RYSK: Clements & Sarama's Learning Trajectories in Mathematics Education (2004)

This is the tenth in a series describing "Research You Should Know" (RYSK) and part of my OpenComps.

When I shared my comps reading list with my committee, Bill Penuel quickly replied with the suggestion that I read this article about learning trajectories by Doug Clements and Julie Sarama. I'd seen Clements present on this topic at last year's RME conference (which focused on learning trajectories/progressions), and I recognized the paper as something I found last spring too late in the writing of a final paper to really read and process, so I am happy to return to it now.

At their most basic, learning trajectories can be thought of as sequences of tasks and activities aimed at the progressive development of mathematical thinking and skill. This appeals to me because, quite frankly, I'm not all that great at focusing on single mathematical tasks. Even with a great task, I find myself wondering, "Where in the curriculum does this task fit? What should students know and be able to do before attempting it? Once students complete this task, what new thing are they ready for?" You could say I get a bit distracted in an effort to see the big picture, a habit of mine that's not necessarily new. Learning trajectories are one way of thinking about curriculum on a larger scale, and the better I understand them, the more organized my thinking can be.

Clements finds the roots of learning trajectories in a 1995 paper by Martin Simon titled Reconstructing Mathematics Pedagogy from a Constructivist Perspective (which I should also add to my comps reading list, I'm sure). While it's certainly possible to create a learning trajectory thinking only about instructional tasks, Clements & Sarama stress the interconnections between the instructional sequence and the psychological developmental progression of students. As teachers, sometimes we make the mistake of breaking down an instructional sequence according to the structure of the mathematics, which may or may not reflect the ways students will actually construct their mathematical knowledge. To avoid this mistake, Clements & Sarama suggest designing learning trajectories using this three-stage process:

  1. Specify a research-based learning model that describes how students construct the mathematical knowledge needed for the trajectory. I think this is a tough task for teachers, both because the specific models in the research are not widely known and understood and because there are surely many areas of mathematics for which specific learning models have not been thoroughly studied.
  2. Select key mathematical tasks to promote learning at each level of students' psychological development. Again, it takes the help of research to judge if a task truly targets a certain level of development or not.
  3. Complete the hypothetical learning trajectory by sequence the tasks to match the students' developmental progression.

Of course, the completed learning trajectory should (a) take advantage of specific and relevant cultural knowledge and practices of your students and (b) be subjected to repeated revision and refinement. Clements & Sarama do not understate the potential of well-constructed learning trajectories:

The enactment of an effective, complete learning trajectory can actually alter developmental progressions or expectations previously established by psychological studies because it opens up new paths for learning and development. This, of course, reflects the traditional debate between Vygotsky (1934/1986) and Piaget and Szeminska (1952) regarding the priority of development over learning. We believe that learning trajectory research, along with other research corpi, suggests the Vygotskian position that, at least in some domains and some ways, learning and teaching tasks can change the course of development. (p. 84)

Finally, Clements & Sarama make two more recommendations regarding the creation of learning trajectories. First, be sure to think carefully about how a trajectory might work for an individual student (following a more cognitive theoretical approach) and also how it might work for a class, complete with student interactions and classroom discourse (a more sociocultural theoretical approach). Second, recognize that these trajectories are always hypothetical and will work best when teachers take the time to create and re-create them to work best with their students.

References

Clements, D. H., & Sarama, J. (2004). Learning trajectories in mathematics education. Mathematical Thinking and Learning, 6(2), 81–89. doi:10.1207/s15327833mtl0602_1

Piaget, J. & Szeminska, A. (1952). The child's conception of number. London, UK: Routledge and Kegan Paul.

Simon, M. A. (1995). Reconstructing mathematics pedagogy from a constructivist perspective. Journal for Research in Mathematics Education, 26(2), 114–145. doi:10.2307/749205

Vygotsky, L. S. (1934/1986). Thought and language. Cambridge, MA: MIT Press.

Project OpenComps

This semester I'll be taking my comprehensive exams, or "comps." As a first-generation college student from the working-class rural Midwest, this is pretty unknown territory for me. I remember being a naive undergraduate who had to ask what masters and doctorates were, and when I started my PhD program I had to ask similarly naive questions about the mysterious and vaguely threatening-sounding comps. Quite simply, comps is my opportunity to show a committee of faculty members that I have the knowledge and skills to take on my own research -- namely, my dissertation. Yes, there are written and oral examinations, but it's the process of working with a committee of faculty to both narrow my focus and double-check that I know what I should know that makes the process valuable.

