How We Learn Mathematics

I was reading the following paper: D. Breidenbach, E. Dubinsky, J. Hawks, and D. Nichols, "Development of the Process Conception of Function," Educational Studies in Mathematics, 23: 247-285, 1992.

Quote Dubinsky (1989): "A person's mathematical knowledge is her or his tendency to respond to certain kinds of perceived problem situations by constructing, reconstructing and organizing mental processes and objects to use in dealing with the situations."

"Applying this point of view to mathematics (or any other subject) consists of determining the nature of the specific processes and objects that are constructed and how they are organized when one studies mathematics"

Ways of thinking about functions:

• prefunction - does not understand any real ways of using function concepts
• action - repeatable mental or physical manipulation (e.g., plug in numbers and calculate); static; one step at a time
• process - think of function as a single dynamic transformation

I then found another article: A. Sfard and L. Linchevski, "The Gains and the Pitfalls of Reification: The Case of Algebra," Educational Studies in Mathematics, 26 (2/3), 191-228, 1994 [Learning Mathematics: Constructivist and Interactionist Theories of Mathematical Development]

This article proceeds with the view that in mathematics, there is a duality in mathematical constructs being a process or an object. That is, conceive of things operationally (process) or structurally (object). Historical examples include the expansion of number systems: positive to negative (operational: subtraction as adding a negative to structural: negative numbers as objects), and real to complex (i=sqrt(-1) as an operational convenience to an actual object)

An included reference suggests finding another article: Kieran, C.: 1992, 'The learning and teaching of school algebra', in D. A. Grouws (ed.), The Handbook of Research on Mathematics Teaching and Learning, Macmillan, New York, pp. 390-419. I'll have to see if I can find this one, as it is cited for the sentence, "[reification] was also used to introduce some order into the quickly growing bulk of findings about algebraic thinking."

Interesting phrase: "the ability to grasp the structural aspect is not easy to achieve" and "those crucial junctions in the development of mathematics where a transition from one level to another takes place are the most problematic."

Another interesting way to think about how mathematics is organized: (1) Logical, or the way it fits together; (2) Historical, or the way in which it was developed; and (3) Cognitive, or the processes in which people learn.

Modes of Algebra
1.1) Algebra as Generalized Arithmetic: The Operational Phase
-- solve for the unknown, but not using symbols (grade school algebraic thinking)
-- rhetoric algebra
-- principally reversing processes
1.2) Algebra as Generalized Arithmetic: The Structural Phase
1.2.1) algebra of a fixed value (unknown)
-- Notational convenience, but treat variable as a fixed value
:::: becomes a mental challenge to think of formula as both a process and result
:::: example given: 2+3 represents process, 5 represents result. But x+3 represents both, no separate "result"
:::: compare to the challenges of new number types required to think about division, subtraction, and extracting square roots
** Nice comment: "Once we manage to overcome this difficulty, it is quickly forgotten. ... Our eyes are easily blinded by habit and by our own ontological beliefs. Nevertheless, much evidence for the difficulty of reification may also be found in today's classroom, provided those who listen to the students are open-minded enough to grasp the ontological gap between themselves and the less experienced learners."
1.2.2) Functional algebra (of a variable)
:::: View formula as object
:::: Parameters represented as symbols not numbers.
2) Abstract Algebra

Give examples of interview questions. Students at early stages of thinking think about formulas as recipes for computations (process) but do not perceive them as valid objects. "The equality sign is interpreted as a 'do something signal' (Behr et al 1976; Kieran 1981)"

Here's something I see all the time in calculus classes: "It [the = symbol] serves here as a 'run' command. When treated in this way, the equality symbol looses [sic] the basic characteristics of an equivalence predicate: it stops being symmetrical or transitive. Indeed, young children seem to have no qualms about solving word problems with the help of a chain of non-transitive equalities. For instance, when asked 'How many marbles do you have after you win 4 marbles 3 times and 2 marbles 5 times?', the child would often write: 3*4=12+5*2=12+10=22."

