something from high school algebra and describe how each domain applied to my growth of knowledge over my teaching career.
Common Content Knowledge (CCK)
Ball et al. describe CCK as the subject-specific knowledge needed to solve mathematics problems. The reason it's called "common" is because this knowledge is not specific to teaching -- non-teachers are likely to have it and use it. Obviously, this knowledge is critical for a teacher, because it's awfully difficult and inefficient to try to teach what you don't know yourself. As an example of CCK, my knowledge includes the understanding that \((x + y)^2 = x^2 + 2xy + y^2\). I've known this since high school, and I would have known it whether or not I became a math teacher.Specialized Content Knowledge (SCK)
SCK is described by Ball et al. as "mathematical knowledge and skill unique to teaching" (p. 400). Not only do teachers need this knowledge to teach effectively, but it's probably not needed for any other purpose. For my example, I need to have a specialized understanding of how \((x+y)^2\) can be expanded using FOIL or modeled geometricaly with a square. It may not be all that important for students to understand both the algebraic and geometric ways of representing this problem, but I need to know both so I can better understand student strategies and sources of error. Namely, the error that \((x + y)^2 = x^2 + y^2\).Horizon Content Knowledge
This domain was provisionally included by the authors and described as, "an awareness of how mathematical topics are related over the span of mathematics included in the curriculum" (p. 403). For my example of \((x + y)^2 = x^2 + 2xy + y^2\), I need to understand how previous topics like order of operations, exponents, and the distributive property relate to this problem. Looking forward, I need to understand how this problem relates to factoring polynomials and working with rational expressions.Knowledge of Content and Students (KCS)
This is "knowledge that combines knowing about students and knowing about mathematics" (p. 401) and helps teachers predict student thinking. KCS is what allows me to expect students to incorrectly think \((x + y)^2 = x^2 + y^2\), and to tie that to misconceptions about the distributive property and exponents. I'm not sure I had this knowledge for this example when I started teaching, but it didn't take me long to figure out that it was a very common student mistake.Knowledge of Content and Teaching (KCT)
Ball et al. say KCT "combines knowing about teaching and knowing about mathematics" (p. 401). While KCS gave me insight about why students mistakingly think \((x + y)^2 = x^2 + y^2\), KCT is the knowledge that allows me to decide what to do about it. For me, this meant choosing a geometric representation for instruction over using FOIL, which lacks the geometric representation and does little to address the problem if students never recognize that \((x + y)^2 = (x + y)(x + y)\).Knowledge of Content and Curriculum
For some reason, Ball et al. include this domain in a figure in their paper but never describe it explicitly. They do, however, scatter enough comments about knowledge of content and curriculum to imply that teachers need a knowledge of the available materials they can use to support student learning. For my example, I know that CPM uses a geometric model for multiplying binomials, Algebra Tiles/Models can be used to support that model, virtual tiles are available at the National Library of Virtual Manipulatives (NLVM), and the Freudenthal Institute has an applet that allows students to interact with different combinations of constants and variables when multiplying polynomials.Some of the above can be hard to distinguish, but thankfully Ball and colleagues clarify by saying:
In other words, recognizing a wrong answer is common content knowledge (CCK), whereas sizing up the nature of an error, especially an unfamiliar error, typically requires nimbleness in thinking about numbers, attention to patterns, and flexible thinking about meaning in ways that are distinctive of specialized content knowledge (SCK). In contrast, familiarity with common errors and deciding which of several errors students are most likely to make are examples of knowledge of content and students (KCS). (p. 401)
In their conclusion, the authors hope that this theory can better fill the gap that teachers know is important, but isn't purely about content and isn't purely about teaching. We can hope to better understand how each type of knowledge above impacts student achievement, and optimize our teacher preparation programs to reflect that understanding. Furthermore, that understanding could be used to create new and improved teaching materials and professional development, and better understand what it takes to be an effective teacher. With this in mind, you can gain some insight to what Ball was thinking when she gave this congressional testimony:
RYSK: Ball's Unlearning to Teach Mathematics (1988)
Dan Lortie's 1975 book Schoolteacher clarified an idea that teachers already know: how we teach is greatly influenced by the way we've been taught. Lortie called the idea apprenticeship of observation, and it specifically refers to how teachers, having spent 13,000+ hours in classrooms as students, take that experience as a lesson in how to be a teacher. What we often fail to deeply reflect on, however, is that we were only seeing the end product of teaching. We didn't see the lesson planning, go to summer conferences, attend professional development workshops, study the science of learning, or take part in the hundreds of decisions a teacher makes every day. Just observing isn't a proper apprenticeship, even after thousands of hours watching good teachers. I think of it this way: I watch a lot of baseball, and I can tell good baseball from bad. This hardly makes me ready to play, sadly, because I'm not spending hours taking batting practice, participating in fielding drills, studying video, digesting scouting reports, and working out in the offseason. Just as watching a lot of baseball doesn't really prepare me to play baseball, watching a lot of teaching doesn't really prepare someone to teach. Still, all those hours heavily influence our beliefs, both of teaching and of subject matter.
