Computing Education Things

In this episode, Kevin Lin (UW) and I take a broader look at the relevance of data structures and algorithms in today’s real-world problems, the role of friction in learning, and the impact of AI at different levels. We also explore online versus in-person teaching in the context of DSA, address common misconceptions, and discuss Kevin’s approach to teaching the subject. Finally, we touch on how he is reimagining computing education. This was a blast, enjoy!

Mentions during the episode:

• CSE 373 Spring 2026
• Visual Data Structures Cheat-Sheet
• BRIDGES
• Josh Hug 
• CS 61B Spring 2026 
• AlgoMasterIO
• CS 1332 Data Structures & Algorithms Visualization Tool
• LeetCode
• GeeksforGeeks
• COMPSCI 201 - Duke
• Owen Astrachan 
• Mark Liffiton
• Artturi Tilanterä dissertation 
• The Computer Always Wins 
• Reimagining CS Education 

The Computing Education Things deep dives relevant to this episode:

• #35 — In Defense of Software Craftsmanship
• #36 — Delegating Understanding
• #39 — Is a Computing Degree Still Valuable?

Where to find Kevin:

• Website 
• Faculty Webpage
• GitHub

In this episode, I spoke with Borja Sotomayor, a Senior Instructional Professor in the Department of Computer Science at the University of Chicago and former Director of the MPCS, about how computer science departments support their teaching activities. We explored topics such as balancing teaching and research priorities, managing large classes, onboarding new faculty, the role of mentorship, evaluating teaching effectiveness, and the need for more structured support for CS educators.

Mentions during the episode:

• Study  
• UChicago CS
• What Does an Instructional Designer Do?
• Transforming College Teaching Evaluation
• TEval
• Extracurricular Activities Predict CS Internship Attainment (Paper)
• Chicago Center for Teaching and Learning
• Revisit: How I organize the teaching staff of my 200+ student class
• SIGCSE TS 2026
• Jennifer Frey
• Jen Heemstra
• Labwork to Leadership
• Using Two-Stage Exams to Promote Active Learning in Large Classes
• Faculty Perceptions of Teaching in Undergraduate Computer Science Education
• Some ABCs of Teaching

Where to find Borja:

• Website
• LinkedIn
• Google Scholar

About this episode

Danny Yellin worked for IBM for 35 years and managed many large software R&D teams, including IBM Research, Mobile offerings and IBM Cloud. He has a PhD in Computer Science from Columbia University and is currently a Faculty Lecturer at Reichman University in Israel. We talked about his proposed curriculum on LLMs for software engineering, how colleges should approach AI in their curricula, the different views on computing education for the AI era and the use of AI in teaching CS.

Where to find Danny:

LinkedIn: https://www.linkedin.com/in/danny-yellin-9878154/

Google Scholar: https://scholar.google.com/citations?user=qfEhHz8AAAAJ&hl=es

References:

Software Engineering and Large Language Models: What university students need to know: https://www.linkedin.com/pulse/software-engineering-large-language-models-what-students-danny-yellin-k0rne/?trackingId=L6%2F1I7QHqhO8TMbzQyZ8xA%3D%3D

Syllabus for "LLMs for Software Engineering": https://www.linkedin.com/feed/update/urn:li:activity:7401911808388923392/?originTrackingId=0yc3qIrmmwx%2FGuhayEFqWg%3D%3D

AI Tools for Software Development (Carnegie Mellon): https://ai-developer-tools.github.io

College of Computing and Artificial Intelligence (UW-Madison): https://cai.wisc.edu/

Learn AI-Assisted Python Programming: https://www.manning.com/books/learn-ai-assisted-python-programming-second-edition

Beyond Vibe Coding with Addy Osmani: https://youtu.be/dHIppEqwi0g?si=a9NvIRRcDLFFG_tY

The Minimum Every Developer Must Know About AI Models (No Excuses!): https://blog.kilo.ai/p/minimum-every-developer-must-know-about-ai-models

About the podcast:

You can watch the full episode on YouTube. Or listen to it on Spotify, Apple Podcasts, or your podcast app of choice.

If you’ve enjoyed this episode, you can listen to more on Computing Education Things’s website: https://computingeducationthings.transistor.fm/

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Thanks for listening!

Ibrahim Albluwi is an Associate Professor in the Department of Computer Science at Princess Sumaya University for Technology in Jordan. In this episode, we talk about his new ACM TOCE paper, "Varying Program Input to Assess Code Reading Skills," and the question of how we can evaluate code readability and comprehension in intro CS.