Equations of the form 2x-3 = 11 can be interpreted as a formula whose result is 11 (which can be solved by inverse operations); equations of the form 2x-3=5x-9 appear to be two different formulas, and inverse operations do not make sense.

Graph from a graph of f ' (x)

First, pay attention: the graph provided on the assignment is the graph of the derivative f '(x) and not the graph of f. So you can't look at the picture and say that because the graph you are looking at is increasing that f '(x) is positive; if the graph is increasing, then that means f '(x) is increasing, and not f(x). (This is useful information, but you just need to think about what it does say.)

Second, the number line sign analysis summaries will help identify the shape of the graph. Imagine taking the unit circle and breaking it up according to quadrants. The signs of f '(x) and f ''(x) determine which of these four basic shapes the graph is most like.

• f '(x) = + and f ''(x) = + means f(x) looks like Quadrant IV (incr, conc. up)
  
• f '(x) = - and f ''(x) = + means f(x) looks like Quadrant III (decr, conc. up)
• f '(x) = + and f ''(x) = - means f(x) looks like Quadrant II (incr, conc. down)
• f '(x) = - and f ''(x) = - means f(x) looks like Quadrant I (decr, conc. down)

The graph is just formed by taking these shapes and putting them end-to-end. You wouldn't actually use the entire portion of the unit circle because we probably don't want vertical tangents like the unit circle has. The circle just helps us remember the basic shape. The points where we join the shapes together will probably be inflection points (concavity changes) or extreme values.

However, sign analysis does not tell us the heights of any points. The problem gives only one point: f(0) = 1. The rest of the points of interest (especially the local extreme values) can be found by thinking about the information relating to the areas of the graph of f '(x). (Again, think about the Fundamental Theorem of Calculus).

Sums of Geometric Sequences

The second problem on the project introduces a new closed form for a sum:

∑_k=1ⁿ [A ρ^k] = A ρ (ρⁿ-1)/(ρ-1).

Unfortunately, too many of you are still intimidated simply by the symbols that are used.

The formula for the geometric sequence, A ρ^k, is like an exponential, except the power is an integer variable rather than a continuous variable like x. For example, if A=2 and ρ=1/3, we have terms that are increasing powers of (1/3) times 2:
2/3 (k=1), 2/9 (k=2), 2/27 (k=3), 2/81 (k=4), etc.

The summation is simply the sum of these values:
∑_k=1ⁿ [2 (1/3)^k] = 2/3 + 2/9 + 2/27 + 2/81 + ... + 2/3ⁿ.
The closed form gives a formula answering the value of this sum:
2(1/3)[(1/3)ⁿ-1]/[(1/3) - 1] = (2/3)*[(1/3)ⁿ-1]/(-2/3) = 1 - (1/3)ⁿ.

So when you write down the Riemann sum for the integral in question, you need to look at using the properties of exponentials so that the Riemann sum looks just like a sum of a geometric sequence. You should identify a factor that does not involve k, and this is A. You should identify the other factor as some number raised to the k power. Then you can use the closed form.

Populations, Birth Rates, and Death Rates

This entry is a general assist for my class working on a project. Suppose you knew the rate at which births are occurring (call it a function of time, b(t)) and you knew the rate at which deaths are occurring (a function d(t)). If the only way the population changes is through births and deaths, then if P(t) is the function describing the size of the population in time, then P'(t) = b(t) - d(t). (It is still your job to explain why this makes biological sense.)

Okay, now for the general principle. Anytime you know the rate of change of a quantity, you can always get back to the original quantity through a definite integral (assuming the rate of change is continuous, anyway). This is the heart of the 2nd Fundamental Theorem of Calculus. Not using P and t as variables (so that you have at least something to translate), here is the basic idea.

Suppose you know f '(x). Then A(x) = ∫₀^x f '(z) dz is an antiderivative of f '. But so is f(x) since that is where f '(x) comes from. That is f(x) = A(x) + C for some constant. In particular, A(0) = 0, so C=f(0). That is, f(x) = f(0) + ∫₀^x f '(z) dz.

