Academic Foundations
Assessment
Using Embedded Assessment
1
Current Data
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Dr. Khaleel student survey data
Dr. Hardt student survey data
Anecdotal data regarding student, faculty
and administrative perceptions of general
education
2
Program Assessment and Revision
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Objectives defined
Essential components:
Skills development and application
Cultural development
Intellectual growth and development
Bloom’s Taxonomy of Thinking
3
Program Assessment and Revision
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Five categories developed
Student outcomes set for each category
Assessment matrix developed to:
A) Assess proposed courses for
appropriateness
B) Enable course developers to ensure that
their course meets Academic Foundation
objectives and that they have appropriate
assessment methods in place
4
Assessment of Student Outcomes
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Tool or tools are needed in order to assess
student-based outcomes
Need to assess outcomes from individual
Academic Foundations courses as well as
long term program outcomes
Many tools exist, none may be perfect
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Embedded Assessment
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Type I
Data gathered on individual performance in
Academic Foundations courses
Since courses accepted in Academic Foundations
were subject to rubric-based approval, overall
student performance in these courses is a valid
contributor to program assessment
Student performance in classes could be analyzed
as a function of gender, age, ethnic background,
academic major etc….
6
Embedded Assessment
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Type II
Data gathered from standardized tools used in Academic
Foundations courses
A) Assesses Academic Foundations outcomes
specific for that subcategory
B) Standardized within the subcategory for
consistency
C) Designed by faculty within the subcategory
D) Yields quantifiable data
E) Contributes to course grade
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Embedded Assessment
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Type III
Data gathered from standardized tools used in upper division
majors classes
A) Cumulative assessment of all Academic
Foundation objectives
B) Standardized university-wide for consistency
C) Could be administered in whole or in parts
D) Quality of participation contributes to course
grade
E) Subcategories evaluated by different
departments
F) Evaluation need not be done that semester
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Advantages
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The approach provides more than one source of
data
The approach fosters broad faculty involvement
The approach utilizes an existing framework of
courses with no need for out of class assessment
The approach creates a likelihood for students
being motivated to participate
The approach will yield data that is quantitative
The approach is amenable to many formats (i.e.
assessing every student in every course or
assessing courses on an alternating basis)
The tools used could be comprehensive, or subdivided into subtests to avoid being too
cumbersome.
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Disadvantages
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Designing the appropriate tools could be
difficult
Convincing faculty to participate with
enthusiasm could be a challenge
Do such tools assess ongoing skills and
understanding?
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Faculty committees identify the most appropriate matrix
items for their subcategory
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Academic Foundations Committee ensures that all
Academic Foundation matrix areas are covered
Global Academic Skills
Mathematics English
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Info. Literacy
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Natural Sciences
Life Sci Physical Sci
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Social Sciences History & Cultural Diversity Arts & Humanities
Social Sciences
History
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Cultural
Cultural Diversity
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Arts
Humanities
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Faculty Committees Design Subcategory Tools
Using Academic Foundations Objectives,
Matrix Outcomes and Bloom’s Taxonomy
Matrix
1 __________
2 __________
3 __________
4 __________
5 __________
6 __________
Subcategory-specific
Academic Foundations
Objectives
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Ten Embedded Assessment Tools Provide
Assessment Data for Academic Foundations Courses
Global Academic Skills
Mathematics English
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Info. Literacy
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Natural Sciences
Life Sci Physical Sci
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Social Sciences History & Cultural Diversity Arts & Humanities
Social Sciences
History
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Cultural
Cultural Diversity
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Arts
Humanities
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