In this episode, you'll learn:

- The importance of code reading skills
- Understanding code: tracing vs. explanation
- Variable Input Tracing: a new approach
- Teaching code reading skills
- The role of AI in code reading
- Debugging and its importance

About the guest

Ibrahim is an Associate Professor of Computer Science at Princess Sumaya University for Technology in Amman, Jordan. His research interests focus on computer science education. He is primarily interested in improving our understanding of how to effectively teach and assess programming, algorithms, data structures, and problem-solving.

Where to find Ibrahim

Website: https://ialbluwi.github.io/index.html

Linkedin: https://www.linkedin.com/in/ibrahim-albluwi-3898b1b/

Google Scholar: https://scholar.google.com/citations?user=RQxMuuv3_W4C&hl=en

Papers referenced:

Varying Program Input to Assess Code Reading Skills https://dl.acm.org/doi/10.1145/3737884

Other referenced:

Vibe Coding Among CS Students https://medium.com/@dannyprol/vibe-coding-among-cs-students-68a8861df436

Code Reading Club: https://codereading.club/

About the podcast

You can watch the full episode on Youtube. Or listen to it on Spotify, Apple Podcasts, or your podcast app of choice. Thanks for listening!

Catch Up on Computing Education Things Newsletter: https://computingeducationthings.substack.com/

About this Episode

In today’s episode, I sit down with Naaz Sibia, a CS PhD student from University of Toronto, to explore the role of visualization tools in computing education. Naaz helps us understand the concept of visualization for CS Education, shares how visualizations can be applied to learn different themes of computing concepts, and we explore how she has navigated the challenges that face CS educators and curriculum design.

In this episode, you'll learn:

- The importance of visualization in computing education
- Engagement and teaching methods in visualization
- Applications of visualization in algorithms and data
- Cognitive load and attention in learning
- Enhancing online learning with visual tools
- Critical thinking and problem solving through visualization

About the guest

Naaz Sibia is a Ph.D. student in Computer Science at the University of Toronto (UofT), co-supervised by Professors Carolina Nobre and Michael Liut. Her research focuses on using interactive visualizations to improve introductory programming education, enhancing student belonging and engagement online, and exploring the role of reflective prompts in flipped classrooms.

Where to find Naaz Sibia

Website: https://www.naazsibia.com/

Linkedin: https://www.linkedin.com/in/naaz-sibia/

Google Scholar: https://scholar.google.ca/citations?user=r60zG3UAAAAJ&hl=en&oi=ao

References

Papers referenced:

Exploring the Role of Visualization Tools in Enhancing Computing Education: A Systematic Literature Review https://scholar.google.ca/citations?view_op=view_citation&hl=en&user=r60zG3UAAAAJ&sortby=pubdate&citation_for_view=r60zG3UAAAAJ:_kc_bZDykSQC

SQLVis paper: https://ieeexplore.ieee.org/document/9576431

Tools referenced:

https://algorithm-visualizer.org/

https://blog.levelupcoding.com/

About the podcast

You can watch the full episode on Youtube. Or listen to it on Spotify, Apple Podcasts, or your podcast app of choice. Thanks for listening!

Catch Up on Computing Education Things Newsletter: https://computingeducationthings.substack.com/

In today’s episode, I sit down with Matthias Hauswirth, Associate Professor at USI (Università della Svizzera italiana), where he leads LuCE, the Lugano Computing Education research lab. Matthias and his team built a great site to learn programming with graphics. It's called PyTamaro, and Matthias is here to tell us about it.

In this episode, you'll learn:

00:00 Introduction to PyTamaro and Graphics in Programming
02:38 Engaging Students Through Graphics and Programming
06:00 The Importance of Problem Decomposition
08:56 Teaching Programming Concepts with PyTamaro
11:46 How to use PyTamaro to teach Python programming
14:42 Exploring the PyTamaro Library and Activities

About the guest

Matthias Hauswirth is an Associate Professor at the Università della Svizzera italiana (USI) in Lugano, Switzerland, where he leads LuCE, the Lugano Computing Education research lab at the Software Institute. His research focuses on how people learn to program. His recent work includes ExpressionTutor.org for teaching expression evaluation; PyTamaro for teaching python programming with graphics; and Progmiscon.org, a comprehensive inventory of programming misconceptions.

Where to find Matthias Hauswirth

Website: https://hauswirth.github.io/
Google Scholar: https://scholar.google.com/citations?user=rIy8rOoAAAAJ&hl=en

References

PyTamaro: https://pytamaro.si.usi.ch/

About the podcast

You can watch the full episode on Youtube. Or listen to it on Spotify, Apple Podcasts, or your podcast app of choice. Thanks for listening!

Catch Up on Computing Education Things Newsletter: https://computingeducationthings.substack.com/