Date of Award

5-2024

Degree Type

Thesis

Degree Name

Departmental Honors

Department

Mathematics and Statistics

Abstract

The design of effective assessments and reporting of a student’s achievement on learning objectives are often overlooked, leaving educational stakeholders lacking the ability to create meaningful evaluations. To assist in creating substantial mathematics assessments this work seeks to answer the following research questions: ‘How can educational taxonomies be utilized to improve the design of mathematics assessments’? and ‘What are the strengths and weaknesses of applying different taxonomies onto mathematics assessments?’. The purpose of this study is to (1) develop a practical design instrument for easier identification and categorization of assessment questions within each educational taxonomy structure and (2) evaluate the effectiveness of each classification process within the identified educational taxonomies for mathematics assessments, while outlining the suggested implications.

With a focus on three renowned taxonomies, Revised Bloom’s Taxonomy (RBT), Webb’s Depth of Knowledge (Webb’s DOK), Structured of Observed Learning and Outcome (SOLO), and a mathematics specific taxonomy, Cangelosi’s Learning Levels, four unit exams of a college algebra course were classified within each framework. Collected data resulted in a discussion of the advantages and disadvantages of each taxonomy’s application. Findings indicate Cangelosi’s Learning Levels are the most effective taxonomy for mathematics due to categories more appropriately describing the type of math problems and additionally providing a holistic connection between what is taught and assessed.

Aligning learning objectives and assessment questions allow for accurately measuring students’ knowledge, promoting different levels of critical thinking, and fostering deeper understanding. This study recommends the necessary leveraging instrument for designing assessments and suggests the most effective taxonomy for meaningful assessment practices in mathematics education.

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Faculty Mentor

Hannah Lewis

Departmental Honors Advisor

David Brown

Capstone Committee Member

Kady Schneiter