Glutton for punishment

OK, someone really needs to save me from myself... Just to make life more interesting, I've decided that it would be cool to teach this new data course using team-based learning (TBL). For those not familiar with TBL, the basic idea is that students are organized into 5-7 person teams that stay together through the entire semester and then as much of the course as possible is built around team activities. So now, on top of trying to figure out the basic content and topics for a course I've never taught before, I'm also trying to figure out how to structure assignments and assessments using a method I've never used before.* Fun!

Actually, it's not quite as crazy as it sort of seems (at least, that's what I keep telling myself) - designing a team-based learning course basically requires backward design, which I was doing anyway, and in a TBL course, the instructor doesn't really do traditional lectures, which I really don't want to do. Instead, students acquire a lot of the basic content knowledge outside of class and then classes are built around simple assessments of that basic knowledge (to make sure students are really prepared), followed by application exercises. Since this is a course where the students are supposed to have already acquired much of the basic knowledge previously anyway (i.e., a lower-division stats class is a prerequisite), and the point is for students to get experience using that knowledge in the ways that economists do, I think the TBL structure actually is a good fit. Instead of me talking, students will spend almost all of the class time doing. Plus, the teams will mean I can give more complex assignments without a corresponding increase in time spent grading (one section will have 70 students and the other will have 60 - yes, I know that's totally nuts). I've still got a ton of things to figure out, and I have serious concerns about how this is going to go over with the students, but as I have been thinking about the types of activities I want students to do and how these will work in groups, I have to say that I'm pretty excited!

* I blame Bill Goffe for this - he mentioned TBL to me in an email a few months ago and then recently posted this link on the tch-econ listserv, showing TBL in action in a large lecture. If you're not familiar with the technique, I highly recommend checking out the video and the website.

Economics Education sessions at the Westerns

For those who will be in Portland, I went through the program and tried to find the econ ed sessions. Also, as the Western regional rep for CSWEP, I'll be 'hosting' the CSWEP breakfast on Thursday, July 1, 7-9am, so feel free to stop by and say hi!


Wednesday, June 30, 12:30–2:15 p.m.
CREATING UNDERGRADUATE RESEARCH AND FIELD EXPERIENCES (ROUNDTABLE)
Organizer: Melanie Fox Kean, Austin College, and Eric Dodge, Hanover College
Chair: Melanie Fox Kean, Austin College, and Eric Dodge, Hanover College
Papers: Elizabeth Perry-Sizemore, Randolph College, Mary O. Borg, University of North Florida, Stephen B. DeLoach, Elon University, and Sheila Kennison, Oklahoma State University
Creating Quality Undergraduate Research Programs in Economics
C. Beth Haynes, Brigham Young University Hawaii
One for the Money, Two for the 'Know,' Three to Get Ready, and Four to Go: Facilitating Undergraduate Research in Developing Countries
Eric Dodge, Hanover College
Dam Economics: A Dam Good Field Experience in the Pacific Northwest
Melanie Fox Kean, Austin College
Undergraduate Research Conferences as a Model for Fostering Research at All Phases of Student Learning


June 30, 2:30-4:15pm
TEACHING MONETARY POLICY: RESOURCES FOR EDUCATORS
Organizer: Yelena F. Takhtamanova, Federal Reserve Bank of San Francisco
Chair: Yelena F. Takhtamanova, Federal Reserve Bank of San Francisco
Papers: Jody Hoff, Federal Reserve Bank of San Francisco
Federal Reserve's Response to Crisis
Yelena F. Takhtamanova, Federal Reserve Bank of San Francisco
Experiential Learning Tools
Gary Zimmerman, Federal Reserve Bank of San Francisco
FRBSF Online Resources for Educators
Jeffrey A. Parker, Reed College
Educators' Perspective
Discussants: Sarah E. Tinkler, Portland State University
Peter Wilamoski, Threshold Group
Jenny Liu, Portland State University

June 30, 4:30–6:15 p.m.
TOPICS IN ECONOMIC EDUCATION
Organizer: Robert C. Dolan, University of Richmond
Chair: Robert C. Dolan, University of Richmond
Papers: Dean D. Croushore, University of Richmond, and Robert M. Schmidt, University of Richmond
RIDIT Analysis of Student Course Evaluations in Economics
Jeffrey A. Parker, Reed College, Jan Crouter, Whitman College, James Grant, Lewis and Clark College, and Jon Rivenburg, Reed College
Peer Effects: Evidence from Core Courses at Three Colleges
David H. Dean, University of Richmond, and Robert C. Dolan, University of Richmond
The Capstone Course in Economics
Discussants: Arthur H. Goldsmith, Washington & Lee University
Garrett Milam, University of Puget Sound
Nicholas Shunda, University of Redlands

