During the 2010 – 2011 academic year, faculty coordinating College Algebra and Statistics for Everyday Life (SEL) performed an assessment of students’ ability in quantitative reasoning and mathematical reasoning. The following five learning outcomes were measures using purposefully designed or identified questions from course exams.
|Learning Outcomes: Statistics for Everyday Life||Learning Outcomes: College Algebra|
|Express and manipulate mathematical information, concepts, and thoughts in verbal, numeric, graphical and symbolic form while solving a variety of problems.||
Develop and interpret mathematical models such as formulas, graphs, tables, and schematics, and draw conclusions and/or inferences from them.
|Apply appropriate quantitative skills to analyze, evaluate and determine the best feasible solution to real-world problems||Apply (a) arithmetic, (b) algebraic, (c) geometric, and (d) other methods to modeling and solving real world problems.|
|Interpret data and results to make informed decisions||Represent and evaluate basic mathematical information graphically and symbolically.|
|Apply quantitative reasoning and mathematics to situations in everyday life||Apply mathematical reasoning skills to develop convincing mathematical arguments.|
|Solve multiple-step problems through different (inductive, deductive and symbolic) modes of reasoning||Solve multiple-step problems through different (inductive, deductive and symbolic) modes of reasoning.|
All SEL multiple-choice questions in the test-bank were mapped to one or more of the five learning outcomes. For each of the five course exams, the percent correct for each question was recorded by learning outcome. Results from the F2010 semester led to improvements in some test questions for the S2011 course offering.
College Algebra faculty designed open-ended test questions for each of the five learning outcomes and a standard five-level scoring rubric was applied to all mid-term and final exams. A score of 3 or 4 on any individual question was defined as meeting the learning outcome while a score of 0, 1, or 2 was considered unacceptable. The percent of students performing at an acceptable level was recorded and compared to the percent of students receiving a course grade of C- and above, the necessary criteria to enroll in the “next” math course. Results from the F2010 semester led to more rigorous grading policies and an automated homework system necessitating multiple efforts to reach a correct solution.