How Hands-On Coding Can Benefit Future Computer Science Teachers

Chanel Belvin, Elaine Anita De Melo Gomes Soares, Toni Dunlap, James Newland
Expanding Pathways in Computing,
Texas Advanced Computing Center,
University of Texas at Austin

Introduction

• To broaden participation in computing (BPC), we need more high-quality, certified Computer Science (CS) teachers to help build the high-performance computing pipeline (HPC).
• Teacher certification exams focus heavily on abstract pseudocode, which is disconnected from the practical, hands-on coding skills needed for effective classroom teaching.
• We’ve observed that pseudocode skill alone is insufficient. Teachers struggle with the exam and lack the confidence to teach programming concepts.
• How can we adjust teacher training to bridge the gap between theory and practice, building both confidence and competence?

Previous Training Model

Live Python Sessions

• Synchronous, live instruction using CMU CS Academy.
• Builds practical, hands-on programming skills.

Exam-Specific Training

• Targeted course on exam-style pseudocode.
• Self-paced “Foundations of CS” course with videos and practice covering all exam topics.

In-Person Cert Prep & Practice Tools

• A dynamic two-day, in-person certification workshop.
• Final practice packet with additional review tools before the exam.
• Focused on interpreting and using pseudocode in context.
• Many practice questions employ multiple pseudocode components such as loops, arrays, conditionals, and data types.

Improved Training Model

Positive Impact of Live Coding

• The addition of live coding experiences in the CS learning process positively impacts learners.²

Foundational Learning

• We now begin with a “Pseudocode Bootcamp” to teach coding concepts directly within the exam’s format.
• This allows for more extensive time on foundational principles before introducing a specific programming language.

Strengthened Cohort Model

• A new in-person orientation builds strong peer-to-peer support networks from the very beginning.
• Each cohort is guided by a dedicated instructional leader who shares best practices and resources.

Teacher Readiness & Support

• Practical programming language training is now offered after certification to focus specifically on classroom readiness.
• For teachers needing a retake, we offer “Domain Nights,” gamified review sessions that target specific areas for improvement.

Participant Feedback

Future Research

• Analysis of the role of pseudocode in teacher attitudes and self-efficacy.
• Report on the effect of certification exam use of mathematical thinking on teacher success.
• Evaluation of the former training model versus the improved training model.

References

• ¹Saupp, B., Macmillan, E., Mayfield, C., Johnson, C., Stewart, M. C., & Hodges, S. (2025). Praxly: An Online IDE for the Praxis CS Test Pseudocode. SIGCSE TS 2025 – Proceedings of the 56th ACM Technical Symposium on Computer Science Education, 2, 1613–1614. https://doi.org/10.1145/3641555.3705231
• ²Raj, A. G. S., Patel, J. M., Halverson, R., & Halverson, E. R. (2018, November 12). Role of live-coding in learning introductory programming. ACM International Conference Proceeding Series. https://doi.org/10.1145/3279720.3279725

STEM student retention can be enhanced through mentorship & research experiences.

Click here for the page related to this project.

Enhancing CT Attitudes in Physics Students by Code-based Model-building

Visit the project page for lots more information, including code and references.

Impacting Student Attitudes About CS Through Coding Integration into Science

In this session, participants will learn how integrating computer science (CS) pedagogy into science and math classes can positively impact students’ attitudes about computer science. When students experience lessons in science and math classes that incorporate computer science (CS), they feel more confident about CS. They are more likely to pursue academic pathways that include CS. Participants will see examples of specific lessons that can be used in science and math courses to improve students’ attitudes about the use of computer science (CS) in non-CS STEM courses.

Participants will use STEMcoding during the session and can access STEMcoding for free for one year.

If you are interested in more academic details about the STEMcoding study, click here.

Improving Student Computational Thinking Outcome Expectancy through Integrating Model-building in a Secondary Physics Course

The role of computation in physics is increasing in both a professional and educational context. High school physics pedagogy should incorporate computational thinking, data science, and computer science skills to bolster learning across these domains. The study described here shows that when students build physically meaningful models using computer programming in a physics course, their outcome expectancy is positively impacted. Outcome expectancy can predict students’ future academic and professional choices. The STEMcoding platform asks students to construct models using Euler-Cromer step-wise modeling of physical phenomena, such as those described by the laws of motion. The study presented had students use STEMcoding during the kinematics portion of Advanced Placement Physics 1 classes at a large, urban high school in southeast Texas. A moderate positive impact on outcome expectancy was found across various groups in the study population. Results show how computational essays can provide scaffolded learning while allowing students to use code to create interactive models, allowing knowledge construction in a domain-specific manner.

Data Science Pedagogy can Transform how Astronomy Students Construct Knowledge.

If you are attending the Data Science Education K12 Conference in San Antonio, talk to me about data science education in astronomy classes during the individual showcases. Be sure to visit the website for my poster!

Modern astronomy research requires computation and data science tools, which are not traditionally part of the Astronomy 101 course. This computational essay describes what a spectral energy distribution (SED) is and uses code to construct one. Although existing computational thinking tools like spreadsheets can be leveraged to allow computational thinking to construct science knowledge, the needs of a student or teacher astronomical researcher will likely go beyond introductory tools. The goal of the SED computational essay presented is not only to inform about SEDs and their use but also to be a part of a toolset meant to allow students and teachers to interrogate other stellar photometric datasets at scale. This notebook was designed by alumni of the NASA/IPAC Teacher Archive Research Project (NITARP). The code can be found on GitHub. This presentation is in the iPoster format, as seen at this link.

More details about this project are available.

https://tinyurl.com/CASTPivotData

Eric Friberg and Jimmy Newland share how data science can help drive science learning through analyzing real-world phenomena.

Visit the resources page for lots of content, including slides from the training.

Astronomy datasets can be hard to use for high school astronomy classes. Data science education pedagogy can be leveraged to create astronomy activities in which students interrogate data, create visuals, and use statistical thinking to construct astronomy knowledge. This session describes how the NASA/IPAC Infrared Science Archive (IRSA) can provide a web-based interface for students to use basic data science techniques in astronomy to build data literacy while learning astronomical concepts. The activities shared will be available for anyone but were designed to be used in astro 101 classes in high school or early college.