Friday, July 2, 8:15–10:00 a.m.
TEACHING METHODOLOGY I
Chair: James P. McCoy, Murray State University
Papers: Gareth Paul Green, Seattle University, John C. Bean, Seattle University, and Dean Peterson, Seattle University
Using Public Policy Issues to Teach Quantitative Analysis in Economics
Martin I. Milkman, Murray State University, and James P. McCoy, Murray State University
Exemplary Programs in Preparing Graduate Assistants to Teach Economics
Paul R. Johnson, University of Alaska, Anchorage
A Classroom Business Cycle Experiment
Discussants: from above participants

July 2, 10:15 a.m.–12:00 n.
TOPICS IN ECONOMICS EDUCATION
Organizer: Sarah E. Tinkler, Portland State University
Chair: Sarah E. Tinkler, Portland State University
Papers: Clifford Nowell, Weber State University, and Lewis R. Gale IV, Weber State University
Correcting Student Evaluations of Teaching for Missing Data: Online versus Face-to-Face Evaluations
James Woods, Portland State University
Online and In-Class Exams as a Method of Assigning Course Grades: Are They Equivalent?
Sarah E. Tinkler, Portland State University
Can Students Read College-Level Economics Textbooks?
Discussants: James Woods, Portland State University
Clifford Nowell, Weber State University
Martin I. Milkman, Murray State University
Tim Anderson, Portland State University

July 2, 2:30–4:15 p.m.
STARTING POINT ECONOMICS: A SOURCE FOR PEDAGOGICAL RESOURCES
Organizer: KimMarie McGoldrick, University of Richmond
Chair: KimMarie McGoldrick, University of Richmond
Papers: KimMarie McGoldrick, University of Richmond
Starting Point---Teaching and Learning Economics
KimMarie McGoldrick, University of Richmond
Cooperative Learning in Economics
Andrea L. Ziegert, Denison University
Service-Learning in Economics
Mark H. Maier, Glendale Community College
Interactive Lecture Demonstrations
Discussants: Samuel K. Allen, Virginia Military Institute
Genevieve Briand, Eastern Washington University
Elizabeth Perry-Sizemore, Randolph College
Steven J. Balassi, St. Mary's College of California

July 2, 4:30–6:15 p.m.
ASSESSMENT TOOLS AND THE TEACHING INNOVATIONS PROGRAM
Organizer: KimMarie McGoldrick, University of Richmond
Chair: Steven J. Balassi, St. Mary's College of California
Papers: William B. Walstad, University of Nebraska
An Assessment of the Teaching Innovations Program for Economics Faculty
Lee Erickson, Taylor University
Comparing Formative Assessments: Daily In-class versus Weekly On-line
Felix B. Kwan, Maryville University
An Innovative Course-Assessment Tool: Letters to Incoming Students
Geetha Rajaram, Whittier College
Teaching Econometrics with Summative and Formative Assessment
Discussants: Thomas White, Assumption College
Beth Wilson, Humboldt State University
Hisaya Kitaoka, Franklin College
Julie K. Smith, Lafayette Colleg

Saturday, July 3, 10:15 a.m.–12:00 n.
TEACHING METHODOLOGY II
Chair: Mark W. Frank, Sam Houston State University
Papers: Genevieve Briand, Eastern Washington University, and R. Carter Hill, Louisiana State University
Teaching Basic Econometric Concepts Using Excel Monte Carlo Simulations
David F. Kauper, University of the Pacific
Teaching Profit Seeking as the Source of Growth
Satyajit Ghosh, University of Scranton, and Sarah Ghosh, University of Scranton
Spreadsheet Based Interactive Teaching Strategies and Assessment of Learning Outcomes
Sean Jasso, Pepperdine University
Demystifying the Pedagogy of Globalization: A Contemporary Approach to Effective Learning
Discussants: Mark W. Frank, Sam Houston State University
Satyajit Ghosh, University of Scranton
Sean Jasso, Pepperdine University
David F. Kauper, University of the Pacific

How much weight do you give evaluations?

By now, a lot of academics (or at least academic economists) have heard about Scott Carrell and James West's paper on professor quality. They use data from the Air Force Academy (where students are randomly assigned to core courses and take common exams) and find that the 'value-added' of professors in intro courses is both positively correlated with student evaluations and negatively correlated with 'value-added' in follow-on courses (which the authors talk about as evidence of 'deep learning'). Basically, professors who seem to be better at inducing 'deep learning' in their intro students are also more likely to get lower evaluations from those students.

On the one hand, I have to say that this feels kind of validating for people like me - that is, I care a lot about helping my students learn to think critically and I think I put a lot of effort into trying to foster deep learning, rather than allowing my students to just memorize stuff, but I rarely get stellar evaluations (at least in my Principles course) and I've often told myself that my focus on deeper learning is one of the reasons why my evaluations aren't as high as some of my colleagues. Certainly, some of the open-ended comments from students on the anonymous department evaluations could be interpreted as them resenting that I ask them to actually think.

