Fred G. Martin

Spr '24 Office Hours
Mon 2:45p-3:45p
NPB 3.108D Main Campus
Tue 2p-3p Thu 11a-12n
San Pedro I Downtown
pls. email for appointment :)

Hi, everyone! I am a professor of computer science at the Univerity of Texas at San Antonio. I’m also chair of the department. I started at UTSA on August 1, 2023.

Chair’s Welcome. It is my privilege to serve the Computer Science department as chair. I strive that all have the opportunity to pursue their professional success and to foster an environment where all are respected and valued members of our community.

Future and prospective students (and parents and other family members): please see my FAQs about our undergraduate and graduate programs.

My Research. I create novel technologies to empower young learners as designers with computing, artificial intelligence, and data science. My students and I have designed robotics construction kits, a collaborative data visualization platform, and a virtual reality programming environment.

I work with K–12 teachers and school districts to bring these materials to their students. I collaborate with faculty in computer science, education, and psychology to create new technologies and curricula, and to study how people learn when using them.

My team is called the Engaging Computing Group—see our people, projects, and publications.

My Teaching. In Spring 2024, I am teaching a research-oriented independent study topics class, CS 4913 Developing AI Tools for Children.

Before UTSA, I was at the University of Massachusetts Lowell. I started in 2002 as an asst. prof. of computer science. After tenure, I did a lot of administrative service, including being an associate dean. Here’s my web page there.

selected publications

* = student under my supervision

DoodleIt: A Novel Tool and Approach for Teaching How CNNs Perform Image Recognition

Vaishali Mahipal*, Srija Ghosh*, Ismaila Temitayo Sanusi*, and 3 more authors

In Proceedings of the 25th Australasian Computing Education Conference, 2023

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To introduce middle school students to key concepts in image recognition, we created an interactive web application that performs sketch recognition and an afterschool curriculum for its use. Our app, called DoodleIt, was inspired by Google’s Quick, Draw!, and makes use of its accompanying open-source sketch library. With DoodleIt, students make simple line drawings on a canvas area and a previously-trained convolutional neural network (CNN) identifies the object drawn. The application dynamically visualizes the different layers that are involved in the process of CNNs, including a display of kernels, the resulting feature maps, and the percentage of match at output neurons. We used DoodleIt in an 18-hour curriculum to introduce middle school students to artificial intelligence, machine learning, and data science. Four hours of content were related to image recognition and the ethics of using AI. Here, we describe the design of the DoodleIt application, the approach we used to introduce the associated ideas to the students, and how we assessed student learning. Qualitative data collected from students are presented and discussed. Our findings indicate that students were able to understand the functionality of the kernels and feature maps involved in the CNN to perform rudimentary image recognition.
Envisioning AI for K-12: What Should Every Child Know about AI?

David Touretzky, Christina Gardner-McCune, Fred Martin, and 1 more author

Proceedings of the AAAI Conference on Artificial Intelligence, Jul 2019

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The ubiquity of AI in society means the time is ripe to consider what educated 21st century digital citizens should know about this subject. In May 2018, the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA) formed a joint working group to develop national guidelines for teaching AI to K-12 students. Inspired by CSTA’s national standards for K-12 computing education, the AI for K-12 guidelines will define what students in each grade band should know about artificial intelligence, machine learning, and robotics. The AI for K-12 working group is also creating an online resource directory where teachers can find AI- related videos, demos, software, and activity descriptions they can incorporate into their lesson plans. This blue sky talk invites the AI research community to reflect on the big ideas in AI that every K-12 student should know, and how we should communicate with the public about advances in AI and their future impact on society. It is a call to action for more AI researchers to become AI educators, creating resources that help teachers and students understand our work.
Computational Thinking for Youth in Practice

Irene Lee, Fred Martin, Jill Denner, and 5 more authors

ACM Inroads, Feb 2011

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Computational thinking (CT) has been described as the use of abstraction, automation, and analysis in problem-solving [3]. We examine how these ways of thinking take shape for middle and high school youth in a set of NSF-supported programs. We discuss opportunities and challenges in both in-school and after-school contexts. Based on these observations, we present a "use-modify-create" framework, representing three phases of students’ cognitive and practical activity in computational thinking. We recommend continued investment in the development of CT-rich learning environments, in educators who can facilitate their use, and in research on the broader value of computational thinking.