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| Deborah Ball (CC BY-NC-ND House Committee on Education and the Workforce Democrats) |
By selecting permutations as the topic, Ball hoped to expose these introductory teachers to a topic they'd never studied formally. By carefully observing how her students constructed their knowledge, Ball would be able to see how their prior understandings about mathematics influenced their learning. The unit lasted two weeks. In the first phase of the unit, Ball tried to engage the students in the sheer size and scope of permutations, like by thinking about how the 25 students could be sat in 1,551,121,000,000,000,000,000,000 different seating arrangements. Working back to the simplest cases, with 2, 3, and 4, students, students could think and talk about the patterns that emerge and understand how the permutation grows so quickly. For homework, Ball asked students to address two goals: increase their understanding of permutations, but also think about the role homework plays in their learning, including how they approach and feel about it and why. In the second phase of the unit, Ball has her students observe her teaching young children about permutations, paying attention to the teacher-student interactions, the selection of tasks, and what the child appears to be thinking. In the last phase of the unit, the students become teachers and try helping someone else explore the concept of permutations. After discussing this experience, students wrote a paper reflecting on the entire unit.
From other research, Ball knew that teacher educators often assumed their students had mastery of content knowledge. Even moreso, future elementary math teachers themselves assumed they had mastery over the mathematical content they'd be expected to teach. She knew, however, that there was something extra a teacher needed to teach that content. Citing Shulman's pedagogical content knowledge, along with numerous others, Ball describes some ways we can think about what that special content knowledge for teaching is, but admits that her permutations project was too narrow to explore how teachers construct and organzie that knowledge. The project would, however, give insight to her students' ideas about mathematics, and assumptions they make about what it means to know mathematics. For example, a student named Cindy wrote:
I have always been a good math student so not understanding this concept was very frustrating to me. One thing I realized was that in high school we never learned the theories behind our arithmetic. We just used the formulas and carried out the problem solving. For instance, the way I learned permutations was just to use the factorial of the number and carry out the multiplication ... We never had to learn the concepts, we just did the problems with a formula. If you are only multiplying to get the answer every time, permutations could appear to be very easy. If you ask yourself why do we multiply and really try to understand the concept, then it may be very confusing as it was to me. (p. 44)
Comments like this revealed that many of Ball's students relied on a procedural view of mathematics, one where the question "Why?" had been rarely asked. Ball also noticed a theme in her students' reflections about knowing math "for yourself" versus for teaching. Alison wrote:
I was trying to teach my mother permutations. But it turned out to be a disaster. I understood permutations enough for myself, but when it came time to teach it, I realized that I didn't understand it as well as I thought I did. Mom asked me questions I couldn't answer. Like the question about there being four times and four positions and why it wouldn't be 4 x 4 = 16. She threw me with that one and I think we lost it for good there.
From observing a young student learn about permutations in phase two, Ball noticed that some of her students started to challenge some of their assumptions they made about themselves as learners. Both from her experience and from the literature, Ball knew that elementary preservice teachers are often the most apprehensive about teaching mathematics. In some cases, these students choose to teach elementary in the hopes of avoiding any mathematical content they might find difficult. Changing these feelings about mathematics and about themselves is a difficult task for the teacher educator, but Ball did see progress. Christy, for example, said, "Most of all, I realized that I do have the ability to learn mathematics when it is taught in a thoughtful way" (p. 45). Unfortunately, not all shared this experience, as Mandy said she "did not enjoy the permutations activities because I was transported in time back to junior high school, where I remember mathematics as confusing and aggravating. Then as now, the explanations seemed to fly by me in a whirl of disassociated numbers and words" (p. 45).
In her conclusion, Ball says activities like the permutations project can be used by teacher educators to expose students' "knowledge, beliefs, and attitudes" (p. 46) about math and teaching math. By understanding the ideas prospective teachers bring with them, teacher educators can better develop preparation programs that address those beliefs in ways that strengthen the positive ones while changing some negative ones. Also, by including these kinds of activities with introductory preservice teachers, this can raise their expectations for what they will encounter later in methods classes. Summarizing, Ball concludes:
How can teacher educators productively challenge, change, and extend what teacher education students bring? Knowing more about what teachers bring and what they learn from different components of and approaches to professional preparation is one more critical piece to the puzzle of improving the impact of mathematics teacher education on what goes on in elementary mathematics classrooms. (p. 46)
RYSK: Shulman's Those Who Understand: Knowledge Growth in Teaching (1986)
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| Lee Shulman. (CC BY-NC) Penn State |
Wondering why the public often has a low opinion of teachers' knowledge and skill, Shulman first looks at the history of teacher examinations. In the latter half of the 1800s, examinations for people wishing to teach were almost entirely content-based. In 1875, for example, the California State Board examination for elementary teachers gave a day-long, 1000-point exam that covered everything from mental arithmetic to geography to vocal music. Its section on the theory and practice of teaching, however, was only worth 50 of the 1000 points and included questions like, "How do you interest lazy and careless pupils?" (p. 5)
By the 1980s, when Shulman wrote this article, teacher examinations painted almost the opposite picture. Instead of focusing on content, they focused on topics such as lesson planning, cultural awareness, and other aspects of teacher behavior. While the topics usually had roots in research, they clearly did not represent the wide spectrum of skills and knowledge a teacher would need to be a successful teacher. More specifically, by the 1980s our teacher examinations seemed to care as little about content as the examinations a century prior seemed to care about pedagogy.