Thankfully, I've been able to watch other graduate students prepare for and take their comps (usually passing, but not always) and now it's time to prepare for mine. I'm going to share that process and preparation with you and tag things #opencomps along the way. You might consider this a step in the direction of something like Hack the Dissertation. I've learned that the entire comps process can vary from program to program, so I can only really describe what it's like for a math education student in CU-Boulder's School of Education. Let's recap how I got this far:

  • With a BA in Mathematics (Teaching) from the University of Northern Iowa and six years teaching high school math, I decided to go to grad school. Having missed the admissions deadline for a master's program, I spent a fall semester as a continuing education student and was admitted into CU-Boulder's master's program for the spring. It went remarkably smoothly, thanks to the help of my advisor David Webb.
  • I expressed an interest in the PhD program and was encouraged to apply. I got recommendations from my current professors and good (enough) GRE scores to be accepted. This meant abandoning the master's program, but thankfully many of the credits I earned transferred to the PhD program.
  • My first year in the PhD program was spent in the "core," the set of six courses every cohort of incoming doctoral students take in the School of Ed. Those courses include two semesters of quantitative methods, two semesters of qualitative methods, a course on theoretical perspectives on social science research, and a course on education research and policy. I took a seventh core course, covering multicultural education, the first semester of my second year.
  • I focused the rest of my second-year coursework on two areas: math education (a course on algebra and a course on theories of mathematical learning) and educational measurement (a course on survey research with an introduction to item response theory, and an advanced measurement course with more IRT and generalizability theory).

I'm required to have 56 hours of coursework (not including dissertation credits) for a PhD. In some programs you need to finish those classes before comps, but in my area it's okay to just be close to 56 so long as the coursework provides the necessary foundation. With over 50 credits under my belt my advisor says I'm ready, so I've taken the first two steps this semester towards comps. First, I needed to choose a committee of three faculty members. My first choice was easy -- my advisor David Webb. I can trust David to make sure I'm ready in the areas of math education and classroom assessment. Also on my committee is Derek Briggs, who will surely hold me to task in the area of quantitative methods, validity, and causal inference. Derek didn't actually teach my core quantitative classes, but I took my measurement courses from him and enjoyed working with him. Due to my wandering interests, the third choice wasn't so easy. (Someone in policy? Qualitative methods? Stats ed? Learning sciences?) I went a bit onto a limb and chose someone I've never taken a class from: Bill Penuel. I got to know Bill a bit last spring during some facilities work, and I'm working for him this semester on a project that combines many of my interests: math ed, professional development, technology, pedagogy, task design, and assessment. I like what I've seen of the project so far and think working more closely with Bill will be a very good thing.

The second step I've taken towards comps this semester was to assemble a reading list. Basically, the reading list contains what I've read for my classes and what I've cited in papers and it gives my committee a place to look for holes in my knowledge. Thanks to Mendeley and careful curation over the past two years, the list wasn't too difficult to assemble. It's long and looking at the 40+ pages of references made me not feel so bad about not reading much over the summer. Take a look at my reading list for yourself, and feel free to ask about anything there, or suggest something you think might interest me!

A First Day Statistics Activity

I have the honor of again teaching our undergraduate statistics course in the School of Education, better known here as EDUC 4716 Basic Statistical Methods. Perhaps the most interesting thing about the course is that it's not required for any education programs, minors, or certificates. Instead, the course attracts students largely from the Department of Speech, Language, and Hearing Sciences (who don't need it to graduate, but do need it to apply to grad school or, more recently, to get certified) and sociology majors. So how does this course end up in the School of Ed? Probably due to the legacy we have in quantitative methods, thanks to people like Robert Linn, Gene Glass, Lorrie Shepard, and now faculty like Derek Briggs and Greg Camilli. Somehow all of their hard work and success filters down and gives a relative stats-hack like me a chance to teach undergrads.

Many of my students are upperclassmen and have spent much of their college experience avoiding math courses. In fact, on last year's FCQ (Faculty Course Questionnaire) my students' average rating for the item "Personal interest in this subject prior to enrollment" was a 1.8 out of 6 -- a response the university tells me is at the 0th percentile across campus. I like to think of this as a great opportunity in a "nowhere to go but up" kind of way, a chance for me to change the way students think of mathematics and see themselves as mathematical beings. Then again, it's hard to make big changes in only 15 class meetings of 2.5 hours each. If I'm going to make a difference, class has to get off to a solid start.

