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iRubric: Quantitative Literacy VALUE Rubric
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Quantitative Literacy VALUE Rubric
built by
AACU_VALUE
Rubric Code:
V476CX
Quantitative Literacy (QL) – also known as Numeracy or Quantitative Reasoning (QR) – is a "habit of mind," competency, and comfort in working with numerical data. Individuals with strong QL skills possess the ability to reason and solve quantitative problems from a wide array of authentic contexts and everyday life situations. They understand and can create sophisticated arguments supported by quantitative evidence and they can clearly communicate those arguments in a variety of formats. Teaching QL requires us to design assignments that address authentic, data-based problems. Such assignments may call for the traditional written paper, but we can imagine other alternatives: a video of a PowerPoint presentation, perhaps, or a well designed series of web pages. In any case, a successful demonstration of QL will place the mathematical work in the context of a full and robust discussion of the underlying issues addressed by the assignment. Courtesy of AAC&U.
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Keywords:
Quantitative literacy, Interpretation, Calculation, Application, Analysis, Representation
Categories:
Subjects:
(General)
Types:
(Other)
Grade Levels:
Undergraduate, Graduate
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Quantitative Literacy VALUE Rubric
Capstone
4 pts
Milestone
3 pts
Milestone
2 pts
Benchmark
1 pts
Interpretation
Ability to explain information presented in mathematical forms (e.g., equations, graphs, diagrams, tables, words)
Capstone
Provides accurate explanations of information presented in mathematical forms. Makes appropriate inferences based on that information. For example, accurately explains the trend data shown in a graph and makes reasonable predictions regarding what the data suggest about future events.
Milestone
Provides accurate explanations of information presented in mathematical forms. For instance, accurately explains the trend data shown in a graph.
Milestone
Provides somewhat accurate explanations of information presented in mathematical forms, but occasionally makes minor errors related to computations or units. For instance, accurately explains trend data shown in a graph, but may miscalculate the slope of the trend line.
Benchmark
Attempts to explain information presented in mathematical forms, but draws incorrect conclusions about what the information means. For example, attempts to explain the trend data shown in a graph, but will frequently misinterpret the nature of that trend, perhaps by confusing positive and negative trends.
Representation
Ability to convert relevant information into various mathematical forms (e.g., equations, graphs, diagrams, tables, words)
Capstone
Skillfully converts relevant information into an insightful mathematical portrayal in a way that contributes to a further or deeper understanding.
Milestone
Competently converts relevant information into an appropriate and desired mathematical portrayal.
Milestone
Completes conversion of information but resulting mathematical portrayal is only partially appropriate or accurate.
Benchmark
Completes conversion of information but resulting mathematical portrayal is inappropriate or inaccurate.
Calculation
Capstone
Calculations attempted are essentially all successful and sufficiently comprehensive to solve the problem. Calculations are also presented elegantly (clearly, concisely, etc.)
Milestone
Calculations attempted are essentially all successful and sufficiently comprehensive to solve the problem.
Milestone
Calculations attempted are either unsuccessful or
represent only a portion of the calculations required to comprehensively solve the problem.
Benchmark
Calculations are attempted but are both unsuccessful and are not comprehensive.
Application / Analysis
Ability to make judgments and draw appropriate conclusions based on the quantitative analysis of data, while recognizing the limits of this analysis
Capstone
Uses the quantitative analysis of data as the basis for deep and thoughtful judgments, drawing insightful, carefully qualified conclusions from this work.
Milestone
Uses the quantitative analysis of data as the basis for competent judgments, drawing reasonable and appropriately qualified conclusions from this work.
Milestone
Uses the quantitative analysis of data as the basis for workmanlike (without inspiration or nuance, ordinary) judgments, drawing plausible conclusions from this work.
Benchmark
Uses the quantitative analysis of data as the basis for tentative, basic judgments, although is hesitant or uncertain about drawing conclusions from this work.
Assumptions
Ability to make and evaluate important assumptions in estimation, modeling, and data analysis
Capstone
Explicitly describes assumptions and provides compelling rationale for why each assumption is appropriate. Shows awareness that confidence in final conclusions is limited by the accuracy of the assumptions.
Milestone
Explicitly describes assumptions and provides compelling rationale for why assumptions are appropriate.
Milestone
Explicitly describes assumptions.
Benchmark
Attempts to describe assumptions.
Communication
Expressing quantitative evidence in support of the argument or purpose of the work (in terms of what evidence is used and how it is formatted, presented, and contextualized)
Capstone
Uses quantitative information in connection with the argument or purpose of the work, presents it in an effective format, and explicates it with consistently high quality.
Milestone
Uses quantitative information in connection with the argument or purpose of the work, though data may be presented in a less than completely effective format or some parts of the explication may be uneven.
Milestone
Uses quantitative information, but does not effectively connect it to the argument or purpose of the work.
Benchmark
Presents an argument for which quantitative evidence is pertinent, but does not provide adequate explicit numerical support. (May use quasi-quantitative words such as "many," "few," "increasing," "small," and the like in place of actual quantities.)
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