publications

2024

1. 

   Introducing Children to AI and ML with Five Software Exhibits

   Saniya Vahedian Movahed*, James Dimino*, Andrew Farrell*, and 10 more authors

   In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024

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   Artificial intelligence (AI) and machine learning (ML) have a deepening impact in our world. For empowered citizenship and career readiness, elementary and middle school students need to understand these technologies. To provide engaging introductory experiences, we created five original interactive software exhibits introducing children to hands-on activities in AI and ML. The exhibits were tested by 125 elementary and middle school students (aged 7 to 14). Four themes emerged: Students recognized that AI and ML systems can process data from cameras (perception); they saw that these systems responded to their training input (trust); they appreciated the practical import of AI/ML systems (affective and cognitive attitudes); and students were introduced to models and modes (specialization). This paper presents our goals in creating the exhibits and results from our initial testing with children. Our work contributes to the literature on formal and informal activities for introducing AI and ML to young learners.

2. Investigating Middle School Students’ Early Learning Experience of Computer Science through Creating Apps for Social Good

   Gillian Bausch, Lijun Ni, Elizabeth Thomas-Cappello, and 3 more authors

   In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 2024

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   This study investigated middle school students’ learning experiences with a computer science and digital literacy (CSDL) curriculum, which was developed through the CS Pathways researcher-practitioner partnership (RPP) project. The curriculum is based on students learning computer science (CS) through creating apps that serve community and social good. Both quantitative and qualitative data were collected from students in three urban districts: 1) 330 paired pre- and post-survey responses indicating students’ confidence and interest in both learning CS and creating apps for social good; 2) 343 open-ended question responses in the post-survey probing into students’ perceptions on learning CS after taking the course. Whether there were gender differences emerged from both data were also examined. The results showed that students’ confidence in coding and creating apps for social good significantly increased after completing the course, regardless of gender. However, their interest in pursuing CS learning remained at a low level. Further analysis showed male students reported significantly stronger interest than female students. Qualitative analysis of the open-ended responses revealed that both male and female students appreciated the collaborative learning environment and learning coding through making apps. Male students did not like certain instructional approaches that their teachers used. Female students expressed their dislike of coding in general. We applied an interest development theory to further understand these results, which suggested that we consider the trajectory of students’ interest development of CS.

3. 

   ChemAIstry: A Novel Software Tool for Teaching Model Training in K-8 Education

   Fred Martin, Vaishali Mahipal*, Garima Jain*, and 2 more authors

   In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 2024

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   Machine learning (ML) systems are increasingly in use in society. For young learners to be informed citizens and have full career potential it is important for them to understand these concepts. To support this learning, we created "ChemAIstry,” an interactive software tool for children which demonstrates training and classification in machine learning. Students select which everyday items are safe to bring into a chemistry lab (e.g., a lab coat is safe; pizza is not). These selections serve as training input for a decision tree classifier. After training, students see how the trained model performs in classifying new objects. ChemAIstry was tested with 40 students aged 7 to 14 years at a public K?8 school. The software captured student selections during training. We analyzed these interactions to yield a "Correspondence Score,” a measure of student understanding of the classification task. We screen-recorded student use of the software and audio-recorded our conversations with them during this use. Our analysis of these data indicates that students were able to understand the concept of model training, including that items were subsequently classified based on their training input. More than half of the student trials indicated that students correctly understood the task. This suggests ChemAIstry was effective in introducing students to these ideas in machine learning. We recommend continued development of related tools for curriculum integration of AI in K-8 education.

4. Creating Apps for Community and Social Good: Preliminary Learning Outcomes from a Middle School Computer Science Curriculum

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

   ACM Transactions on Computing Education, 2024

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   This study examined student learning outcomes from a middle school computer science (CS) curriculum developed through a researcher and practitioner partnership (RPP) project. The curriculum is based on students creating mobile apps that serve community and social good. We collected two sets of data from 294 students in three urban districts: (1) pre- and post-survey responses on their learning experiences and attitudes toward learning CS and creating community-serving apps; (2) the apps created by those students. The analysis of student apps indicated that students were able to create basic apps that connected with their personal interests, life experiences, school community, and the larger society. Students were significantly more confident in coding and creating community-focused apps after completing the course, regardless of gender, race/ethnicity, and grade. However, their interest in solving coding problems and continuing to learn CS decreased afterward. Analyses of students’ attitudes by gender, grade, and race/ethnicity showed significant differences among students in some groups. Seventh-grade students rated more positive on their attitudes than eighth graders. Students identifying with different race/ethnicity groups indicated significantly different attitudes, especially students identifying as Southeast Asian, Black/African American, and Hispanic/Latino. Self-identified male students also reported stronger interest and more positive attitudes overall than self-identified female students. Students also reported positive experiences in learning how to create real apps serving their community, while there were disparities in their experiences with coding in general and some of the instructional tools used in the class.

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.

2018

1. 