My opening activity this year started with the preparation of four simple index cards with different distribution shapes:
Four common distributions, clockwise from top left: normal, left skewed, right skewed, and normal.

I have the benefit of a small class of 14 students. So I cut my graphs into a total of 14 pieces:

14 pieces for 14 students. Note on the bottom I've provided the hints A, B, C, and D.

When class started, I mixed up the graph pieces and handed one to each student. Then I told the class to find the other people in class who had the graph pieces that aligned with theirs. Once they had a completed graph, form a group at one of the tables and discuss which of the following they thought their group's graph might describe:
  • People born each month of the year
  • Student GPAs at this university
  • Student heights at this university
  • Starting salaries of new graduates from this university
It took my class less than 3-4 minutes to find their groups and then I gave them another 3-4 minutes to discuss what their graph shape might describe. As a class, I had each group share their ideas and then we discussed them. Not everybody agreed initially about which shape matched which description, which led into important comments about how we might think about unbiased sampling of students and imagining different scales and labels along the horizontal axes.

So in less than 15 minutes I combined group-making, statistics, and active, student-centered problem solving into one activity. This activity also gets students thinking about distribution shapes, which I sometimes worry we ignore in the rush to calculate centers and spreads. If you're wondering how to adapt this for your classroom, I offer these suggestions:
  • If you have a few more students, cut more slices.
  • If you have twice as many students, consider making two of each distribution shape and scaling the x-axis to match one of 8 potential descriptions. (i.e., a normal distribution scaled for heights in inches could be distinguished from one scaled for SAT scores.)
  • If you want to use this for Algebra 1, you can make graphs that describe things like, "Toni walked to the bus stop at 2 mph, rode the bus at 30 mph to the bike shop, then rode a bike back home at 12 mph." Such an activity begins CPM's Algebra Connections and was the inspiration for my activity.
  • If you want to use this for Algebra 2 or higher, you can use graphs of functions that students will become familiar with (parabolas, cubics, hyperbolas, etc.). I don't think it's worth fretting over vocabulary at this point -- just give students an opportunity to think about how the functions behave and what phenomena they could possibly model.

Nielsen's Reinventing Discovery (2011) in the Context of Education Research

As a Ph.D. student I've taken my share of methods courses, giving me skills in everything from ethnography to ANOVA. But as important as those things are, I've sensed that there are new research methods emerging thanks to technological advancements and online communities. Our lives are too data-rich and our means of communication are too plentiful to limit ourselves to the same methods for research -- and learning -- that we used just 10 years ago.

Even though I feel like I live in the thick of this revolution, engaging with teachers and researchers on Google+ and Twiter, I wanted a broader perspective on how researchers use networks to make new discoveries. For this I turned to Michael Nielsen's book Reinventing Discovery: The New Era of Networked Science. Although Nielsen is a pioneer in quantum computing, I hoped to find some ideas that I could apply to a social science like education research.

Nielsen uses a variety of examples and concepts to describe what works and what doesn't (or hasn't) in networked science. Instead of listing them here, watch this TEDx talk by Nielsen:


If that talk wasn't long enough for you, Neilsen held a longer talk at Google that is worth checking out.

As much as I like Neilsen's example of Tim Gowers's Polymath Project, I can't imagine a direct translation to education research. One of the beautiful aspects of mathematics is that it usually doesn't require conducting an experiment, interviewing subjects, sampling a population, or agreeing on a conceptual framework -- the kinds of things that make social science untidy and difficult. Frankly, if solving problems in education were structured like proving mathematical theorems, I think we'd be solving more problems and finding better solutions than we are currently.

Neilsen's story about Qwiki hits home for me. For some time now I've imagined creating and maintaining a wiki that essentially translates the contents of the NCTM's Second Handbook of Research on Mathematics Teaching and Learning into knowledge that teachers could access and use. Just like Qwiki, it's easy to get math teachers and educators to agree that this would be a great resource to have. Unfortunately, I'm not sure how a math education wiki like the one I've imagined would avoid Qwiki's fate. Without incentives for experts to contribute and maintain the site, I'd probably spend more time fighting spam than helping teachers.