Looking back even further in history, Shulman recognized that we haven't always made this distinction between content and teaching knowledge. The origins of the names of our highest degrees, "master" and "doctor," both essentially mean "teacher" and reflected the belief the highest form of knowing was teaching, an idea going back to at least Aristotle:
We regard master-craftsmen as superior not merely because they have a grasp of theory and know the reasons for acting as they do. Broadly speaking, what distinguishes the man who knows from the ignorant man is an ability to teach, and this is why we hold that art and not experience has the character of genuine knowledge (episteme) -- namely, that artists can teach and others (i.e., those who have not acquired an art by study but have merely picked up some skill empirically) cannot. (Wheelwright, 1951, as cited in Shulman, 1986, p. 7)
Shulman saw a blind spot in this dichotomy between content and teaching knowledge. What he saw was a special kind of knowledge that allows teachers to teach effectively. After studying secondary teachers across subject areas, Shulman and his fellow researchers looked to better understand the source of teachers' comprehension of their subject areas, how that knowledge grows, and how teachers understand and react to curriculum, reshaping it into something their students will understand.
Pedagogical Content Knowledge
To better understand this special knowledge of teaching, Shulman suggested we distinguish three different kinds of content knowledge: (a) subject matter knowledge, (b) pedagogical content knowledge, and (c) curricular knowledge. It was the second of these, pedagogical content knowledge (PCK), that Shulman is best remembered for. Shulman describes the essence of PCK:Within the category of pedagogical content knowledge I include, for the most reguarly taught topics in one's subject area, the most useful forms of representation of those ideas, the most powerful analogies, illustrations, examples, explanations, and demonstrations -- in a word, the ways of representing and formulating the subject that make it comprehensible to others. Since there are no single most powerful forms of representation, the teacher must have at hand a veritable armamentarium of alternative forms of representation, some of which derive from research whereas others originate in the wisdom of practice. (p. 9)
In addition to these three kinds of teacher knowledge, Shulman also proposed we consider three forms of teacher knowledge: (a) propositional knowledge, (b) case knowledge, and (c) strategic knowledge. These are not separate from the three kinds of knowledge named above, but rather describe different forms of each kind of teacher knowledge. Propositional knowledge consists of those things we propose teachers do, from "planning five-step lesson plans, never smiling until Christmas, and organizing three reading groups" (p. 10). Shulman organized propositional knowledge into principles, maxims, and norms, with the first usually emerging from research, the second coming from a practical experience (and generally untestable, like the suggestion to not smile before Christmas), and the third concerning things like equity and fairness. Propositions can be helpful but difficult to remember to implement as research intended.
Learning propositions out of context is difficult, so Shulman proposed case knowledge as the second form of teacher knowledge. By case, he means learning about teaching in a similar way a lawyer learns about the law by studying prior legal cases. In order to truly understand a case, a learner starts with the factual information and works towards the theoretical aspects that explain why things happened. By studying well-documented cases of teaching and learning, teachers consider prototype cases (that exemplify the theoretical), precedents (that communicate maxims), and parables (that communicate norms and values). (If you're scoring at home, Shulman has now said there are three types of cases, which itself is one of three forms of knowledge, each of which capable of describing three different kinds of content knowledge.)
The last form of knowledge, strategic knowledge, describes how a teacher reacts when faced with contradictions of other knowledge or wisdom. Knowing when to bend the rules or go against conventional wisdom takes more than luck -- it requires a teacher to be "not only a master of procedure but also of content and rationale, and capable of explaining why something is done" (p. 13).
The value of this article by Shulman goes beyond the theoretical description of pedagogical content knowledge. Additionally, this article serves as a strong reminder that when we judge a teacher, we must consider a broad spectrum of skills and abilities, and not limit ourselves to only those things we think can be easily measured. As Shulman explains:
Reinforcement and conditioning guarantee behavior, and training produces predictable outcomes; knowledge guarantees only freedom, only the flexibility to judge, to weigh alternatives, to reason about both ends and means, and then to act while reflecting upon one's actions. Knowledge guarantees only grounded unpredictability, the exercise of reasoned judgment rather than the display of correct behavior. If this vision constitutes a serious challenge to those who would evaluate teaching using fixed behavioral criteria (e.g., the five-step lesson plan), so much the worse for those evaluators. The vision I hold of teaching and teacher education is a vision of professionals who are capable not only of acting, but of enacting -- of acting in a manner that is self-conscious with repect to what their act is a case of, or to what their act entails. (p. 13)
In our current era of teacher evaluation and accountability, with all its observational protocols and test score-driven value added models, this larger view of teaching presented to us by Shulman is a gift. His recommendation that teacher evaluation and examination "be defined and controlled by members of the profession, not by legislators or laypersons" (p. 13) is a wise one, no matter how politically difficult. Shulman hoped for tests of pedagogical content knowledge that truly measured those speical skills that teachers have, skills that non-teaching content experts would not pass. I don't think those measurement challenges have been overcome, but continuing towards that goal should strengthen teacher education programs while also improving the perception of teaching as a profession. As Shulman concludes (p. 14):
We reject Mr. Shaw and his calumny. With Aristotle we declare that the ultimate test of understanding rests on the ability to transform one's knowledge into teaching.
