publications

2023

1. AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

   Duri Long, Jessica Roberts, Brian Magerko, and 3 more authors

   In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

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   Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy.

2. Creating Apps for Community and Social Good: Learning Outcomes of a Culturally Responsive Middle School Computer Science Curriculum

   Lijun Ni, Gillian Bausch, Elizabeth Thomas-Cappello, and 2 more authors

   In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 2023

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   This study examined student learning outcomes from a culturally responsive middle school computer science (CS) curriculum. The curriculum is based on students creating mobile apps serving community and social good. Two sets of data were collected from 294 students in three urban districts: (1) pre- and post- survey responses on their attitudes toward learning CS and creating culturally responsive apps; (2) the apps created by those students. The analyses of student apps indicated that students were able to create basic apps that connected with their personal interests, life experiences, class community, and the larger society. Paired sample t-tests of pre- and post- survey results indicated that students were significantly more confident in coding and creating community-focused apps after completing the course, regardless of gender and race. However, their interest in solving coding problems and continuing to learn CS decreased afterward. Analyses of students’ attitudes by gender, grade, and race showed significant differences among some of those groups. Seventh grade students rated more positively on their attitudes than eighth graders. Students of different racial groups indicated significantly different attitudes, especially the Southeast Asian and African American groups. Male students also reported stronger confidence and interest and more positive attitudes overall than female students.

3. 

   Developing Machine Learning Algorithm Literacy with Novel Plugged and Unplugged Approaches

   Ruizhe Ma, Ismaila Temitayo Sanusi*, Vaishali Mahipal*, and 2 more authors

   In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1, 2023

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   Data science and machine learning should not only be research areas for scientists and researchers but should also be accessible and understandable to the general audience. Enabling students to understand the details behind the technology will support them in becoming aware consumers and encourage them to become active participants. In this paper, we present instructional materials developed for introducing students to two key machine learning algorithms: decision trees and k-nearest neighbors. The materials were tested in a middle school’s afterschool artificial intelligence program with four participating students aged 12 to 13. A combination of hands-on activities, innovative technology, and intuitive examples facilitated student learning. With hand-drawn decision trees and penguin species classifications, students used the algorithms to solve problems and anticipate other possible applications. We present the technology used, curriculum materials developed, and classroom structure. Following the guidelines from AI4K12 and introducing foundational machine learning algorithms, we hope to foster student interest in STEM fields.

4. 

   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.

2022

1. Promoting Machine Learning Concept to Young Learners in a National Science Fair

   Ismaila Temitayo Sanusi*, Ilkka Jormanainen, Solomon Sunday Oyelere, and 2 more authors

   In Proceedings of the 22nd Koli Calling International Conference on Computing Education Research, 2022

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   There is a growing number of initiatives for teaching artificial intelligence or machine learning in the compulsory levels of education. However, more research and development is required to understand technological and pedagogical aspects of AI teaching especially in K-12 level. In the context of a two day workshop in a science festival, we introduced the concept of Convolution neural network (CNN) and examined how children learn about the way CNN performs image recognition. The concept was presented through hands-on practice with DoodleIt, a simple app for introducing the fundamental ideas behind CNN.

2. Design and Impact of a Near Peer-Led Computer Science Summer Bridge Program

   Jaelyn Dones*, Fred Martin, Justin Lu*, and 1 more author

   In 2022 IEEE Frontiers in Education Conference (FIE), 2022
3. Evaluating Student Spatial Skills Learning in a Virtual Reality Programming Environment

   Justin Lu*, Lauren Seavey*, Samuel Zuk*, and 2 more authors

   In 2022 IEEE Frontiers in Education Conference (FIE), 2022
4. CS Pathways: A Culturally Responsive Computer Science Curriculum for Middle School

   Garima Jain*, Fred Martin, Bernardo Feliciano*, and 5 more authors

   In 2022 IEEE Frontiers in Education Conference (FIE), 2022
5. Teachers as Curriculum Co-designers: Supporting Professional Learning and Curriculum Implementation in a CSforAll RPP Project

   Lijun Ni, Gillian Bausch, Bernardo Feliciano*, and 2 more authors

   In 2022 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT), 2022

2019

1. 

   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.

2011

1. 

   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.