Search for: All records

With the growing prevalence of AI, the need for K-12 AI education is becoming more crucial, which is prompting active research in developing engaging and age-appropriate AI learning activities. Efforts are underway, such as those by the AI4K12 initiative, to establish guidelines for organizing K- 12 AI education; however, effective instructional resources are needed by educators. In this paper, we describe our work to design, develop, and implement an unplugged activity centered on facial recognition technology for middle school students. Facial recognition is integrated into a wide range of applications throughout daily life, which makes it a familiar and engaging tool for students and an effective medium for conveying AI concepts. Our unplugged activity, “Guess Whose Face,” is designed as a board game that focuses on Representation and Reasoning from AI4K12’s 5 Big Ideas in AI. The game is crafted to enable students to develop AI competencies naturally through physical interaction. In the game, one student uses tracing paper to extract facial features from a familiar face shown on a card, such as a cartoon character or celebrity, and then other students try to guess the identity of the hidden face. We discuss details of the game, its iterative refinement, and initial findings from piloting the activity during a summer camp for rural middle school students.

 

Historically, female students have shown low interest in the field of computer science. Previous computer science curricula have failed to address the lack of female-centered computer science activities, such as socially relevant and real-life applications. Our new summer camp curriculum introduces the topics of artificial intelligence (AI), machine learning (ML) and other real-world subjects to engage high school girls in computing by connecting lessons to relevant and cutting edge technologies. Topics range from social media bots, sentiment of natural language in different media, and the role of AI in criminal justice, and focus on programming activities in the NetsBlox and Python programming languages. Summer camp teachers were prepared in a week-long pedagogy and peer-teaching centered professional development program where they concurrently learned and practiced teaching the curriculum to one another. Then, pairs of teachers led students in learning through hands-on AI and ML activities in a half-day, two-week summer camp. In this paper, we discuss the curriculum development and implementation, as well as survey feedback from both teachers and students. 

Our researchers seek to support students in building block-based programming projects that are motivating and engaging as well as valuable practice in learning to code. A difficult part of the programming process is planning. In this research, we explore how novice programmers used a custom-built planning tool, PlanIT, contrasted against how they used storyboarding when planning games. In a three-part study, we engaged novices in planning and programming three games: a maze game, a break-out game, and a mashup of the two. In a set of five case studies, we show how five pairs of students approached the planning and programming of these three games, illustrating that students felt more creative when storyboarding rather than using PlanIT. We end with a discussion on the implications of this work for designing supports for novices to plan open-ended projects. 

The demand is growing for a populace that is AI literate; such literacy centers on enabling individuals to evaluate, collaborate with, and effectively use AI. Because the middle school years are a critical time for developing youths’ perceptions and dispositions toward STEM, creating engaging AI learning experiences for middle grades students (ages 11 to 14) is paramount. The need for providing enhanced access to AI learning opportunities is especially pronounced in rural areas, which are typically underserved and underresourced. Inspired by prior research that game design holds significant potential for cultivating student interest and knowledge in computer science, we are designing, developing, and iteratively refining an AI-centered game development environment that infuses AI learning into game design activities. In this work, we review design principles for game design interventions focused on middle grades computer science education and explore how to introduce AI learning experiences into interactive game-design activities. We also discuss results from our initial co-design sessions with middle grades students and teachers in rural communities. 

The Computer Science Frontiers (CSF) project introduces teachers to the topics of artificial intelligence and distributed computing to engage their female students in computing by connecting lessons to relevant cutting edge technologies. Application topics include social media and news articles, as well as climate change, the arts (movies, music, and museum collections), and public health/medicine. CSF educators are prepared in a pedagogy and peer-teaching centered professional development program where they simultaneously learn and teach distributed computing, artificial intelligence, and internet of things lessons to each other. These professional developments allow educators to hone in on their teaching skills of these new topics and gain confidence in their ability to teach new computer science materials before running several activities with their students in the academic year classroom. In this workshop, teachers participating in the CS Frontiers professional development will give testimonials discussing their experiences teaching these topics in a two week summer camp. Attendees will then try out three computing activities, one from each Computer Science Frontiers module. Finally, there will be a question and answer session. 

Project-based learning can encourage and motivate students to learn through exploring their own interests, but introduces special challenges for novice programmers. Recent research has shown that novice students perceive themselves to be "bad at programming, especially when they do not know how to start writing a program, or need to create a plan before getting started. In this paper, we present PlanIT, a guided planning tool integrated with the Snap! programming environment designed to help novices plan and program their open-ended projects. Within PlanIT, students can add a description for their project, use a to do list to help break down the steps of implementation, plan important elements of their program including actors, variables, and events, and view related example projects. We report findings from a pilot study of high school students using PlanIT, showing that students who used the tool learned to make more specific and actionable plans. Results from student interviews show they appreciate the guidance that PlanIT provides, as well as the affordances it offers to more quickly create program elements. 

The Covid-19 pandemic has offered new challenges and opportunities for teaching and research. It has forced constraints on in-person gathering of researchers, teachers, and students, and conversely, has also opened doors to creative instructional design. This paper describes a novel approach to designing an online, synchronous teacher professional development (PD) and curriculum co-design experience. It shares our work in bringing together high school teachers and researchers in four US states. The teachers participated in a 3-week summer PD on ideas of Distributed Computing and how to teach this advanced topic to high school students using NetsBlox, an extension of the Snap! block-based programming environment. The goal of the PD was to prepare teachers to engage in collaborative co-design of a 9-week curricular module for use in classrooms and schools. Between their own training and the co-design process, teachers co-taught a group of high school students enrolled in a remote summer internship at a university in North Carolina to pilot the learned units and leverage ideas from their teaching experience for subsequent curricular co-design. Formative and summative feedback from teachers suggest that this PD model was successful in meeting desired outcomes. Our generalizable FIRST principles—Flexibility, Innovativeness, Responsiveness (and Respect), Supports, and Teamwork (collaboration)—that helped make this unique PD successful, can help guide future CS teacher PD designs.