This will always work, even if I don't start the integral at 0: f(x) = f(a) + ∫_a^x f '(z) dz. Written another way, it looks like the first Fundamental Theorem of Calculus: f(x) - f(a) = ∫_a^x f '(z) dz.
In other words, the 2nd FTC implies that every function is its starting value plus the integral of its rate of change.

Now, for our population problem, we don't actually know the rate of change completely; we only know the value at specific points. So instead of computing an integral (to get an exact value), we will approximate the integral using a Riemann sum. We are restricted to using the table data, so Δt=2 is forced upon us. For example, ∫₀² b(t) dt can only be estimated with a single rectangle while ∫₀⁴ b(t) dt would involve two rectangles. The idea of the Riemann sum is that we choose b(t_k*) as one of our data points (either on the left or right).

More specifically, on the interval [0,2] (k=1), we can either use t₁*=0 so that b(t₁*)=100 or use t₁*=2 so that b(t₁*)=135. In the first case, the rectangle for k=1 contributes b(t₁*)Δt = 200, while the second case leads to a contribution of b(t₁*)Δt = 270. The average (midpoint) of these two values (200+270)/2 = 235 is the estimate that would come from using the trapezoid sum. We do this for each of the 8 intervals between our data points, for both the births and the deaths.

By considering our estimates for the number of births and deaths in each of the intervals ([0,2], [2,4], [4,6], etc.), we can produce an estimate of the new population at each of the times (2, 4, 6, 8, etc.). By thinking about what estimates lead to the largest predicted population, we get an upper limit (i.e. bound) for our estimate --- no population consistent with this data can ever go above that value. Similarly, we can choose those estimates to create a lower bound. The true population will be somewhere in between.

Exponential Functions

I'm getting feedback that exponential functions are giving you extra trouble. I'd appreciate getting feedback to help know how I can clarify the concepts. Here is a summary of some of the key concepts that I'm wanting you to understand:

• b^x is not just a formula "b to the power x" but is a new function, which I'm asking you to call exp_b(x).
  
• The properties of exponents like b^x+y=b^x b^y and (b^x)^y=b^xy become properties of the exponential functions.
  
  exp_b(x+y)=exp_b(x)*exp_b(y)
  exp_b(xy)= [exp_b(x)]^y


• Logarithms are the inverse functions of exponential functions.
  
  exp_b(log_b(x))=x
  log_b(exp_b(x))=x
  
  In formula representation, these are written as follows:
  
  b^log_b(x)=x
  log_b(b^x)=x
• Whenever you see a formula with an exponential, say b^3x-2, you should be able to think in both formula and function modes interchangeably.
  
  b^3x-2 = b^3x b^-2 = (b³)^x b^-2
  b^3x-2 = exp_b(3x-2)
  The first mode allows us to recognize that b^3x-2t is actually of the form Aq^x where A=b^-2 and q=b³. The second mode is useful to remind us that we really have a composition when we need to compute a derivative or when dealing with inverse functions.
• There is a special base e that is most important because the corresponding exponential function is its own derivative.
  
  exp'_e(x)=exp_e(x)
  
  d/dx[e^x]=e^x
  
  The natural exponential is written without a base. The corresponding inverse function is called the natural logarithm, ln(x).
  
  exp(ln(x))=x
  ln(exp(x))=x
  
  In formula representation, these are written as follows:
  
  e^ln(x)=x
  ln(e^x)=x
  The x in these formulas, as always, is a placeholder. So any number or formula could be used in place of x.
• Any exponential can be written in terms of the natural exponential. The key is to use the properties of exponents.
  
  b = e^ln(b)
  b^x = [e^ln(b)]^x = e^{ln(b) x}
  
  Another way to think of this is using composition of inverse functions: exp(ln( ))
  
  b^x = exp(ln(b^x))
  ln(b^x) = x ln(b)
  b^x = exp(x ln(b)) = e^{x ln(b)}
  
  That is, by replacing b^x by e^{ln(b) x}, we can write any exponential A b^x in the form A e^kx where k is the number ln(b).
• Derivatives of exponentials use the basic property that exp'(x) = exp(x), or d/dx[e^x] = e^x. Usually, this also requires the chain rule: d/dx[e^u] = e^u u'.
  
