Economics

NCTM Presentation: Integrating Authentic Economics Applications into the Math Classroom

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These are the resources that will be presented on Saturday at 11:00 (room 108 in BCEC).

Handout: Economic Applications NCTM

Slides: NCTM economic applications

Extra Resources:

Create a business

demand

Making Airplanes

Hybrid Microeconomics Part 2 of 3: Early Reflections

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Five week into my hybrid AP Microeconomics course, we had our first major test. Having only met in person four times, I was anxious to see the results. The test was marginally more difficult than the same one given to my “face-to-face” AP Microeconomics course this fall. The results were better than my students from the fall!

My first major realization is that the students are very much actively engaged with the material; there are readings, workbook activities, videos, and online quizzes. In a traditional class, students may have been able to not thoroughly complete their reading or problems, then coast through a 40-minute class. During our in-person sessions, the students have more thoughtful questions because they have confronted the material on their own; they make connections, ask insightful questions, and anticipate concepts.

I used a LMS (Canvas) in the fall with my face-to-face course, but students rarely used the resources- Canvas tracks student hours logged on and it was minimal in the fall. This spring, the online resources have been more robust and students need to take online quizzes, so inevitably the usage will be more, but its not that I notice increased usage, I notice increased engagement. An example is an discussion board thread that was complete (students were asked to define some vocab words and did), then a week later I noticed that it was highlighted as “unread.” When I looked, a handful of students continued the conversation. They were asking about subtleties from the text and how some definitions seeming contradicted each other. Granted this was a subset of the class, but these five students were going beyondFeatured image
their required engagement with the concepts and really wrestling with ideas that can be difficult and deepening their understanding by explaining examples and different ways of thinking about a problem or idea.

So the quantifiable results are at minimum on par (probably better, but small sample size), but the student engagement with Microeconomics is clearly through the roof. These students are becoming active learners; a skill as useful as the economics they are learning.

Hybrid Microeconomics Part 1 of 3: About the Course

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I am teaching a hybrid AP Microeconomics course this spring.  This is the first of three posts describing my experience teaching this course.  The first is the basics about the course.  Attached is a FAQ that I sent to students before the class began.

About the Course: AP Microeconomics

  • This is a one-semester course.
  • It is a hybrid course where students meet once every six school days and complete online readings, activities, and assessments.
  • The goal was to add the opportunity for students to fit the course into their schedule and to add the flexibility to students’ workload.
  • Students are expected to complete about an hour’s worth of work each day (equivalent of 40 minute class plus 20 minute homework).

Resources

  • Students use the Learning Management System Canvas.
  • Students read from Mankiw’s Principles of Economics textbook.
  • Students complete workbook activities from Stone’s AP Microeconomics Resource Manual.

Course Meetings

  • Students meet in small groups in occasionally non-traditional locations (ie. library or breakout room)
  • The schedule was challenging to coordinate everyone’s availability, given that we did not have a dedicated meeting time.

Assignments

  • There are five major resources that we use:
    • Videos (short bursts of information)
    • Textbook (longer and more theoretical)
    • Workbook & Handouts (focusing on mechanics)
    • Class (connecting dots and filling in the blanks)
    • Discussion Board (with threaded answers to questions, both conceptual and vocabulary)

Assessments

  • Students take online quizzes to test for basic comprehension and completion
  • Students take in-person quizzes and tests by scheduling a convenient time.

The Role of Gender and Tracking in Student Achievement at the Extremes

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The following is the abstract of a recent paper I wrote analyzing student performance data. Here is a link to the entire paper.

http://dl.dropbox.com/u/33186846/Student%20Achievement%20at%20Extremes.pdf

Measurement of student achievement is at the heart of educational policy and standardized testing has been both supported and contested as a genuine representation of student achievement. This study shows that the interpretations of standardized testing may have starkly contrasting meanings for different cohorts of students. By using quantile regression to account for conditional PSAT scores, educational factors such as gender and tracking are shown to effect students in varying ways. For the students in this study, gender was a significant indicator of performance on the PSAT test, with being male accounting for more than a five point “bump.” The conclusion from the quantile regressions is that students with extreme PSAT scores are outliers based on ability or inability, not because of their gender. Meanwhile, students that participated in the “honors” tracking system had more of an increase in their predicted score the higher their conditional PSAT score.

The Facebook Bubble

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An economic bubble can be defined as an overvaluation of a product or asset.  In the case of Facebook and its looming IPO (initial public offering) this week, I want to describe three potential bubbles relating to facebook.

First is in the literal sense.  By most estimations, Facebook will have the largest IPO in history: $100 Billion.  Simply, Facebook stock may simply be overvalued.  With an inflated price, a classic bubble burst may be on Facebook’s horizon.  The rationale for the inflated value may be because of two social networking bubbles.

Facebook uses each user’s personal information to sell ads.  That’s how Facebook makes its money.  Their ads are targeted based on users’ likes and clicks; they can sell their ads for a higher price because of the targeted audience.  The bubble occurs as more people become fed up by the exploitation of their personal information.  Once this practice becomes more common knowledge, I believe there will be an exodus from social networking sites that are simply shills for data gathering.

The third potential Facebook bubble is a mass departure by the people who were Facebook’s initial constituents.  Now approaching their thirty-somethings, the college students that Facebook originally targeted may be bored by a decade of the social networking site.  Many adults (mainly forty-plus) are only on Facebook because of their children.  Maybe the hype will catch up with users who are departing their roaring twenties where sharing everything was a way to connect.  Those users are now entering into their adult lives and may no longer feel the need to constantly share or to delve into their friends’ every matter.

