Flipping the Research Methods Classroom, Part 2

Today we have the second post in a series on building a flipped course by Natascha van der Zwan and Alexandre Afonso. Both are assistant professors at the Institute of Public Administration at Leiden University, the Netherlands. They can be reached by emailing Natascha at n.a.j.van.der[dot]zwan[at]fgga[dot]leidenuniv[dot]nl.

How to Build a Flipped Classroom

University teaching is not very different from the way Adam Smith or Max Weber taught a century or more ago. Aside from the inescapable PowerPoint, there is usually a lecturer standing in front of a group of students who take notes. The reason teaching stayed the same may be purely path dependent: departing from this format may go against administrative rules and habit. Developing new ways to teach requires an investment in resources in time and energy that always run scarce when the new semester looms. At Leiden University, we are lucky to have a great deal of institutional support and a clear commitment from the university for developing innovative forms of teaching.

And this meant . . . going to the film studio!

Leiden University wants to play a leading role in the development of open educational resources. There are several Leiden-originated massive open online courses (MOOCs) on Coursera, including a course on kidney transplants by the Leiden University Medical Center and our colleague Bernard Steunenberg’s MOOC on Politics and Policy in the European Union. Over the years, the university has developed quite an infrastructure to make these MOOCs, with several studios on campus to create instructional videos. Also Leiden’s Online Learning Lab employs professional videographers and instructional designers who specialize in online learning and in helping faculty members such as Alex and myself make the jump to online video content.

We had a list of demands that we wanted for our flipped classroom. These were, in no particular order: Continue reading

Flipping the Research Methods Classroom, Part 1

Today we have the first post in a series on building a flipped course by Natascha van der Zwan and Alexandre Afonso. Both are assistant professors at the Institute of Public Administration at Leiden University, the Netherlands. They can be reached by emailing Natascha at n.a.j.van.der[dot]zwan[at]fgga[dot]leidenuniv[dot]nl.

Since I have started teaching graduate courses on research methods, I have struggled with the way in which such courses are usually taught. Why do we still teach research methods from textbooks? Most academics will agree that you learn best how to do research by simply doing it, and the traditional lecture format, where students are rather passive, seems inappropriate to achieve this.

For this reason, my Leiden colleague Alexandre Afonso and I have spent the last two years developing a new way of teaching research methods to our students, transforming our existing course into a flipped classroom using blended learning. The flipped classroom was developed with the financial and material support of Leiden University’s ICTO program and the Faculty of Governance and Global Affairs. Alexandre and I will describe what our flipped classroom consists of, how we set it up, and our experiences teaching it. Continue reading

And Now For Something Else Completely Different

The effects of too much time spent sitting in front of a computer put me in the market for a standing desk converter — one of those table-top contraptions that are adjustable in height, enabling the user to work sitting down or standing up. Like me, you’ve probably been seeing them increasingly frequently in your travels and have wistfully wondered, “Do I deserve to enter the ranks of the office equipment elite?” Luckily our crack library staff came to my rescue. They permitted me to test drive one and take this inexpensive Ikea hack back to my office for my own use.

I then researched various commercially-available models to get something for my home. I was drawn to products made by Varidesk, Eureka Ergonomics, and FlexiSpot. My search narrowed my options to one model from each company. One was priced at US$400 and two were priced at US$300. I scrutinized the design of each to gauge durability and convenience. I read comparative analyses written by professional reviewers.

Then serendipity struck: a standing desk unit sold by Staples, the office supply retailer, looked remarkably familiar. I compared dimensions and appearance, and yes, it was an exact match to one of the previously-described models, but priced at only US$200. So I bought the thing and am now using it to type this post.

It occurred to me that the process I used to make my decision is the same type of analytical thinking that we want our students to become proficient at — cast a wide net to gather the best information one can find, evaluate it according to context, and render a judgment. It’s one of the skills that we say students will develop if they take political science courses. So now I’m trying to figure out how to turn my experience into an assignment, to make the connection between what gets learned in an academic setting and the ability to apply it elsewhere more obvious to students.

Data Visualization in the Classroom

Today’s post is guest-authored by Alexander Von Hagen-Jamar, a postdoctoral researcher with the STANCE research program, in the  Department of Political Science at Lund University. His research and teaching focus on international relations, international security, state building and capacity, and empirical methodology.

In 2013, I spent two trimesters teaching at Carleton College in Northfield, Minnesota. While there, I had the opportunity to design a course about any subject I wished (within my expertise). I choose to organize the class topically around the consequences of violent political conflict. The other core learning goal was skill-oriented: I wanted to help the students develop applied quantitative literacy in context, and through doing so, encourage them to think deliberately about communication in a variety of mediums. To do that, I designed a series of assignments, centered around a group data visualization assignment. Continue reading

Two Online Games From The New York Times

Last month The New York Times published an updated version of its confirmation bias game that might be useful for teaching research methods or political psychology. The newer version includes an explanation of how confirmation bias affects government policy.
Also of note is another game on President Trump’s plan for changing U.S. immigration criteria. I failed to qualify under these new proposed rules. Probably all of my students will fail also.

Interactive Resources for Teaching Stats

The internet has allowed the creation and dissemination of a wide range of tools useful to those of us who teach statistics in our research methods courses. I found two to be particularly helpful.