Neither the Polymath Project or Qwiki offer a blueprint for a new kind of mathematics education research. Thankfully, Nielsen describes some general characteristics for successful networked science. First, in his chapter titled "Restructuring Expert Attention," Nielsen suggests networked science has these attributes:
  • Harnessing Latent Microexpertise -- The project must allow even the narrowest of expertise. A 3rd-year algebra teacher might not have the broad expertise of an experienced math education researcher, but that 3rd year teacher might have small elements of expertise that exceed that of the recognized experts.
  • Designed Serendipity -- The project needs to be easy to follow and encourage participation from a variety of experts. You want problems to be seen by many in the hopes that just a few will think they have a solution they wish to contribute.
  • Conversation Critical Mass -- One person's ideas need to be seen by others so they create more ideas, and the conversation around all the contributions keeps the project going.
  • Amplifying Collective Intelligence -- The project should showcase the fact that collectively we are smarter than any one individual.
Those are all great characteristics of any project. But what makes this any different than any traditional, offline project? Nielsen offers several suggestions. Unlike a large group project with clear divisions of labor, technology allows us to divide labor dynamically. Wikipedia certainly would not have grown the way it did if labor had been divided statically between a set of contributors. Also, networked science uses market forces to direct the most attention to the problems of greatest interest. Lastly, contributing to an online project rarely feels like committee work, and participants can more easily ignore poor contributions or disruptive members.

Projects like Wikipedia and Linux exhibit the above attributes, but Nielsen explains that such projects needed something extra in order to scale to thousands of participants. Nielsen describes these in a chapter called "Patterns of Online Collaboration," and they are: (1) being modular, (2) encouraging small contributions, (3) easy reuse of earlier work, and (4) signaling to what needs attention. When I look at this list and think of Wikipedia, I can see how well a wiki or open source software project fosters these patterns. But how do we build such a project in education? Given Nielsen's framework above, a project that would interest me needs three key aspects:
  • The content of the project has to be something that both teachers and researchers can contribute, such as a collection of math tasks, curriculum plans, or perhaps pedagogical techniques.
  • Teachers need to be able to easily use and modify each other's content.
  • (This one's the crux!) When teachers use content, there needs to be a way to collect and submit feedback about the use of that content, and that feedback becomes data that researchers can use not only to improve the content of the site, but to produce new and traditional reports of research.
It's that last bullet that's the hardest but most intriguing. There are so many places to get lesson ideas on the internet, but I don't know of any that collect data about the effectiveness of the lesson in a format suitable for research. Khan Academy claims to do this this kind of data collection internally, but KA is a closed project that lacks nearly all of the attributes Nielsen has described in his book. The project I want needs to be an open one, with all of its moving parts exposed and no more owned or identified with a single participant as Jimmy Wales is identified with Wikipedia. If you have ideas for what such a project could/should look like, leave them in the comments!

Settling slope and constructive Khan criticism

This was co-written with Frederick Peck, a fellow Ph.D. student in mathematics education at the University of Colorado at Boulder and the Freudenthal Institute US. We each have six years of experience teaching Algebra 1 and are engaged in research on how students understand slope and linear functions. Fred shares his research and curriculum at RMEintheClassroom.com.



Sal Khan (CC BY-NC-ND Elvin Wong)
The Answer Sheet has recently been the focus of a lively debate pitting teacher and guest blogger Karim Kai Ani against the Khan Academy's Salman Khan. While Karim's initial post focused mainly on Sal Khan's pedagogical approach, Karim also took issue with the accuracy of Khan Academy videos. As an example, he pointed to the video on slope. Specifically, Karim claimed Sal's definition of slope as "rise over run" was a way to calculate slope, but wasn't, itself, a definition of slope. Rather, Karim argued, slope should be defined as "a rate that describes how two variables change in relation to one another." Sal promptly responded, saying Karim was incorrect, and that "slope actually is defined as change in y over change in x (or rise over run)." To bolster his case Sal referenced Wolfram Mathworld, and he encouraged Valerie Strauss to "seek out an impartial math professor" to help settle the debate. We believe that a better way to settle this would be to consult the published work of experts on slope.

Working on her dissertation in the mid-1990s, Sheryl Stump (now the Department Chairperson and a Professor of Mathematical Sciences at Ball State University) did some of the best work to date about how we define and conceive of slope. Stump (1999) found seven ways to interpret slope, including: (1) Geometric ratio, such as "rise over run" on a graph; (2) Algebraic ratio, such as "change in y over change in x"; (3) Physical property, referring to steepness; (4) Functional property, referring to the rate of change between two variables; (5) Parametric coefficient, referring to the "m" in the common equation for a line y=mx+b; (6) Trigonometric, as in the tangent of the angle of inclination; and finally (7) a Calculus conception, as in a derivative.

