economics, assessment, Quantitative Literacy and Reasoning Assessment
As the number of young people attending college has increased, the diversity of college students’ educational backgrounds has also risen. Some students enter introductory courses with math anxiety or gaps in their quantitative training that impede their ability to master or even grasp relevant disciplinary content. Too often professors learn of these anxieties and gaps only during the post mortem of the first midterm. By that time, a good portion of a student’s grade is determined and successful recovery may be impossible. During the 2016-17 academic year, the Department of Economics at Carleton College ran a pilot project using the Quantitative Literacy and Reasoning Assessment (QLRA) as a pre-course diagnostic tool. Results show that the QLRA predicts student grades even after controlling for other SAT/ACT math scores and overall GPA. This finding suggests that quantitative reasoning is an important input into success in Principles of Economics (both Macro and Micro). When the QLRA alone is used to predict success in a course (as defined by either a grade of C- or better, or a grade of B- or better), we find that we could nearly always pick out students who were on the way to sub-par performance. On the other hand, the tool has a fairly high false positive rate; almost half of students identified as “at risk” based on QLRA performance went on to earn a successful grade in the course. In total, we argue that the QLRA may be a useful and inexpensive early-warning device for introductory courses in economics; it may be worth exploring a similar use of the instrument in other disciplinary settings where introductory courses require quantitative reasoning.
Grawe, Nathan D., and Kristin O'Connell.
"Using the Quantitative Literacy and Reasoning Assessment (QLRA) for Early Detection of Students in Need of Academic Support in Introductory Courses in a Quantitative Discipline: A Case Study."
Numeracy 11, Iss. 1 (2018): Article 5.
Available at: http://scholarcommons.usf.edu/numeracy/vol11/iss1/art5
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