Those who can, do. Those who understand, teach.
RYSK: Shepard's The Role of Assessment in a Learning Culture (2000)
In her presidential address at the 2000 AERA conference, Lorrie Shepard revealed a vision for the future of educational assessment. That message turned into an article titled The Role of Assessment in a Learning Culture, and its message is still very much worth hearing today. Lorrie Shepard remains a globally-respected expert in assessment, psychometrics, and their misuses, and I'd think she was totally awesome even if she wasn't my boss.
Shepard is often present for debates about large-scale testing, but this paper focuses on classroom assessment -- the kind, says Shepard, "that can be used as a part of instruction to support and enhance learning" (p. 4). Shepard does this by first explaining a historical perspective, then describing a modern view of learning theories, then envisioning how new assessment practices could support those theories. Impressively, she does this all in just 11 well-written pages. (In fact, given that the paper is available on the web, I wouldn't blame you at all for skipping this summary and just reading the article for yourself.)
History
Shepard highlights several major themes from history that have continued to drive our assessment practices. One is the social efficiency movement, which "grew out of the belief that science could be used to solve the problems of industrialization and urbanization" (p. 4). While this movement might have helped our economic and educational systems scale rapidly (think about Ford and the assembly line), social efficiency carries with it a belief that people have a certain innate (and largely fixed) set of capabilities, and our society operates its most efficiently when we measure people and match their capabilities to appropriate education and employment. For example, students were often given IQ tests to determine if their future path should lie on a particular academic or vocational track.The dominant learning theories of the early and mid-1900s were associationism and behaviorism, both of which promoted the idea that learning was an accumulation of knowledge that could be broken into very small pieces. Behaviorism was also tied closely to theories of motivation, as it was believed learning was promoted when knowledge was made smaller and opportunities for positive reinforcement for learning were made greater. Much of the assessment work related to these beliefs can be traced back to Edward Thorndike, considered to be the father of scientific measurement and earliest promoter of "objective" testing. It's been 100 years since Thorndike was elected president of the American Psychological Association, and decades since his ideas seriously influenced the leading edges of learning theory. Still, as most anyone who works in schools or experienced a traditional education can attest, ideas of social efficiency and behaviorism are still evident in schools -- especially in our assessment practices.
Together, the theories of social efficiency, scientific measurement, and beliefs about intelligence and learning form what Shepard sees as the dominant 20th-century paradigm. (See page 6 of the paper for a diagram.) It's important to begin our discussion here, says Shepard, because "any attempt to change the form and purpose of classroom assessment to make it more fundamentally a part of the learning process must acknowledge the power of these enduring and hidden beliefs" (p. 6).
Modern Theories
In the next section, Shepard describes a "social-constructivist" framework that guides modern thought on learning:The cognitive revolution reintroduced the concept of mind. In contrast to past, mechanistic theories of knowledge acquisition, we now understand that learning is an active process of mental construction and sense making. From cognitive theory we have also learned that existing knowledge structures and beliefs work to enable or impede new learning, that intelligent thought involves self-monitoring and awareness about when and how to use skills, and that "expertise" develops in a field of study as a principled and coherent way of thinking and representing problems, not just as an accumulation of information. (pp. 6-7)
These ideas about cognition are complimented by Vygotskian realizations that the knowledge we construct "is socially and culturally determined" (p. 7). Unlike Piaget's view that development preceded learning, this modern view sees how development and learning interact as social processes. While academic debates remain about the details of cognitive vs. social (and vs. situative vs. sociocultural vs. social constructivist vs. ...), for practical purposes these theories can coexist and are already helping teachers view student learning in ways that improve upon behaviorism. However, Shepard says, since about the 1980s this has left us in an awkward state of using new theories to inform classroom instruction, while still depending on old theories to guide our assessments.
Improving Assessment
If we wish to make our theories of assessment compatible with our theories of learning, Shepard says we need to (a) change the form and content of assessments and (b) change the way we use and regard assessment in classrooms. Some of the potential changes in form are already familiar to most teachers, such as a greater use of open-ended performance tasks and setting assessment tasks in real-world contexts. Furthermore, Shepard suggests that classroom routines and related assessments should reflect the need to socialize students "into the discourse and practices of academic disciplines" (p. 8) as well as foster metacognition and important dispositions. Shepard does not go into much more detail here because others have already given attention to these ideas, but gives us this simple yet powerful idea (p. 8):with good instructional tasks."
Next Shepard pays special attention to negative effects of high-stakes testing. Shepard could be called a believer in standards-based education, but recognizes how "the standards movement has been corrupted, in many instances, into a heavy-handed system of rewards and punishments without the capacity building and professional development originally proposed as part of the vision (McLaughlin & Shepard, 1995)" (p. 9). Unfortunately, Shepard's predictions have held true over the past 12 years: we've seen test scores distorted under political pressure, a corruption of "teaching to the test," and a trend towards the "de-skilling and de-professionalization of teachers" (p. 9). What's worse might be a decade of new teachers who've learned to "hate standardized testing and at the same time reproduce it faithfully in their own pre-post testing routines" (p. 10) because they've had such little exposure to better forms of assessment.
