Pedagogy in Action > Library > Developing Quantitative Reasoning > QR Across the Curriculum Profiles > Jill Bouma, Sociology

Jill Bouma, Sociology

Jill Bouma is an associate professor of Sociology at Berea College, a private 4-year institution. Information for this case study was obtained from an interview conducted on September 17, 2013. This page is part of a collection of profiles about a variety of techniques for integrating Quantitative Reasoning (QR) across the curriculum.

Jump down to Design and Implementation of QR Goals | Key QR Assignment of the Course | Challenges | Advice | Documents

Overview and Context

About the Course

This 100-level sociology course, Problems of American Institutions, is popular with both majors and non-majors, in part because it fulfills a general education requirement called, "Social Science Perspective." I have taught this course for about ten years.

The student audience is largely comprised on students from Appalachia, one of the poorest regions in the country. Berea College has a long history of promoting social justice going back to before the Civil War. Students are only admitted if their parents are low income (i.e. earn below a certain amount.) Tuition is paid in full for all students. Berea College was the first school in the South to be integrated with black and white students and it was the first co-educational school. It is also unique in that all students work 10 to 15 hours per week.

Syllabus for Problems of American Institutions (Microsoft Word 73kB Oct23 13)

Key QR Assignment Description (links to section in this page)

How Quantitative Reasoning (QR) and Literacy are Approached

Berea College recognizes the importance of quantitative reasoning and has general education requirement called, "Practical Reasoning Quantitative" or PRQ. While I teach a considerable amount of quantitative literacy and reasoning in the Problems in American Institutions course, I have not tried to have this course designated as fulfilling the PRQ requirement.

Since many of our students do not have a strong background in math, and some have a real math phobia, I focus primarily on literacy and on simply developing comfort with numbers. I help them learn what numbers mean and we play with them a lot. For example, we explore basic statistical information such as economic data expressed in percentages (e.g. What is the population of Kentucky? What is the unemployment rate? How many people are in poverty?) In the data assignment described below, I teach both literacy and reasoning.

Design and Implementation of QR Goals

Motivation to integrate QR

One motivation was a determination to help students understand how to use data in ways that will improve their lives economically. Our students have lived through many of the economic problems that I teach them about in this course. Unemployment rates in areas where most of our students come from are really awful, and many children are poor. Most students can tell compelling stories about living in poverty. I want them to have the ability to use data to back up their stories.

Another motivation was a change within our department that occurred in 2002. Our department was among the first Integrated Data Analysis (IDA) departments funded through the American Sociological Association (ASA) and the National Science Foundation (NSF). We were one of about 13 departments across the country to receive this funding. The purpose of this initiative was to get whole departments or at least majorities of departments to adopt a policy of integrating data into sociology courses. I wrote the grant for our department, which consisted of only three people at the time, all of whom were supportive.

PowerPoint presentation: Integrating Data Analysis at Berea College (PowerPoint 952kB Oct23 13)

QR goals

I want to increase students' confidence using numbers to make arguments. I want our students to know what data means—especially economic data–and to be able to make use of that data to make convincing arguments. I want students to be able to show, using data, that there are real income differences and inequalities among certain segments of our population.

As part of this ability to understand and make arguments, I want students to learn how to communicate using numbers both orally and in writing. For example, students learn how to read, interpret, and report basic frequencies. I try to encourage students to become comfortable looking at tables and figuring out how to read and talk about them. I also ask students to create tables themselves; and, how to properly format and label tables so that they are more accessible for other people.

In addition, I want students to be able to form and test hypotheses. In order to do this, students must first understand the difference between independent and dependent variables. Then they must learn how to describe relationships between variables, such as between gender and income or race and employment.

Pedagogic approaches used

In order to increase students' confidence in their ability to learn quantitative reasoning, I model enthusiasm for numbers. It's rare that I have students who actually like math or are excited by it. A lot them are really afraid of it. But I love teaching about numbers. I use humor and make my excitement for numbers funny so that it becomes infectious. I rub my hands together and exclaim, "Oh, goody, today we get to do numbers! Isn't this great? Don't you love this?" At first, students laugh and roll their eyes, but by the end, they start to imitate me and exhibit more enthusiasm for working with numbers.

Repetition is among the most important techniques I use because quantitative reasoning is not intuitive. My students have not done it very often. Also, there are a lot of common mistakes made in describing percentages. So I give them a lot of chances to practice and then correct them. One way they practice is by taking a True-False test (Microsoft Word 43kB Oct23 13) that includes many of the common mistakes. I also provide students with a handout that includes a list of rules for reading and writing about statistics (Microsoft Word 37kB Oct23 13).

