2025 ASEE Annual Conference & Exposition

Introduction: State and national learning standards play an important role in articulating and standardizing K-12 computer science education. However, these standards have not been extensively researched, especially in terms of their cognitive complexity. Analyses of
cognitive complexity, accomplished via comparison of standards to a taxonomy of learning,
can provide an important data point for understanding the prevalence of higher-order versus lower-order thinking skills in a set of standards.

Objective: The objective of this study is to answer the research question: How do state and
national K-12 computer science standards compare in terms of their cognitive complexity?

Methods: We used Bloom’s Revised Taxonomy in order to assess the cognitive complexity of
a dataset consisting of state (n = 9695) computer science standards and the 2017 Computer
Science Teachers Association (CSTA) standards (n = 120). To enable a quantitative
comparison of the standards, we assigned numbers to the Bloom’s levels.

Results: The CSTA standards had a higher average level of cognitive complexity than most
states’ standards. States were more likely to have standards at the lowest Bloom’s level than
the CSTA standards. There was wide variety of cognitive complexity by state and, within a
state, there was variation by grade band. For the states, standards at the evaluate level were
least common; in the CSTA standards, the remember level was least common.

Discussion: While there are legitimate critiques of Bloom’s Revised Taxonomy, it may
nonetheless be a useful tool for assessing learning standards, especially comparatively. Our
results point to differences between and within state and national standards. Recognition of
these differences and their implications can be leveraged by future standards writers,
curriculum developers, and computing education researchers to craft standards that best meet the needs of all learners.