For the rest of the article, Shepard focuses on how assessment can and should be used to support student learning. First, classrooms need to support a learning culture where "students and teachers would have a shared expectation that finding out what makes sense and what doesn't is a joint and worthwhile project" (p. 10). This means assessment that is more informative and reflective of student learning, one where "students and teachers look to assessment as a source of insight and help instead of an occasion for meting out rewards and punishments" (p. 10). To do this, Shepard describes a set of specific strategies teachers should use in combination in their classrooms.
Dynamic Assessment
When Shepard wrote this article, formal ideas and theories about formative assessment were still emerging and the field had yet to settle on some of the language we now use. But if you're at all familiar with formative assessment, Shepard's description of "dynamic" assessment will sound familiar: teacher-student interactions continuing through the learning process rather than delayed until the end, with the goal of gaining insight about what students understand and can do both on their own and with assistance from classmates or the teacher.Prior Knowledge
The idea of a pre-test to see what students know before instruction begins is not new, but Shepard says we should recognize that traditional pretests don't usually take account of social and cultural contexts. Because students are unfamiliar with a teacher's conceptualization of the content prior to instruction (and vice versa), scores might not accurately reflect students' knowledge as well as, say, a conversation or activity designed to elicit the understandings students bring to the classroom. Also, as Shepard has frequently observed, traditional pre-testing often doesn't significantly affect teachers' instruction. So why do it? Instead, why not focus on building a learning culture of assessment: "What safer time to admit what you don't know than at the start of an instructional activity?" (p. 11)Feedback
The contrast in feedback under old, behaviorist theories and newer, social-constructivist theories is clear. Feedback under old theories generally consisted of labeling answers right or wrong. Feedback under new theories takes greater skill: teachers need to know how to ignore student errors that aren't immediately relevant to the learning at hand, while crafting questions and comments that force the student to question themselves and any false knowledge they might be constructing. (See Lepper, Drake, and O'Donnell-Johnson, 1997, for more on this.)Transfer
While it is our hope that our students will be able to generalize the specific knowledge they have learned and apply it to other situations, our ability to accurately research and make claims about knowledge transfer turns out to be a pretty tricky business. Under a strict behaviorist perspective, it was appropriate to believe that each application of knowledge should be taught separately. Many of our current theories support an idea of transfer, and evidence shows that we can help students by giving them opportunities to see how their knowledge reliably works in multiple applications and contexts. So while some students might not agree, Shepard says teachers should not "agree to a contract with our students which says that the only fair test is one with familiar and well-rehearsed problems" (p. 11).Explicit Criteria
If students are to perform well, they need to have clear guidance about what good performances look like. "In fact, the features of excellent performance should be so transparent that students can learn to evaluate their own work in the same way their teachers would" (p. 11). This reinforces ideas of metacognition and, perhaps more importantly, fairness.Self-Assessment
There are cognitive reasons to have students self-assess, but other goals are to increase student self-responsibility and make teacher-student relationships more collaborative. Students who self-evaluate become more interested in feedback from others, are more aware of standards of excellence, and take more ownership over the learning process.Evaluation of Teaching
This is another idea now heavily intertwined with formative assessment, but Shepard takes it one step farther than I normally see it. Instead of just using assessment to improve one's teaching, Shepard recommends that teachers be transparent about this process and "make their investigations of teaching visible to students, for example, by discussing with them decisions to redirect instruction, stop for a mini-lesson, and so-forth" (p. 12). This, Shepard says, is critical to cultural change in the classroom:If we want to develop a community of learners -- where students naturally seek feedback and critique of their own work -- then it is reasonable that teachers would model this same commitment to using data systematically as it applies to their own role in the teaching and learning process. (p. 12)
Conclusion
Shepard admits that describing this new assessment paradigm is far easier than it is to implement in practice. It relies on a great deal of teacher ability and confronting some long-held beliefs. Shepard recommended a program of research accompanied by a public education campaign to help citizens and policymakers understand the different goals of large-scale and classroom assessments. Neither the research or educating the public is easy, because both are built upon a history of theories and practice that a new paradigm needs to discard. Perhaps we haven't taken on this challenge with the effort and seriousness we've needed, and I worry that now we're more apt to talk about "learning in an assessment culture" rather than the other way around, as Shepard titled this article. I sometimes wonder if she's considered writing a follow-up with that title, or if she's hoping she'll never have to. I guess the next time it comes up I'll have to ask her.Math note: This is an article about assessment and not specific to mathematics, but I'd be remiss if I didn't share Shepard's inclusion of one of my all-time favorite fraction problems:
RYSK: Gravemeijer's Local Instruction Theories as Means of Support for Teachers in Reform Mathematics Education (2004)
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| Gravemeijer (from above) at the 2011 RME Conference |