I also use the analogy of telling a story to teach about communicating with numbers. I start by asking students, "What are the opening lines of the story you want tell?" From there, I ask them to build their story using numbers as evidence. Then, I ask one or two students to read their stories aloud, and I usually choose one who I know is pretty good and a couple who I know struggle. The strugglers are actually the most instructive because the other students can hear what is unclear or incorrect and offer suggestions. It's a very time intensive process, but the more I use this method, the better the students get at telling stories using numbers.

Peer review and involvement of other students is also very helpful. Students read each other's papers and also read their own writing aloud in class. For the final papers, I ask students to review other students' work and to fill out a peer review response sheet (Microsoft Word 50kB Oct23 13) . I also ask students to teach someone else outside of class about the information in their papers in order to help clarify their arguments.

Knowing the course is successful

I use a pre- and post-test (Microsoft Word 43kB Oct23 13) in my class to assess student learning each semester. The questions are closely tied to the data analysis assignment described below. I administer the test the first day of class, and again as part of the final exam, along with a set of questions measuring students' confidence. Among other components, part of the test includes a table provided as a student handout (Microsoft Word 27kB Oct23 13), and students have to write about it using proper explanations. Students also must properly identify independent and dependent variables. I definitely see dramatic improvement. Students usually score between 50 to 60 percent on the pre-test, and between 80 to 95 or even 100 percent on the final.

Secondly, our department has introduced a new assessment tool that we are now using in the majority of our sociology courses. This test of social information (Microsoft Word 28kB Oct23 13) includes 24 questions (which we suspect may be too long.) What we are trying to do is to help students make sense of basic socio-economic data and feel more comfortable with using it. Questions include: "What is the unemployment rate in the U.S.? What is the population of Kentucky? How many people are poor? What percentage of children are poor?" All of the questions are open response and ask for numbers and percentages.

We hope to administer this test to first-year students as they enter the college and to graduating seniors. We will emphasize teaching these numbers throughout our courses. We provide all the questions and students can fill in the numbers whenever they want. The numbers change every so often, so I teach students where to look up the numbers and tell them to keep their list updated.

I think it's both funny and sad to see some of the guesses on these questions. As an example, some students drastically underestimate or overestimate world population: they might guess the world population is 100 million or in the trillions. This indicates to me that our students don't have much of a sense of what numbers really mean. This is not surprising, however, since most adults don't have much of a grasp of these numbers either.

Key QR Assignment of the Course

For the Data Analysis Project, students conduct data analysis to investigate the effects of race and gender on earnings in the United States, give a group presentation, and write a paper.

This assignment comes after students learned the theoretical background of stratification in the U.S., so the exercise provides students with a hands-on opportunity to explore how real data relates to sociological theory.

Two handouts are used in sequence to scaffold student learning for this project:

Handout #1 (Microsoft Word 85kB Oct23 13)

Handout #2 (Microsoft Word 90kB Oct23 13)

The class works through Handout #1 together, which deals with inequality in earnings based on race and gender for the whole United States. This first handout includes an introduction to the project and teaches students how to:

Next, the students work in pairs during class on Handout #2. This second part of the project examines data for the state of Kentucky in comparison to another state of the students' choosing.

Students access data from the website, DataCounts!, to find demographic and economic information about the states from the dataset, 2008 American Community Survey. The handout provides step-by-step instructions for finding data on this website.

Once students have found the data and created tables based on the instructions, they go through a number of exercises using the data. Students answer a series of fill-in-the-blank questions and write a short summary describing the data they found. They make a hypotheses about what they expect to find in Kentucky and another state with regards to differences in earnings based on race, age, and gender.

The next step is for students to create cross-tabs tables. At this point, they must be able to identify the dependent and independent variables in order to run the analysis in the computer program. Students create the following tables for the United States as a whole, Kentucky, and another state of their choosing.

  1. frequencies of age, gender, race, and income distributions
  2. cross-tabs of gender by earnings
  3. cross-tabs of race by earning

Working in pairs, students present their findings about the state they chose to the class. Afterwards, working individually, students write a 4 to 6 page paper analyzing the data. Overall, they have about 2 weeks to complete the paper. Handout #2 provides guidance about the structure and content of the paper, which includes: an introduction, brief literature review, description of the population being examined, results, tables, conclusion, and bibliography.

Lastly, students review each other's papers using a rubric, which counts for 10% of their grade.

Challenges

Advice

Documents




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