Whether Facebook repels users by sharing their information or users finally lose interest in social networking, the company faces many unknowns while it emerges as a public company.  While twenty years from now Facebook may be the largest company in the world, if I had to put money on it, I would bet that we all look back and see Facebook as an another instance of a dot-com bubble.

An Explanation of Conditional Expectations… Part Two

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In my first (found here) of a three part series on statistical analysis, I discussed how data can inform decisions within countless industries.  My research in human capital, and specifically education, provides for various usages of data in decision making.  Specifically, available data can be used for predictions of when, where, and which students may have trouble, determinants of parental satisfaction, admission decisions (at both secondary and collegiate levels), and student achievement both academically, on standardized tests, and in future wages.

This post is intended to shed light on the science of data analysis, specifically conditional expectations.  The mathematical approach to conditional expectation is based on heavy statistical concepts, some of which are not appropriate for this venue.  That being said, my intention is to provide an accessible explanation of the techniques used in statistical analysis.  For a mathematical approach, I recommend the seminal text Econometric Analysis, by William Greene, 2002; or Econometric Analysis of Cross Section and Panel Data, by Jeffrey Wooldridge.

Econometrics, and essentially any data analysis, is based on determining a prediction.  In statistical terms, this is called an expectation.  A conditional expectation is a prediction based on available information.

For instance, consider guessing the height of a random human being.  The average human height is 5 foot 6 inches, so, this would be a logical starting point for our prediction.  But if we know more information about this random person, we can improve our expectation.  Specifically, if we knew the person was male, we would want to change our expectation conditional on that fact.  The average height of an adult man is 5 foot 9.5 inches, so that would be our new prediction given some information.

If we also knew that the person weighed 240 pounds, we may want to increase our expected height.  Note here that there is no causal assumption, just a change in our expectation, given some piece of information.  This is correlation: the taller a person is the more, on average, we expect that person to weigh.  We may predict 6 foot 1 inch for our random male weighing 240 pounds.  Data analysis can help us with our predictions, given some imperfect information.

Next, consider that we also knew this random person’s SAT score was 1200.  We probably would not consider changing our expectation of height.  If we had data on a sample of people with the variables height, sex, weight, and SAT score, some variables may be good predictors of height and be statistically significant while others, SAT score in this example, would not be statistically significant and would not persuade us to change our expectation.

Multiple Regression Analysis essentially considers a sample of data and determines the predictive success of each variable.  Using the information from a statistical data program (even excel can do this reasonably well), we can arrive at a predictive equation for the most logical expectation conditional on the information we have at our disposal.

The data will find the coefficients- the “B’s”- and also determine the likelihood that each “B” is a significant predictor.  In this case, I suspect B3 would not be statistically significant.

In part three, I will discuss an example of student achievement data from a nation-wide sample and how conditional expectations can be used to inform decisions in many fields.

The Rise of Big Data… From Moneyball to the Classroom and Back Again

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This is the first of a three-part series of posts discussing the importance of data analysis and introducing readers to the importance and value in statistical and econometric analysis.

Last Sunday, Steve Lohr wrote a great piece in the New York Times explaining the importance of “big data” in today’s society.  He explained that increasingly “businesses make sense of an explosion of data-  Web traffic and social network comments, as well as software and sensors that monitor shipments, suppliers and customers- to guide decisions, trim costs and lift sales.”

The explosion of data Lohr is referring to is accessible to any field- not just a profit maximizing business- and when used properly, that data can be used to enhance and enrich any institution.  Clearly businesses utilize data to inform their decisions, but increasingly, political campaigns, public health officials and advertising agencies are innovating their traditional practices by developing methods and metrics based on data analysis.

The most glorified example is illustrated in the book and recent film “Moneyball,” written by Michael Lewis describing the revolution in baseball by Billy Beane and the Oakland Athletics.  The short story is that the team began to analyze players using complex statistical analyses instead of traditional benchmarks.  Billy Beane is not the only front office executive to develop and exploit new statistical methods; the general manager of the Houston Rockets, Daryl Morey wrote a piece for Grantland.com regarding the “stats movement in sports” and how the success of Moneyball has transcended sports and become impacted countless industries.

Morey briefly describes how statistical analyses have entered the realm of education: the Gates foundation is gathering data to evaluate teachers.  But Morey and the Gates foundation are only scratching the surface.  Education at all levels is ripe for a takeover of objective data analyses.  Statistics currently used within schools to evaluate programs or students rely on static data.  Static data consists of the most basic statistics we remember from high school: averages and percents.  New data- big data- is about how information, records, numbers move over time and how a fact or figure can be broken down to find relationships and meaning behind the numbers.

Consider a static piece of data such as: In a specific district, 28% of parents are unhappy about their child’s school.  This does not tell an administrator much- probably only something that she already knows.  But, a deeper look into the data could reveal important information such as “of the 28% of parents who are unhappy, 70% of their students play a varsity sport.”  This has more value; specifically, there is a trend among unhappy parents.

The next two posts will dig deeper into the “why” and “how” of how data can be used to improve decision making.  At the most basic level, data can help shape expectations, specifically conditional expectations given some sort of observed trend in the data.  I will explain the concept of conditional expectation in part two.