When Martin Simon introduced the concept of hypothetical learning trajectories in his 1995 paper Reconstructing Mathematics Pedagogy from a Constructivist Perspective, he described them as part of a teaching cycle that was informed by the teacher's knowledge and then revised after assessment of student understanding. While much of the focus was placed on the idea of the trajectory, Simon made clear that no two trajectories will be alike, as each one is hypothesized for a unique group of students who are uniquely constructing knowledge. In other words, you can't just prescribe a trajectory and ask teachers to follow it to the letter. Instead, Simon suggested we needed to build an understanding of the knowledge teachers were using to inform and modify their trajectories:
A possible contribution that can be made by the analysis of data and the resulting model reported in this paper is to encourage other researchers to examine teachers' "theorems in action" and to make teachers' assumptions, beliefs, and emerging theories about teaching explicit. (p. 142)
This paper by Gravemeijer is, in part, a response to Simon's call to other researchers. Gravemeijer first states that in a constructivism-inspired reform mathematics, the traditional goals of instructional design must change:
What is needed for reform mathematics education is a form of instructional design supporting instruction that helps students to develop their current ways of reasoning into more sophisticated ways of mathematical reasoning. For the instructional designer this implies a change in perspective from decomposing ready-made expert knowledge as the starting point for design to imagining students elaborating, refining, and adjusting their current ways of knowing. (p. 106)
Next, Gravemeijer recognizes that while every teacher can use their knowledge to hypothesize a learning trajectory, we (researchers, teacher educators, curriculum designers) need to have some knowledge in common if we want to help teachers:
The example Simon (1995) worked out shows that designing hypothetical learning trajectories for reform mathematics is no easy task. We can, therefore, ask ourselves what kind of support can be given to teachers. It is clear that we cannot rely on fixed, ready-made, instructional sequences, because the teacher will continuously have to adapt to the actual thinking and learning of his or her students. Thus it seems more adequate to offer the teacher some framework of reference, and a set of exemplary instructional activities that can be used as a source of inspiration. (p. 107)
This is where Gravemeijer introduces the concept of a local instruction theory, which he describes as "the description of, and rationale for, the envisioned learning route as it relates to a set of instructional activities for a specific topic" (p. 107). I admit, it's difficult at first to discern this from a hypothetical learning trajectory, but I think the key is the relationship to instructional activities (which are more fixed/solid) instead of a trajectory's relationship to student understanding (which is more flexibile/fluid). By addressing the relationship of learning to the instructional activities, Gravemeijer uses local instruction theories to describe a common foundation teachers can use for building trajectories, saying that "Externally developed local instruction theories are indispensable for reform mathematics education" and that it is "unfair to expect teachers to invent hypothetical learning trajectories without any means of support" (p. 108). (If you're still confused, I think I can safely oversimplify it like this: Simon says trajectories are about student learning, not mathematical tasks. Gravemeijer agrees, but since trajectories are unique because student learning is unique, it helps if we have some agreed-upon ideas about how mathematical tasks should be designed.) Given Gravemeijer's long association with the Freudenthal Institute, he naturally describes how design principles from Realistic Mathematics Education (RME) provide the kind of instructional design framework for creating a local instruction theory.
Design Research and RME
Some curricula and instructional strategies are developed then subjected to treatment and control groups to test their effectiveness. That's not design research and not how RME has been developed. Instead, design research consists of cyclical iterations of thought experiments, teaching experiments, and retrospective analyses. It's similar to how teachers improve their instruction as they gain experience: they plan an activity for year one, then conduct that activity, then reflect on the activity so it will be better in year two. Of course, a team of researchers who are carefully theorizing, observing, collecting data, and analyzing the results across multiple classrooms can more quickly and effectively improve tasks and instruction than a teacher can alone.Gravemeijer describes the design research he conducted with Paul Cobb and others around the development of mental computation strategies for addition and subtraction with elementary students. There are numerous papers and at least part of one dissertation all related to this work, so I won't describe it here. I will, however, describe the three RME design principles that Gravemeijer cites as helping form the local instruction theory that guided the design research process.
Guided Reinvention
Hans Freudenthal (1973) believed mathematics is best learned when students get to experience a process of learning that's similar to the way the mathematics was invented.If mathematics is to be applied, applying mathematics should be taught and learned. Applying is often interpreted, as mentioned above, as substituting numerical values for parameters in general theorems and theories. This is a misleading terminology. Mathematics is applied by creating it anew each time -- I will expound this in more detail too. This activity can never be exercised by learning mathematics as a ready-made product. Drilling algorithms may be indispensable, but inventing problems to drill algorithms does not create opportunities to teach applying mathematics. This so-called applied mathematics lacks the flexibility of good mathematics. (Freudenthal, 1973, p. 118)
I've heard criticisms of this approach. "How in the world can a student reinvent mathematics that took mathematicians hundreds of years to understand?" That's a valid question, and the best answer is: "Through carefully designed curriculum and instruction." The goal is not to replicate the invention of the mathematics, but learn from history how a mathematical idea might be constructed in the mind of a student. Of course, this takes an extensive and special knowledge of the history of mathematics, and largely explains why Freudenthal's Mathematics as an Educational Task is almost 700 pages long.