(CC BY-NC-SA Raymond Johnson)
If you compare Karim and Sal's definitions to Stump's list, you'll likely judge that while both have been correct, neither have been complete. We could stop here and declare this duel a draw, but to do so would foolishly ignore that there is much more to teaching and learning mathematics than knowing what belongs in a textbook glossary. Indeed, research suggests that a robust understanding of slope requires (a) the versatility of knowing all seven interpretations (although only the first five would be appropriate for a beginning algebra student); (b) the flexibility that comes from understanding the logical connections between the interpretations; and (c) the adaptability of knowing which interpretation best applies to a particular problem.

All seven slope interpretations are closely related and together create a cohesive whole. The problem is, it's not immediately obvious why this should be so, especially to a student who is learning about slope. For example, if slope is steepness, then why would we multiply it by x and add the y-intercept to find a y-value (i.e., as in the equation y=mx+b)? And why does "rise over run" give us steepness anyway? Indeed, is "rise over run" even a number? Students with a robust understanding of slope can answer these questions. However, Stump and others have shown that many students -- even those who have memorized definitions and algorithms -- cannot.

(CC BY Amber Rae)
This returns us to Karim's original point: There exists better mathematics education than what we currently find in the Khan Academy. Such an education would teach slope through guided problem solving and be focused on the key concept of rate of change. These practices are recommended by researchers and organizations such as the NCTM, and lend credence to Karim's argument for conceptualizing slope primarily as a rate. However, even within this best practice, there is nuance. For instance, researchers have devoted considerable effort to understanding how students construct the concept of rate of change, and they have found, for example, that certain problem contexts elicit this understanding better than others.

Despite all we know from research, we should not be surprised that there's still no clear "right way" to teach slope. Mathematics is complicated. Teaching and learning is complicated. We should never think there will ever be a "one-size-fits-all" approach. Instead, educators should learn from research and adapt it to fit their own unique situations. When Karim described teachers on Twitter debating "whether slope should always have units," we see the kind of incremental learning and adapting that moves math education forward. These conversations become difficult when Sal declares in his rebuttal video that "it's actually ridiculous to say that slope always requires units*" and Karim's math to be "very, very, very wrong." We absolutely believe that being correct (when possible) is important, but we need to focus less on trying to win a mathematical debate and focus more on the kinds of thoughtful, challenging, and nuanced conversations that help educators understand a concept well enough to develop better curriculum and pedagogy for their students.

Khan Academy (CC BY-NC-ND Juan Tan Kwon)
This kind of hard work requires careful consideration and an open conversation, even for a seemingly simple concept like slope. We encourage Sal to foster this conversation and build upon what appears to be a growing effort to make Khan Academy better. Doing so will require more than rebuttal videos that re-focus on algorithms and definitions. It will require more than teachers' snarky critiques of such videos. Let's find and encourage more ways to include people with expertise in the practice and theories of teaching mathematics, including everyone from researchers who devote their lives to understanding the nuance in learning to the "Twitter teachers" from Karim's post who engage this research and put it into practice. This is how good curriculum and pedagogy is developed, and it's the sort of work that we hope to see Sal Khan embrace in the future.



*Sal's point is that if two quantities are both measured in the same units, then the units "cancel" when the quantities are divided to find slope. As an example, he uses the case of vertical and horizontal distance, both measured in meters. The slope then has units of meters/meters, which "cancel". However, the situation is not so cut and dry, and indeed, has been considered by math educators before. For example, Judith Schwartz (1988) describes how units of lb/lb might still be a meaningful unit. Our point is not to say that one side is correct. Rather, we believe that the act of engaging in and understanding the debate is what is important, and that such a debate is cut short by declarative statements of "the right answer."

References

Schwartz, J. (1988). Intensive quantity and referent transforming arithmetic operations. In J. Heibert & M. J. Behr (Eds.), Number Concepts and Operations in the Middle Grades (Vol. 2, pp. 41–52). Reston, VA: National Council of Teachers of Mathematics.

Stump, S. L. (1999). Secondary mathematics teachers' knowledge of slope. Mathematics Education Research Journal, 11(2), 124–144. Retrieved from http://www.springerlink.com/index/R422558466765681.pdf