An official website of the United States government
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.
more »
« less
This content will become publicly available on June 1, 2026
The Landscape of State and National K-12 Computer Science Learning Standards (Fundamental)
Introduction: Learning standards are a crucial determinant of computer science (CS) education at the K-12 level, but they are not often researched despite their importance. We sought to address this gap with a mixed-methods study examining state and national K-12 CS standards. Research Question: What are the similarities and differences between state and national computer science standards? Methods: We tagged the state CS standards (n = 9695) according to their grade band/level, topic, course, and similarity to a Computer Science Teachers Association (CSTA) standard. We also analyzed the content of standards similar to CSTA standards to determine their topics, cognitive complexity, and other features. Results: We found some commonalities amidst broader diversity in approaches to organization and content across the states, relative to the CSTA standards. The content analysis showed that a common difference between state and CSTA standards is that the state standards tend to include concrete examples. We also found differences across states in how similar their standards are to CSTA standards, as well as differences in how cognitively complex the standards are. Discussion: Standards writers face many tensions and trade-offs, and this analysis shows how – in general terms – various states have chosen to manage those trade-offs in writing standards. For example, adding examples can improve clarity and specificity, but perhaps at the cost of brevity and longevity. A better understanding of the landscape of state standards can assist future standards writers, curriculum developers, and researchers in their work.
more »
« less
- Award ID(s):
- 2311746
- PAR ID:
- 10643578
- Publisher / Repository:
- ASEE Conferences
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The Computer Science Teachers Association (CSTA) K-12 Standards were last updated in 2017, when only six states in the United States had adopted learning standards for primary and secondary education (K-12) computer science. Fast forward to 2024, and 41 states now have K-12 CS standards (and one has high school CS standards only). In preparation for writing an updated set of standards, CSTA is engaging in three stages of work: reimagining CS for high school students, conducting a crosswalk of K-12 CS standards across all 50 states compared to the CSTA standards (2017), and engaging in the technical process of defining final standards content via research and revision. All three stages draw significantly from the community of practitioners, researchers, curriculum designers, postsecondary faculty, and other interested parties. They also draw significantly from research published since the last revision to take into account the current evidence on learning computer science. In this poster, we describe our process for building the groundwork of knowledge for revising the standards, share highlights of the results to date, and describe how this data will be used to inform the upcoming revision of the CSTA standards.more » « less
-
In the United States, state learning standards guide curriculum, assessment, teacher certification, and other key drivers of the student learning experience. Investigating standards allows us to answer a lot of big questions about the field of K-12 computer science (CS) education. Our team has created a dataset of state-level K-12 CS standards for all US states that currently have such standards (n = 42). This dataset was created by CS subject matter experts, who - for each of the approximately 10,000 state CS standards - manually tagged its assigned grade level/band, category/topic, and, if applicable, which CSTA standard it is identical or similar to. We also determined the standards' cognitive complexity using Bloom's Revised Taxonomy. Using the dataset, we were able to analyze each state's CS standards using a variety of metrics and approaches. To our knowledge, this is the first comprehensive, publicly available dataset of state CS standards that includes the factors mentioned previously. We believe that this dataset will be useful to other CS education researchers, including those who want to better understand the state and national landscape of K-12 CS education in the US, the characteristics of CS learning standards, the coverage of particular CS topics (e.g., cybersecurity, AI), and many other topics. In this lightning talk, we will introduce the dataset's features as well as some tools that we have developed (e.g., to determine a standard's Bloom's level) that may be useful to others who use the dataset.more » « less
-
K-12 computer science (CS) teachers are often the only teachers of the subject at their school. Many school-based administrators and personnel lack the content knowledge to support their ongoing professional growth. How then can an ecosystem of support be developed to support K-12 CS teachers? We have created several tools aligned to the CSTA Standards for CS Teachers that support administrators, instructional specialists, and teacher leaders to provide evidence-based feedback and promote the ongoing development of CS teachers at their schools. These tools, including a CS coaching toolkit and instructional practice evidence guide, have the potential to drive impactful, job-embedded development.more » « less
-
This report examines the similarities and differences between state-adopted K-12 CS standards and the 2017 CSTA K-12 Standards. This analysis includes basic information about the standards (such as counts by state and level) and their cognitive complexity, as well as more detailed information about their relationship to the CSTA standards.more » « less
