Gamification and Web-based Homework

Every so often, I’ll publicize through this blog an interesting article that I’ve found in the mathematics or mathematics education literature that can be freely distributed to the general public. Today, I’d like to highlight “Gamification and Web-based Homework,” by Geoff Goehle. Here’s the abstract:

In this paper we demonstrate how video game mechanics can be used to help improve student engagement with online mathematics homework. Specifically, we integrate two common video game systems, levels and achievements, with the online homework program WeBWorK. We describe the key features of the implementation of these systems and discuss how students responded after they were used in a calculus class.

The full article can be found here:

Full reference: Geoff Goehle (2013) Gamification and Web-based Homework, PRIMUS: Problems, Resources, and Issues in Mathematics Undergraduate Studies, 23:3, 234-246, DOI: 10.1080/10511970.2012.736451

The Fundamental Theorem of Algebra: A Visual Approach

A former student forwarded to me the following article concerning a visual way of understanding the Fundamental Theorem of Algebra, which dictates that every nonconstant polynomial has at least one complex root: The paper uses a very clever idea, from the opening paragraphs:

[I]f we want to use pictures to display the behavior of polynomials defined on the complex numbers, we are immediately faced with a difficulty: the complex numbers are two-dimensional, so it appears that a graph of a complex-valued function on the complex numbers will require four dimensions. Our solution to this problem will be to use color to represent some dimensions. We begin by assigning a color to every number in the complex plane… so a complex number can be uniquely specified by giving its color.

We can now use this color scheme to draw a picture of a function f : \mathbb{C} \to \mathbb{C} as follows: we simply color each point z in the complex plane with the color corresponding to the value of f(z). From such a picture, we can read off the value of f(z)… by determining the color of the point z in the picture…

The article is engagingly written; I recommend it highly.


Laverne and Shirley

In class one morning, I was quickly counting out the number of digits of a decimal expansion that was on the board: “One, two, three, four, five, six, seven, eight.” Struck by sudden inspiration, I continued, “Sclemeel, schlemazel, hasenfeffer incorporated.”

Sadly, only one student (unsurprisingly, a non-traditional student) laughed. It took me a minute to realize that not only are my college students too young to remember “Laverne and Shirley,” they’re also too young to remember when Wayne and Garth paid homage to “Laverne and Shirley” in their 1992 movie.

Schoolhouse Rock and Calculus

After presenting the Fundamental Theorem of Calculus to my calculus students, I make a point of doing the following example in class:

\displaystyle \int_0^4 \frac{1}{4} x^2 \, dx

Hopefully my students are able to produce the correct answer:

\displaystyle \int_0^4 \frac{1}{4} x^2 \, dx = \displaystyle \left[ \frac{x^3}{12} \right]^4_0

= \displaystyle \frac{(4)^3}{12} - \frac{(0)^3}{12}

= \displaystyle \frac{64}{12}

= \displaystyle \frac{16}{3}

Then I tell my students that they’ve probably known the solution of this one since they were kids… and I show them the classic video “Unpack Your Adjectives” from Schoolhouse Rock. They’ll watch this video with no small amount of confusion (“How is this possibly connected to calculus?”)… until I reach the 1:15 mark of the video below, when I’ll pause and discuss this children’s cartoon. This never fails to get an enthusiastic response from my students.

If you have no idea what I’m talking about, be sure to watch the first 75 seconds of the video below. I think you’ll be amused.

Inverse Functions: Index

I’m doing something that I should have done a long time ago: collect past series of posts into a single, easy-to-reference post. The following posts formed my series on the different definitions on inverse functions that appear in Precalculus and Calculus.

Square Roots, nth Roots, and Rational Exponents

Part 1: Simplifying \sqrt{x^2}

Part 2: The difference between \sqrt{t} and solving x^2 = t

Part 3: Definition of an inverse function and the horizontal line test

Part 4: Why extraneous solutions may occur when solving algebra problems involving a square root

Part 5: Defining \sqrt{x}

Part 6: Consequences of the definition of \sqrt{x}: simplifying \sqrt{x^2}

Part 7: Defining \sqrt[n]{x} if n is odd or even

Part 8: Rational exponents if the denominator of the exponent is odd or even


Part 9: There are infinitely many solutions to \sin x = 0.8

Part 10: Defining arcsine with domain [-\pi/2,\pi/2]

Part 11: Pedagogical thoughts on teaching arcsine.