  d/dx[e^2x] = e^2x (2) = 2e^2x (u = 2x)
  d/dx[e^-3x] = e^-3x (-3) = -3e^-3x (u = -3x)
  d/dt[e^(t2-5t)] = e^(t2-5t) (2t-5) = (2t-5)e^(t2-5t) (u = t²-5t)
  

So this was a moderately long list. Perhaps just re-reading it helped you understand something better. Or perhaps you realize something is still confusing. Please post comments explaining why you are finding problems challenging or perhaps explaining what helped you suddenly understand what you had missed earlier.

Concerns about Derivatives

Okay, I have just finished grading HW #4. This assignment had some differentiation rules. But many of you are not comprehending the purpose of a derivative rule.

For example, we had the special rule for squares of functions:

d/dx[f²(x)] = 2 f(x) f '(x)

This means that any time there is a formula squared and you need to take its derivative, you can apply this rule.

(2x+3)² corresponds to the function f(x)=2x+3 being squared. So since f '(x) = 2:

d/dx[(2x+3)²] = 2 (2x+3) (2) = 4(2x+3)

Similarly, (x²-4x+5)² corresponds to the function f(x) = x²-4x+5 being squared, and f '(x)=2x-4

d/dx[(x²-4x+5)²] = 2 (x²-4x+5) (2x-4)

You need to be an expert at identifying the form of an expression in order to apply appropriate rules of differentiation, and next semester, rules of integration (anti-differentiation).

Science and Mathematics

The other day, I had my students respond to a question about how mathematics relates to science.

In class, I had pointed out that mathematical definitions are very precise while scientific measurements can be rather messy. A mathematician has a very precise meaning when they say that two variables are proportional or have a linear relation. But when we get real data, even if they do not satisfy these precise meanings, we still gain significant information about the relation and might even say that the measured variables are proportional or linear. Unfortunately, many students seemed to think I was looking for a repeated discussion of this point.

Science can be thought of as the study of the physical world through the scientific method. Essentially, we make observations on what happens in the world (whether that be physical, chemical or biological interactions) and want to understand why that is happening as well as to predict what will happen in the future. In order to do this, scientists propose various hypotheses based on their observations (and past accumulated scientific experience) and then test those hypotheses. Experiments quite often include quantitative measurements, and part of the prediction is often to propose relationships between independent variables (the variables in treatments and control) and the dependent variables (outcomes). Experience may support a hypothesis or falsify the hypothesis, but it never can prove a hypothesis.

Knowledge based on patterns that we predict will continue, but which we can support but never prove, is called inductive knowledge. Science is an example of inductive knowledge.

Mathematics can be thought of as the study of structures that satisfy very specific rules. We have properties of arithmetic, algebra, and calculus. We establish specific axioms that describe our basic assumptions about the structures and then use logical argument to deduce the behavior of more complicated constructions. We might look at examples to see what ideas might be true or false, and in this sense mathematics can also take advantage of inductive knowledge. However, the objective in mathematics is not just to suppose that a pattern will continue; the objective is to determine conclusively if it must continue. We seek for proofs (that it is true) or counterexamples (break the pattern).

Knowledge based on basic assumptions (axioms) and logical argument that determines conclusively what must follow from these assumptions is called deductive knowledge. mathematics is an example of deductive knowledge.

Models form a connection between mathematics and science. Data often appear to follow a general trend, even in the presence of the noise of messy observation. A mathematical model takes that messiness and forms an abstract clean relationship that mathematics can work with. Based on the deductive approach of mathematics, we can often establish consequences of the assumed model form. We then apply those consequences as hypotheses in our scientific framework. The predictions from the deductive approach provide the predictions that can be used to falsify these hypotheses.