Active Learning in Economics With FRED Data

Framework

Information literacy and numeracy are considered as complementary domains by multiple scholars on teaching and learning. Erickson (2019) describes a cross-case analysis across three universities where instructors deploy course assignments aimed at developing the critical use of quantitative information when constructing mathematical claims. In a similar vein, Mendez-Carbajo, Jefferson, and Stierholz (2019) discuss both the identification of sources and the close examination of the quantitative information itself as the foundation of robust quantitative arguments.

Within the field of economics education, several authors argue for bringing the “empirical turn” identified by Hamermesh (2013) in the field at large into varied undergraduate instructional practices. Mendez-Carbajo (2020) and Wolfe (2020) describe small-group projects targeted to students of introductory economics building data-reading skills; Halliday (2019) describes small-group projects designed for advanced undergraduate students building data-science skills; and Marshall and Underwood (2019) describe an upper-division project designed for economics majors building data-management skills.

Methods

We begin by mapping the data-related expected competencies of economics majors described by Hansen (2009) and the expected set of data literacy competencies for all business students described by Pothier and Condon (2019) to the content of the online instructional module FRED® Interactive online module “Real GDP per capita.”

In this module, students receive directions to interact with the FRED (Federal Reserve Economic Data) portal (https://fred.stlouisfed.org/) by searching for a specific data series and plotting it in a graph. Hints are available and feedback is provided in order to highlight practices directly related to information literacy. Next, the students answer eight multiple-choice and true/false questions highlighting the interplay among economic analysis, information literacy, and numeracy that underpins active learning with data in economics. This questions direct students to recognize the source, units, and frequency of the data, as well as to interpret the data visualization they created.

Whenever a user answers a question included in the module, they are required to report the level of confidence in their answers. Users do so by selecting one of four discrete options presented in a sliding bar: very confident, pretty confident, unsure, or don’t know. We code the levels of confidence by assigning a numerical value to each option: very confident = 4, pretty confident = 3, unsure = 2, and don’t know = 1.

To adjust the answers for the reported level of confidence, first, the correct answers are coded as 1 and the incorrect answers are coded as -1. Next, those values are multiplied by the coded level of confidence divided by four. This way, the self-efficacy reported by each student is weighed into the average percentage of correct and incorrect answers to each question. For example, a correct answer where the student is very confident is scored as 1 x (4 / 4) = 1 and an incorrect answer where the student doesn’t know is scored as -1 x (1 / 4) = -0.25.

We analyze the work completed by 1,130 students: 326 high school students and 804 college students across 34 different educational organizations. We use both t-tests of equality of means across groups as well as ordinary least squares regression analysis.

Results

We find a positive association between data literacy and numeracy skills. However, high school students display a lower average baseline on information literacy and college students display a relatively weaker association across knowledge domains.

Importance

Although we find evidence of a positive association between data-related information literacy and numeracy, the strength of that association is significantly different among high school and college students. We also find marked differences in confidence levels among high school and college students, which can pose a challenge to instructors when attempting to dislodge misconceptions.

References

Erickson (2019). Introducing information literacy to mathematics classrooms: A cross-case analysis. Numeracy, 12 (1), 135-157, DOI:10.5038/1936-4660.12.1.7

Halliday, S. D. (2019). Data literacy in economic development. The Journal of Economic Education, 50 (3), 284-298, DOI: 10.1080/00220485.2019.1618762

Hamermesh, D. S. (2013). Six decades of top economics publishing: Who and how? Journal of Economic Literature, 51 (1), 162–72. DOI: 10.1257/jel.51.1.162.

Hansen, W. L. (2009). Reinvigorating liberal education with an expected proficiencies approach to the academic major. In Educating economists: The Teagle discussion on re-evaluating the undergraduate economics major, ed. D. Colander and K. McGoldrick, 107–25. Cheltenham, UK and Northampton, MA: Edward Elgar.

Marshall, E. C. & Underwood, A. (2019). Writing in the discipline and reproducible methods: A process-oriented approach to teaching empirical undergraduate economics research. The Journal of Economic Education, 50 (1), 17-32. DOI: 10.1080/00220485.2018.1551100

Mendez-Carbajo, D., Jefferson, C. O., & Stierholz, K. L. (2019). Keeping it real: Information literacy, numeracy, and economic data. Numeracy, 12 (2), 5, DOI:10.5038/1936-4660.12.2.5

Mendez-Carbajo, D. (2020). Active learning with FRED data. The Journal of Economic Education, 51 (1), 87-94, DOI: 10.1080/00220485.2019.1687377

Wolfe, M. H. (2020). Integrating data analysis into an introductory macroeconomics course. International Review of Economics Education, 33, DOI: 10.1016/j.iree.2020100176