  1. Guessthecorrelation.com – As its name implies, the site gives students a scatterplot of points and asks them to guess the correlation. My students were asked to play three games and upload screenshots of their final scores as evidence that they had completed the assignment. Many went on to play more than three games; the sound effects and points make it a very addictive game. What it brought home to them very effectively is that correlation is about how tight the points are to the (imaginary) best-fit line, not about the slope of the line. Students enjoyed playing a game as homework; it was certainly less onerous than practicing calculating correlations by hand.
  2. The Rice Virtual Lab in Statistics: Sampling Distributions – The Java-based simulation for sampling distributions allows you to draw a distribution of any shape you want, select repeated samples of any size, and then plot the sampling distribution of the means (or several other statistics). I even let them draw some of the distributions and do some of the simulations so that they were convinced it wasn’t just the values I was picking. I was able to demonstrate to the class in just a few minutes that the shape of the parent distribution doesn’t matter; the means will always be distributed normally.  Watching their faces, this really blew their minds; they probably would have blindly accepted it if I just told them this is how it is, but having seen it, we had a much easier time accepting that the same property held for regression coefficients. (The chance to visit the central limit theorem was a bonus for undergrads.) The whole activity took less time than a lecture of the similar material. (A similar lab simulation exists for confidence intervals as well.)

What about you? What are your favorite interactive sites for teaching research methods or statistics?

To tell the truth

Sic

Reading this week’s Economist article on new algorithms for generating audio and video content, I was really struck by the speed with which the assumptions we teach our students have to be questioned.

As the techy types interviewed in the piece argue, it’s only a question of time before it will be possible for anyone to generate any content they like; to get anybody to say anything you want them to.

While that might have some benefits – the technology will allow us to identify such fake content more easily too – it’s also clear that our traditional reliance on content as a repository of ‘truth’ is under attack.

More prosaically, we all have enough trouble as it is with our students’ (and (sometimes) colleagues’) inability to make critical judgements about the veracity of sources: if you doubt me, come and spend an hour or two on Twitter.

Continue reading

How Much Does the Layperson Know?

Today we have another guest post by Gigi Gokcek of the Dominican University of California.

Students are often surprised to learn how little the average person knows about politics, or even current events. In response, I encourage my students to ask their friends and neighbors how much they know about government in the United States or elsewhere. Occasionally a student reports back to me about his or her conversation in the dining hall with a few friends. I decided to create an assignment to demonstrate to students how much they knew about world events relative to their peers. Continue reading

The Intel Community and the Theory of Knowledge

Today we have a guest post from David Young, Head of Theory of Knowledge and Ideas, The English College in Prague. He can be reached at david [dot] young [at] englishcollege [dot] cz.

A while ago I was asked to  develop a critical thinking course for an International Baccalaureate (IB) school as a preparation for its Theory of Knowledge course.  As someone who teaches global politics, I was drawn to two books: David T. Moore’s Critical Thinking and Intelligence Analysis (2nd ed 2007), and the invaluable The Art of Intelligence (2014) by William J. Lahneman and Ruben Arcos. Both have had a significant impact on my teaching and my position as the school’s co-coordinator for Theory of Knowledge (ToK), a core element in the IB programme.

In ToK, students are supposed to formulate and evaluate knowledge claims and ask questions about the acquisition of knowledge, making it one of the most challenging elements in a congested pre-university curriculum. I’ve found the analysis of intelligence and the ethical issues surrounding its collection and dissemination to be an exciting way for students to learn about ToK concepts such as reason, imagination, intuition, and sense perception. From my perspective, using principles of intelligence analysis has both enhanced my understanding of ToK and improved the course for students.
Continue reading

Using Stats in the Regular Classroom: The 3 S’s Approach to Interpretation

One of the obstacles to using statistically-tested articles in the regular classroom is that most students don’t know (or don’t remember) how to interpret the results. I developed a very simple scheme, known as the 3 S’s, to help them understand results tables and quantitative articles more generally. While the basic framework was designed with linear regression in mind, after a few practices you should be able to introduce the framework in the context of more complicated modes (i.e., in probit/logit the size of the coefficients isn’t really meaningful by itself).

The first S: Sign. Hypothesis testing is generally about whether the relationship we find goes in the direction we think it should. This is predicted by the sign on the coefficient: whether the relationship is positive (upward slope) or negative (downward slope). So the first thing we’re interested in, when we’re testing a hypothesis, is whether we’ve gotten the sign right. Does what we found match what we expected?

The second S: Sureness. Now that we’ve found the sign or direction of the relationship, how sure are we that the sign is right? This is the concept of statistical significance, simplified down to its core element. Sureness asks about whether the value we found is “far enough” away from 0 to allow us to be sure that the sign is right. If the value we found is very close to zero and we’re very uncertain (statistically speaking) about that value, we can’t trust that the sign is right. The true value could actually lie on the other size of 0, and thus our sign would be incorrect. If the value is “far enough” from 0, then we can be reasonably sure that the sign is correct. I usually gloss over the concept of “far enough” and explain that they’ll cover standard errors in their statistics or methods course. For now it’s enough to know that we can be sure of most rather large numbers and even some small numbers if we’re very highly certain about the value we estimated for them.

The third S: Size. Only after we’re sure the sign is right can we meaningfully talk about the size of the relationship. Size isn’t the only thing that matters; in fact, it’s usually the least important in interpreting statistical results. The size of the relationship tells us how much the value of the dependent or outcome variable changes for each one-unit change in the independent or input variable. I have sometimes found it helpful to write out the middle-school equation for a line, y = mx + b, and explain the effect of coefficients by talking about what if x changed from 4 to 5 – how much would y change? What if it went from 58 to 59? Etc.

You can find a helpful powerpoint that walks through this logic – the Crash Course Statistics package – on my website.