But on the other hand, I can't be quite so cynical as to blame it all on my students. I think about teachers, like those in Ken Bain's What the Best College Teachers Do, and I know that they are able to not only promote deep learning but they are able to do so in a way that students appreciate and respond to. So clearly, I have work to do...

But I have to say, one of the best things about tenure is that I don't really have to care about my teaching evaluations. I know that is exactly the kind of thing that makes a lot of people think tenure is a bad thing, and I do realize that for some people, the 'threat' of bad evaluations is the only thing motivating them to care about their teaching at all. But a) I don't think anyone could seriously argue that me not caring about my evaluations is equivalent to me not caring about teaching and b) since I'm going to keep working on my teaching regardless, the anonymous student evaluations done for my department tend to just stress me out without giving me much useful feedback. I get much more useful information from the end-of-course surveys I have students do that are tailored to the individual courses, which I will be talking more about soon...

Quantitative methods in economics

I should clarify something I said last week about how this data and statistics course I'm prepping isn't really a standard course: I do know there are lots of econ departments that offer an economic statistics course that is a step below econometrics. I guess what I was thinking is that the intention behind this course was specifically to give students tangible skills working with data, so there is supposed to be a heavy emphasis on finding and accessing economic data and using Excel to manipulate that data, rather than on formulas and statistical theory (which students presumably should have already had anyway, in a lower-division required statistics course). But my impression is that a lot of econ statistics courses for majors at other schools are really more stats courses, just with econ-related examples*.

The interesting thing is that the Siegfried, et al, report ("The Status and Prospects of the Economics Major, JEE, Summer 1991) discusses this very situation (emphasis mine):

Originally conceived as a means of providing students with a sufficient empirical foundation to enrich their understanding and facilitate their active participation in applied courses, the [quantitative methods] requirement all too often fails to fulfill this purpose. Although this requirement presupposes the development of skills in working with real data, contrived numerical examples are more common in these courses... These courses are often overloaded and taught at too fast a pace to adequately prepare students for the empirical dimension of elective courses. Frequently, data appraisal (e.g., survey design, sampling procedures, data accuracy) is squeezed out of the course, and some quantitative methods courses fail to cover adequately the philosophy, appropriate use, and limitations of hypothesis testing and regression analysis. Sophistication in empirical work requires more than just training in statistics. It requires attention to observation skills, measurement problems, and empirical judgment. Students need guidance on how to judge the quality of data, and how to identify evidence that would help to resolve an empirical dispute.

I think that this is what my department was likely striving for when we originally adopted this course. Over the years, instructors have tried to retain the emphasis on data but the content seems to be more along the lines of 'contrived numerical examples'; students use lots of real data and have assignments to make graphs and charts in Excel but from what I can tell, there is still more focus on formulas and statistical concepts than the 'art' of using data to resolve economic questions.

As I plan this course, I am definitely striving for something more along the lines of what Siegfried, et al, suggest:

Rather than view this as a matter of learning statistics, we need to ask what it is that student must know to function as economists. The foundation of empirical methods depends on (1) knowing something about the measurement of economic variables (methods of data collection, reliability, etc.); (2) being able to organize, work with, and manipulate data for purposes of comparison; (3) the capacity to test hypotheses with empirical data; and (4) knowing how to interpret the results of various statistical procedures.

In the framework of backward design, that pretty much lays out my learning outcomes (what I want my students to be able to do). Now I just have to figure out how to achieve this...

* If I am wrong about this, I would really love to hear from people who teach more data- and application-driven courses. As an undergrad advisor, I've seen a lot of requests from transfer students who want to take an econ stats course somewhere else and have it count the same as our course but they rarely look comparable (or at least, they don't look like the course that we think we have, though now that I'm taking a closer look at what my colleagues have been teaching, I think the courses at other schools may be closer than we thought). 

Researching teaching

Given that I've never taught statistics, I eagerly read this Tomorrow's Professor post on Teaching What You Don't Know, which led me to Therese Huston's book by the same name. I haven't read all of it yet but I started with chapter 3, "Getting Ready" (yeah, I jumped ahead but Huston even says it's OK - the chapter starts with: "If you've just been assigned to teach a course that's outside your specialty and you're barely hanging on as it is..., you might have skipped directly to this chapter and bypassed everything else. That's fine."). The first thing that Huston suggests is 'planning backward':

Fortunately, you can turn to the proven educational principle of backward design, also known as planning backward, to organize your class in a way that maximizes student learning and focuses your daily preparation. It's called "backward" design because you begin with the end product first: what do you want students to be able to do as a result of learning in your course? Note that the emphasis is on what students can do, rather than what they will know.