Didactical Phenomenology
The concept of didactical phenomenology relates the mathematical "thought thing" and the phenomenon it describes. This is not a theory I know well but hope to study more in the future.Mathematical concepts, structures, and ideas serve to organise phenomena -- phenomena from the concrete world as well as from mathematics -- and in the past I have illustrated this by many examples. By means of geometrical figures like triangle, parallelogram, rhombus, or square, one succeeds in organising the world of contour phenomena; numbers organise the phenomenon of quantity. On a higher level the phenomenon of geometrical figure is organised by means of geometrical constructions and proofs, the phenomenon "number" is organised by means of the decimal system. So it goes in mathematics up to the highest levels: continuing abstraction brings similar looking mathematical phenomena under one concept -- group, field, topological space, deduction, induction, and so on. (Freudenthal, 1983, p. 28)
Traditionally we teach an abstract mathematics and then find examples for students to make the mathematics concrete. With didactical phenomenology, we focus on progressive mathematization, suggesting "looking for phenomena that might create opportunities for the learner to constitute the mental object that is being mathematized" (Gravemeijer, p. 116). Yes, it's hard to understand without a lot of specific examples, and that's why Freudenthal wrote almost 600 pages on this topic. It's all in a book I have yet to read, so I'll forgive myself for giving a better description here.
Emergent Modeling
I can best describe emergent modeling with an example. Imagine an elementary class learning about fractions. Instead of giving students a formal model (like a numerator and denominator), the concept of emergent modeling says we should let students reach these models informally and progressively. If a task involves the sharing of parts of cookies with the students, students might begin with breaking apart actual cookies. Once realizing this isn't convenient, students might move to drawing cookies on paper. At some point they'll realize that drawing all the details of the cookie isn't necessary and just use a circle to represent a cookie. Up until this point, these are all models-of a cookie. The key step in this process is when students start using circles to model other contextual situations, like working with fractions of time, money, space, etc. Now the circle is a model-for a part-whole relationship, and not representing a specific object like a cookie. These models-for have the power to generalize to other contexts, and eventually students no longer need the circle and rely on formal mathematics to represent and work with fractions. Gravemeijer describes a similar process in this paper, except with how bead strings, unifix cubes, and rulers can lead to marked and empty number lines as students develop ideas of cardinality, ordinality, and distance as they learn mental strategies for addition and subtraction.Conclusion
I hope by now you have some sense for a local instruction theory. The three RME principles above -- guided reinvention, didactical phenomenology, and emergent modeling -- do not describe a detailed instructional sequence of tasks and instructions for a teacher. They are, however, a way of theorizing how a particular instructional sequence should work, grounded in the design research conducted by Gravemeijer et al. This kind of local instruction theory is what allows teachers to design hypothetical learning trajectories that focus on the construction of student understanding, and provide some common ground for helping teachers become better at trajectory hypothesizing.RYSK: Simon's Reconstructing Mathematics Pedagogy from a Constructivist Perspective (1995)
After reading Clements & Sarama's (2004) Learning Trajectories in Mathematics Education a few days ago, I wanted to go back to the origins of learning trajectories: a 1995 paper from Martin Simon that explored how mathematics can and should be taught differently with a constructivist mindset. Simon is a professor of math education at NYU and has a history of researching how students and their teachers come to understand their mathematical knowledge.
From the start, I immediately appreciated two strengths of this article: Simon's clear writing and the relatively straightforward description of constructivism he offers. When you're still trying to sort out what constructivism is and is not (like me and many classroom teachers), it's a whole lot easier to parse this article from 1995 than, say, a heavily theoretical, mid-2000s piece by Jim Greeno. Recognizing that there are multiple (and often subtly different) ways to describe constructivism, Simon lays out his interpretation like this:
Constructivism derives from a philosophical position that we as human beings have no access to an objective reality, that is, a reality independent of our way of knowing it. Rather, we construct our knowledge of our world from our perceptions and experiences, which are themselves mediated through our previous knowlege. Learning is the process by which human beings adapt to their experiential world. (p. 115)
So when we have an idea that "works," meaning it does what we need it to do to make sense of our experiences, then we've constructed knowledge. This can be a tough sell to the mathematically-minded who say things like, "I didn't construct two plus two equals four. That's an objective fact." In response, the constructivist would disagree about having access to objective reality, but acknowledge that we (almost?) universally construct the knowledge that 2+2=4 because we experience no evidence suggesting otherwise (what Simon and others refer to as disequilibrium).
There's also a theoretical debate about the construction of knowledge as an individual, cognitive process versus a social process. This is an interesting debate, to be sure, and it has been pushing the leading edges of theories for learning mathematics for about the past twenty years. If you're wearing a theoretician hat, then you care deeply about how this debate might be won. But if you're wearing a researcher hat (like Simon is here), then you use both theories to help you gain whatever insights they might afford you. Simon (crediting work by Cobb, Yackel, and Wood) calls this coordination of psychological and sociological approaches "social constructivism," and compares it to how a physicist can better explain the nature of light by considering it both a particle and a wave.