Part 12: Solving SSA triangles: impossible case

Part 13: Solving SSA triangles: one way of getting a unique solution

Part 14: Solving SSA triangles: another way of getting a unique solution

Part 15: Solving SSA triangles: continuation of Part 14

Part 16: Solving SSA triangles: ambiguous case of two solutions

Part 17: Summary of rules for solving SSA triangles


Part 18: Definition for arccosine with domain [0,\pi]

Part 19: The Law of Cosines and solving SSS triangles

Part 20: Identifying impossible triangles with the Law of Cosines

Part 21: The Law of Cosines provides an unambiguous angle, unlike the Law of Sines

Part 22: Finding the angle between two vectors

Part 23: A proof for why the formula in Part 22 works



Part 18: Definition for arctangent with domain (-\pi/2,\pi/2)

Part 24: Finding the angle between two lines

Part 25: A proof for why the formula in Part 24 works.


Part 26: Defining arcsecant using [0,\pi/2) \cup (\pi/2,\pi]

Part 27: Issues that arise in calculus using the domain [0,\pi/2) \cup (\pi/2,\pi]

Part 28: More issues that arise in calculus using the domain [0,\pi/2) \cup (\pi/2,\pi]

Part 29: Defining arcsecant using [0,\pi/2) \cup [pi,3\pi/2)


Part 30: Logarithms and complex numbers




A nice news article on Bayesian statistics

The New York Times consistently provides the best coverage of mathematics and science by a traditional news outlet. Today, I’d like to feature their article The Odds, Updated Continually, which gives a nice synopsis of the growth of Bayesian statistics in recent years and how Bayesian statistics differs from the frequentist interpretation of statistics. For example:

Statistics may not sound like the most heroic of pursuits. But if not for statisticians, a Long Island fisherman might have died in the Atlantic Ocean after falling off his boat early one morning last summer.

The man owes his life to a once obscure field known as Bayesian statistics — a set of mathematical rules for using new data to continuously update beliefs or existing knowledge…

The essence of the frequentist technique is to apply probability to data. If you suspect your friend has a weighted coin, for example, and you observe that it came up heads nine times out of 10, a frequentist would calculate the probability of getting such a result with an unweighted coin. The answer (about 1 percent) is not a direct measure of the probability that the coin is weighted; it’s a measure of how improbable the nine-in-10 result is — a piece of information that can be useful in investigating your suspicion.

By contrast, Bayesian calculations go straight for the probability of the hypothesis, factoring in not just the data from the coin-toss experiment but any other relevant information — including whether you’ve previously seen your friend use a weighted coin.

Scientists who have learned Bayesian statistics often marvel that it propels them through a different kind of scientific reasoning than they’d experienced using classical methods.

“Statistics sounds like this dry, technical subject, but it draws on deep philosophical debates about the nature of reality,” said the Princeton University astrophysicist Edwin Turner, who has witnessed a widespread conversion to Bayesian thinking in his field over the last 15 years…

The Coast Guard has been using Bayesian analysis since the 1970s. The approach lends itself well to problems like searches, which involve a single incident and many different kinds of relevant data, said Lawrence Stone, a statistician for Metron, a scientific consulting firm in Reston, Va., that works with the Coast Guard.

At first, all the Coast Guard knew about the fisherman was that he fell off his boat sometime from 9 p.m. on July 24 to 6 the next morning. The sparse information went into a program called Sarops, for Search and Rescue Optimal Planning System. Over the next few hours, searchers added new information — on prevailing currents, places the search helicopters had already flown and some additional clues found by the boat’s captain.

The system couldn’t deduce exactly where Mr. Aldridge was drifting, but with more information, it continued to narrow down the most promising places to search.

Just before turning back to refuel, a searcher in a helicopter spotted a man clinging to two buoys he had tied together. He had been in the water for 12 hours; he was hypothermic and sunburned but alive.

Even in the jaded 21st century, it was considered something of a miracle.