Since this new course I'm teaching is supposed to be focused on having students do a bunch of stuff, this sounded good to me. Huston goes on to discuss developing learning outcomes, outlining evidence of learning (how will you know your students have learned what you want them to learn?) and then, only after that, thinking about what you need to do as an instructor to help students produce that kind of evidence. This all made a lot of sense to me, though I was still fuzzy on how exactly to go about doing this.

So I googled 'backward course design' and that led me to the work of Grant Wiggins and Jay McTighe and for the last few days, I've been reading Understanding by Design and filling pages of notes with random ideas about 'big questions' that I think this course should be trying to address, as well as smaller questions that will probably end up driving individual class meetings or units, plus activities, assignments and projects for students to do.

And because I can only go so long before a lack of an overarching organizational structure starts to drive me nuts, I also went back to some classic articles in economics education that I thought might help, including Hansen's work on proficiencies for undergraduate economics majors (AER 1986, JEE 2001) and the Siegfried, et al, article on "The Status and Prospects of the Economics Major" (JEE 1991), which also led me to three papers in the AER Papers and Proceedings from 1987, from a roundtable on teaching economics statistics. There's a lot to digest!

I'll be coming back to all the teaching stuff but my point here is that it dawned on me that I'm actually tackling this new course the way I tend to tackle new research projects. That is, I think one of the side effects of graduate school, or maybe doing research in general, is that whenever I'm faced with something I don't know much about, I have a tendency to think, "the answer must be in a book/journal somewhere". As a researcher, whenever a possible research topic pops into my head, I usually start by trying to find out if anyone has already answered the question (that is, for questions that have not just grown out of research I'm already working on and where I know the literature). So I guess it's not surprising that I seem to be taking a similar approach to teaching this new course (though I didn't really go looking for 'how to teach what you don't know' but it's turning out that I'm going through a similar process nonetheless). I know that, just like with my research, at some point I have to stop reading other people's stuff and start doing it myself but given my lack of experience with both the subject matter and with course design in general, I'm thinking that doing some research before jumping in isn't such a bad use of my time...

Designing a course

After a few weeks of catching up on referee reports and other projects that I should have done a long time ago but ignored because of classes, plus simply some much-needed piddling, I'm back in the teaching saddle and starting to design the new course I'll be teaching in the fall.

Most economists (and, I assume, many other University professors) rarely actually purposely design courses. That is, a lot of professors start out as teaching assistants during graduate school, which usually means we just do whatever the professor we're working for tells us to do. When we move on to teaching our own classes, if it's a course for which we T.A.'ed, then we just follow whatever the professor we worked for did in that course. If it's a new course, we find other people who have taught the course and ask them for help, which amounts to getting their syllabus and maybe old assignments and exams. Once in a blue moon, someone might develop a course that hasn't really been offered much before (like a specialized course on the economics of some area of the world, or the course for teachers that I created two years ago), but the vast majority of economics topics courses are 'standard' in that if you go to any other university, there will be a course with the same name and similar content, and all the textbooks include generally the same stuff, and you can almost always find someone who can tell you how they taught it so you don't have to start from scratch. Once you've taught a course a few times, you may make improvements and adjustments but I think it's really rare for someone to step back and completely re-think a course from the ground up.

But this summer, that's basically what I'm doing. Actually, since I haven't taught the course before myself, I guess I'm not "re"-thinking but I am stepping back and trying to design the course clearly, from the beginning, rather than just relying on what others have done. This is partly because this is not really a standard course in economics - it's a data and statistics course that my department created about a decade ago when it was clear that econometrics was too much for most of our majors but we wanted them to have more experience with data than they were getting in lower-division stats courses. So the idea is to give the students lots of hands-on practice with finding, manipulating, analyzing and presenting data, mostly using Excel. There may be similar courses at other universities (and if you happen to teach in a place that has one, please let me know!), but there isn't really a "standard" way to teach it, other than following what my colleagues have done previously.

What's scary is that I have never taught anything remotely resembling statistics, so I'm not exactly sure what I was thinking when I volunteered to take this on. But the general concept - i.e., that students should have a better understanding of how economists (as well as the rest of the world) use data - is one that I believe in strongly. Similar to writing well, I think that understanding data and statistics is a skill that not only gives students a competitive advantage in the workplace but the very process of developing that skill can help them think more like economists. So although I'm sort of clueless about how to teach a lot of the statistical tools (and am apparently going to have to finally bite the bullet and start using Office 2007), I'm really excited about this opportunity to think about the bigger picture and how to get students engaged in the ways in which economists use data and statistics. I'm sure that you, lucky readers, will be hearing a lot about it along
the way...