Simon takes pains in this article to separate the theory of constructivism from a notion of "constructivist teaching." It's a mistake that I've seen and heard many times, and it's important to understand the differences and nuances. Simon states:
As I stated above, constructivism, as an epistemological theory, does not define a particular way of teaching. It describes knowledge development whether or not there is a teacher present or teaching is going on. ... There is no simple function that maps teaching methodology onto constructivist principles. A constructivist epistemology does not determine the appropriateness or inappropriateness of teaching strategies. ... The commonly used misnomer, "constructivist teaching," [suggests that] constructivism offers one set notion of how to teach. The question of whether teaching is "constructivist" is not a useful one and diverts attention from the more important question of how effective it is. From a theoretical perspective, the question that needs attention is, In what ways can constructivism contribute to the development of useful theoretical frameworks for mathematics pedagogy? (p. 117)
Using this perspective and a lot of theoretical support from work done in the early 1990s, Simon sets out to explore "the ongoing and inherent challenge to integrate the teacher's goals and direction for learning with the trajectory of students' mathematical thinking and learning" (p. 121, emphasis in original). Unlike a traditional perspective, where the pedagogical focus tended towards chopping mathematical content into manageable pieces to be demonstrated and practiced, Simon wished to focus on student understanding and a plan for mathematical tasks that improved that understanding.
I won't describe Simon's teaching experiment in great detail (after all, it was data-rich enough for Simon to publish multiple papers), but it involved a group of preservice elementary teachers and a set of tasks designed to elicit understandings about how multiplication related to the simple area formula A = l x w. Simon knew that his students had no trouble multiplying or using the formula. That wasn't the problem. Instead, he gave them this task:
Determine how many rectangles, of the size and shape of the rectangle that you were given, could fit on the top surface of your table. Rectangles cannot be overlapped, cannot be cut, nor can they overlap the edges of the table. Be prepared to describe to the class how you solved this problem. (p. 123)
Students used their rectangle (I'm imagining an index card) to measure the length and width of their table. A few groups questioned whether the rectangle should maintain its orientation, or if the long edge should always align with the edge of the table. This launched a class discussion and Simon pushed students to explain how they found the area without defaulting to "I used the formula." Some students talked about rows and columns, some talked about counting rectangles, but comments about "overlapping" rectangles suggested misunderstanding was still apparent. Compounding the problem was that these students were not accustomed to provide this level of justification.
Simon tried varying the task to elicit better student explanations. Some students seemed to get it while others still struggled or remained silent. (The transcript excerpts in Simon's paper are very valuable here, if you can get a copy of it.) Simon began to worry that students were actually misunderstanding things about area, not just how multiplication relates to rectangle area, so he assigned a second task about finding the area of an irregular shape. This was less of a problem for students, so Simon returned to the "turned rectangle" problem and tried another activity with measuring tables, both with rectangular cards and also with sticks. Some students were stuck in their thinking that the area unit must be the size and shape of the card, while others began to see how using the long edge of the card for length and width of the table created new, square units not shaped like the card.
All these classes were observed by researchers who took notes and videotaped the classroom activity. Simon also kept a journal of his reflections after each lesson and planning session. Following the teaching experiment, Simon analyzed his role as the decision maker in the classroom activities. First, he had hypothesized that his students would otherwise be satisfied with knowing and using a formula for area, but had probably never explored why the formula worked. This hypothesis was based on prior experience with similar students, prior research, and pretesting. Simon carefully thought out what he thought would happen in his initial activity, saying this thinking
provides an example of the reflexive relationship between the teacher's design of activities and consideration of the thinking that students might engage in as they participate in those activities. The consideration of the learning goal, the learning activities, and the thinking and learning in which students might engage make up the hypothetical learning trajectory, a key part of the Mathematical Learning Cycle described in the next section. (p. 133)
The "Mathematical Learning Cycle" Simon, in a simplified way, suggests how a teacher's knowledge can be used to create a hypothetical learning trajectory (containing a learning goal, a plan for learning activities, and a hypothesis about the learning process), and how assessment of student knowledge gives the teacher new and better knowledge upon which to refine the hypothetical learning trajectory. (I can't help but wonder if Simon once thought he'd be remembered for "learning cycles," not "learning trajectories.") The trajectory as planned is always hypothetical because it is just the teacher's prediction and the true trajectory cannot be known in advance. Modification of the trajectory happens as the teacher increases his/her knowledge about what students understand, which can be during a planning session between lessons or on-the-fly during a classroom activity. Of course, the more knowledge a teacher has in advance -- about their students, about the mathematical content, and about theories of learning that content -- the better the hypothetical learning trajectory can be. Sometimes we don't have all the information we want, says Simon:
As a teacher, I often do not have a well-developed map of the mathematical conceptual area in which I am engaging my students; that is, I may not have fully articulated for myself (or found in the literature) the specific connections that constitute understanding or the nature of development of understanding in that area. ... Thus, in such cases, my operational definition of understanding is the ability to overcome these particular difficulties; I may not have unpacked the difficulties in order to understand the conceptual issues that are implicated. Thus, even if I do not have a thorough knowledge of what constitutes mathematical understanding in a particular domain, having a rich set of problem situations that challenge students and having knowledge of conceptual difficulties that they typically encounter provide me with an approximation that lets me be reasonably effective in promoting learning in the absence of more elaborated knowledge. (This is not to suggest that the more elaborated understanding would not be more powerful.) (p. 139)
In his summary, Simon reiterates some major themes:
- Student understanding is prioritized in the design of instruction
- Teachers learn as students learn
- Planning instruction includes the creation of a hypothetical learning trajectory
- Because of #2, teachers need to constantly revise #3








