Can High Fail Rates be Reversed? Attending to Literacy Needs in Foundation Economics

Document Type

Conference Proceeding

Publisher

University of Melbourne

Faculty

Faculty of Business and Law

School

School of Accounting, Finance and Economics / Centre for Innovative Practice

RAS ID

12737

Comments

Giles, M. J., Yong, J. L., & Harris, A. J. (2011). Can High Fail Rates be Reversed? Attending to Literacy Needs in Foundation Economics. Paper presented at The Quantitative Analysis of Teaching and Learning in Higher Education in Business, Economics and Commerce. University of Melbourne. Melbourne, Victoria.

Abstract

Poor pass rates in foundation economics at Edith Cowan University over many semesters have often been blamed on low entry standards and lack of numeracy skills. Little attention has been given to the nexus, or lack thereof, of conceptual learning and understanding of economics with overall literacy competency. The hypothesis is that the poor exam performance of foundation economics students is less to do with lack of knowledge and understanding of economics and more to do with the pervasive inability of first year students to undertake assessment tasks that require good literacy skills. To test this hypothesis, the unit coordinators for the foundation economics unit at Edith Cowan University in Semester One 2010 instituted some reforms. First, they added four short answer pop quizzes with no assessment marks attached. Next, they offered on-campus students voluntary literacy workshops. Finally, the 25% assignment was split into a plan (due week 8 and worth 5%) and a report (due week 10 and worth 20%). Subsequently, the fail rates fell from 32% in Semester Two 2009 to 17% in Semester One 2010 on the Joondalup campus and from 24% to 2%, respectively, on the Mount Lawley campus. Performance (final and exam marks) models are estimated using demographic and course information, pop quiz participation, and plan and report marks as variables of influence. The paper will report on those regressors that are statistically significant as well as the fit of the model.

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