   Redefining Science Learning with a Platform for Sharing Experimental Data

   Maureen Melanson*, Glenda Javier*, Caitlin Canane*, and 1 more author

   In Society for Information Technology & Teacher Education International Conference, Jul 2018

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   This article focuses on integration of the technology of iSENSE into the secondary education as a teaching and learning tool to design, develop, and infuse digital learning experiences that utilize technology. The objective of each lesson was to use this application to achieve redefinition, create new tasks previously inconceivable, and transform the education process using the SAMR model as presented by Dr. Ruben Puentedura. The objective was to create higher-order thinking tasks that have a significant impact on student learning. Data was collected from high school biology, chemistry, and biotechnology classrooms and integrated into lessons using iSENSE, a web based program, created from a collaborative effort of the University of Massachusetts Lowell and Machine Science Inc, for compiling, sharing, visualizing and analyzing data. Through our research, we were able to conclude that the utilization of iSENSE technology helps students manipulate data to gain a deeper understanding of math and science concepts and encourages further research in the 9-12 classroom.

2017

1. 

   Empowering middle school students to create data-enabled social apps

   Lijun Ni, Farzeen Harunani*, and Fred Martin

   Journal of Computing Sciences in Colleges, Jul 2017

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   MIT App Inventor has enabled middle school students to learn computing while creating their own apps-including apps that serve community needs. However, few resources exist for building apps that gather and share data. There is a need for new tools and instructional materials for students to build data-enabled, community-focused apps. We developed an extension for App Inventor, called AppVis, which allows app-makers to publish and retrieve data from our existing web-based collaborative data visualization platform. We used AppVis with supporting instructional materials in two one-week summer camps attended by a total of 33 middle school students. Based on student interview data and analysis of their final apps, we found that this approach was broadly accessible to a diverse population of students and motivated them to build apps that could be used by their own communities.

2016

1. 

   Inquiry learning with data and visualization in the STEM classroom

   Samantha Michalka, James Dalphond*, and Fred Martin

   In Society for Information Technology & Teacher Education International Conference, Jul 2016

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   Understanding data is a crucial 21st century skill for students in STEM education. At the core of the latest science and math standards is the principle that students should be able to apply real world science and engineering principles to work with and understand data. Here, we discuss three styles of integrating collaborative data analysis and exploration in the classroom using a web-based data visualization system that was purpose-built for middle through high school use (iSENSE). We discuss each of these styles/strategies, potential benefits and reasons for use, and give concrete examples of classroom use by teachers.

2015

1. 

   Probability with Collaborative Data Visualization Software

   Melinda BN Willis, Sue Hay, Fred G Martin, and 2 more authors

   The Mathematics Teacher, Jul 2015

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   The data collection and analysis tool iSENSE helps algebra students collect, share, and explore their own experimental data while learning about the law of large numbers.

2. 

   Doing the Data Walk

   Steve Cogger

   The Science Teacher, Jul 2015

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2014

1. 

   Integrating computational thinking across the K–8 curriculum

   Irene Lee, Fred Martin, and Katie Apone

   ACM Inroads, Jul 2014

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   We examine how young learners can gain early exposure and engage in rich computational experiences in K-8. These experiences can build students’ computational thinking, understanding of CS concepts, programming skills and confidence as critical thinkers as well as provide experience with collecting and analyzing data. We discuss how three types of computational activities—digital storytelling, data collection and analysis, and computational science investigations—can be used to incorporate computational thinking (CT) across the curriculum.

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.

2010

1. 

   iSENSE: A web environment and hardware platform for data sharing and citizen science

   Fred Martin, Sarah Kuhn, Michelle Scribner-MacLean, and 6 more authors

   In 2010 AAAI Spring Symposium Series, Feb 2010

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   The Internet System for Networked Sensor Experimentation (iSENSE) enables users to store sensor data on the web, view data from other contributors, and combine data from multiple sources to examine regional, national, and global phenomena. Intended for educational use and citizen science applications, the system is compatible with a wide range of classroom probes and sensors. We have also prototyped a custom data-logging device—the Portable iSENSE Network Point, or PINPoint—which features on-board sensors, a GPS receiver, and a connector for external probes. By pooling their data on the web, users can create an expanded sensor network and engage in collaborative research on STEM topics ranging from human health to environmental science and energy conservation.

2. 

   Impact of a professional development program using data-loggers on science teachers’ attitudes towards inquiry-based teaching

   Sachiko Tosa*, and Fred Martin

   Journal of Computers in Mathematics and Science Teaching, Aug 2010

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   This study examined how a professional development program which incorporates the use of electronic data-loggers could impact on science teachers’ attitudes towards inquiry-based teaching. The participants were 28 science or technology teachers who attended workshops offered in the United States and Japan. The professional development program emphasized (a) guided inquiry activities, (b) participants’ own exploration within the range of given tasks, (c) instructors’ guidance on the processes of inquiry and technology, and (d) discussions of the ways to bring their inquiry experiences in their classrooms. Data sources included field notes, video recordings, artifacts, and survey responses. Analysis of participants’ discourse identified many instances in which the program helped the teachers deepen their understanding of inquiry-based teaching. The findings are presented as three assertions: (a) all the elements incorporated in the program contributed positively to participants’ engagement in inquiry, (b) connections between participants’ sensory experiences and graphical representations of data led them to have new understanding of the phenomena under the investigation, and (c) there were strong connections between their experiences about inquiry and teaching strategies that they wanted to incorporate in their classrooms. Applications of the findings into the development of more effective professional development programs will be discussed.