The CT in STEM Skills Taxonomy Framework
Our taxonomy is broken down into four major categories: Data Skills, Modeling and Simulation Skills,
Computational Problem Solving Skills, and Systems Thinking Skills. Presenting our taxonomy as a set of
distinct categories and skills may give the impression that each entry is independent. On the contrary, the
skills are interrelated and dependent on one another. In practice, skills are often used in conjunction to
achieve a specific STEM goal. Here we present each category, give a brief description, and list each of its
constituent skills in a table that includes what mastery of that skills looks like (Tables 1-4).
Data Skills
Data lie at the heart of all STEM fields. They serve many purposes, take many forms, and play a variety of
roles in the course of conducting STEM inquiry. In modern STEM fields computers play a central role in
the efficient collection, production, manipulation, and communication of data. Some researchers spend
entire careers studying how to use computers to most effectively meet the growing demands of data-driven
sciences. CT skills are used in all facets of data-related STEM work from the initial data collection phase
all the way through drawing conclusions and sharing findings.
Table 1: The five skills that comprise the Data Skills category.
Collecting Data
Creating Data
Manipulating Data
Analyzing Data
Visualizing Data
Students who have mastered these skills will be able to:
…propose systematic data collection protocols and articulate how that protocol could be
automated with computational tools when appropriate.
…define computational procedures and run simulations that produce a set of data that
can be used to answer a specific STEM question or advance their understanding of the
topic under investigation.
…manipulate a given dataset using computational tools in order to get the data into a
desired configuration using strategies such as sorting, filtering, and standardizing.
…analyze a given set of data and make claims and draw conclusions based on the
finding from their analysis.
…use common computational tools to produce visualizations that convey information
gathered during analysis.
1
Modeling and Simulation Skills
The use of computational models and simulations is a central practice in the investigation of STEM
phenomena. When speaking of computational models and simulations we are referring to non-static
representations of phenomena that can be executed with a computer. In this section we will use the term
‘computational model’ and intend for it to be inclusive of all variety of computational models and
simulations ranging from simple interactive models that support inquiry up to sophisticated computer
programs that run on supercomputers and lack a graphical component. Computational tools make the
practice of modeling and simulation possible on a scale that is not possible otherwise. They make it
possible to investigate questions and test hypotheses that would otherwise be too expensive, dangerous,
difficult or entirely not possible to carry out otherwise. However, computational models only approximate
reality, and it is important for students to be aware of their limitations.
Table 2: The five skills that comprise the Modeling and Simulation Skills category.
Using Computational
Models to Understand
a Concept
Using Computational
Models to Find and
Test Solutions
Assessing
Computational Models
Designing
Computational Models
Constructing
Computational Models
Students who have mastered these skills will be able to:
…advance their own understanding of a concept by interacting with a computational
model the demonstrates the concepts.
…find, test, and justify the use of a particular solution through the use of a
computational model as well as be able to apply the information gained through
using the model when appropriate.
…articulate the similarities and differences between a computational model and the
phenomenon that it is modeling, this includes raising issues of threats to validity as
well as identifying assumptions built into the model.
…design a computational model, this includes articulating what the components of
the model will be, how they interact, what data will be produced by the model, and
identify what implicit assumptions are being made by the proposed model.
…implement new model behaviors, either through extending an existing model or by
creating a new model either within a given modeling framework or from scratch.
2
Computational Problem Solving Skills
Conducting STEM research is a process of solving many overlapping problems that involves skills such as
utilizing existing tools, applying specific processes, finding new solutions, managing large amounts of data,
and reframing problems that cannot be solved within the constraints of the research endeavor. An essential
component of CT-STEM is the ability to take advantage of computational power in creative and productive
ways to solve the challenges confronted during STEM endeavors.
Table 3: The seven skills that comprise the Computational Problem Solving Skills category.
Preparing Problems for
Computational
Solutions
Programming
Choosing Effective
Computational Tools
Assessing Different
Approaches/Solutions
to a Problem
Developing Modular
Computational
Solutions
Creating
Computational
Abstractions
Troubleshooting and
Debugging
Students who have mastered these skills will be able to:
…employ a variety of problem solving strategies towards reframing a given problem
into a form where it can be solved, or at least progress can be made, through the
use of computational tools.
…understand, modify, and create computer programs that aid in various aspects of
STEM inquiry.
…articulate the pros and cons of using various computational tools and be able to
make an informed, justifiable decision at the outset of a STEM endeavor.
…assess different approaches/solutions to a problem based on the requirements
given, the constraints of the problem, and the available resources and tools.
…develop solutions that consist of modular, reusable components and take advantage
of the modularity of their solution both in working on the current problem as well
as reusing pieces of previous solutions when confronting new challenges.
…identify, create, and use computational abstractions as they work towards goals
within a STEM discipline.
…identify, isolate, reproduce then ultimate correct unexpected problems encountered
when working on a STEM problem, and do so in a systematic, efficient manner.
3
Systems Thinking Skills
STEM phenomena rarely involve individual objects acting in isolation; instead they can be understood as
systems that emerge from the interactions between elements that constitute the whole. Elements can be
anything from organisms in an ecosystem, to atoms in a molecule, or mechanical components of a car
engine. Many of the skills that we have described above are valuable for investigating systems, but this
section introduces CT skills that emphasize systems thinking.
Table 4: The five skills that comprise the Systems Thinking Skills category.
Investigating a
Complex System
as a Whole
Understanding the
Relationships
within a System
Thinking in
Levels
Communicating
Information about
a System
Defining Systems
and Managing
Complexity
Students who have mastered these skills will be able to:
…pose questions about, design and carry out investigations of, and ultimately interpret
and make sense of, the data gathered about a system as a single entity.
…identify the constituent elements of a system, articulate their behaviors, and explain
how interactions between elements produce the characteristic behaviors of the system.
…identify different levels of a given system, articulate the utility of each level with
respect to understanding the system, and be able to move back and forth between
levels, correctly attributing features of the system to the appropriate level.
…communicate information they have learned about a system in a way that makes the
information accessible to viewers who do not know the exact details of the system from
which the information was drawn.
…define the boundaries of a system so that they can then use the resulting system to as a
domain for investigating a specific question as well as identify ways to simplify an
existing system without compromising its ability to be used for a specified purpose.
4