Developing computational thinking (CT) assessment methods appropriate for elementary students is attracting growing attention as CT research in elementary education progresses. To review the current elementary CT assessments for potential gaps, and seek additional methodologies to expand our understanding of CT, an integrative literature review of 75 research papers was performed in two phases. In Phase One, we conducted a critical analysis of existing elementary CT assessment studies. Key results include: 1) Artifact analysis, CT assessment items, and interviews are the most common methods utilized to assess CT in elementary grades; 2) Existing CT assessments primarily focus on students' computational artifacts and performance on CT tests; however, strategies to study students' thought processes during CT problem-solving are limited and under-utilized. Guided by the results of phase one, along with the theoretical perspective that connected CT to visual processing ability, in phase two we performed a survey of literature in the area of understanding cognitive processes through eye-tracking (i.e., visual attention) and think-aloud methodologies (i.e., verbalization). We focused on eye-tracking and think-aloud methodologies as these have been used to understand students' cognitive processes during problem-solving in other areas. Based on these findings, we proposed that in addition to current established methodologies, eye-tracking with the think-aloud technique can provide new insights into students' CT.
Conversational AI such as Alexa and Google Home are increasingly ubiquitous in young people's lives, but these young users are often not afforded the opportunity to learn about the inner workings of these technologies. One of the most powerful ways to foster this learning is to empower youth to create AI that is personally and socially meaningful to them. We have built a novel development environment, AMBY–"AI Made By You"–for youth to create conversational agents. AMBY was iteratively designed with, and for youth aged 12-13 through contextual inquiry and usability studies. AMBY is designed to foster AI learning with features that enable users to generate training datasets and visualize conversational flow. We report on results from a two-week summer camp deployment, and contribute design implications for conversational AI authoring tools that empower AI learning for youth.
Given the academic diversity of today's classrooms, elementary teachers engaged in computer science (CS) and computational thinking (CT) instruction must create CS/CT experiences that are accessible and engaging to a broad range of learners, including those with disabilities. One method of developing inclusive instructional experiences is through the Universal Design for Learning (UDL) framework, wherein teachers proactively design instruction for the broadest range of learners. Doing so may be challenging as elementary teachers may not be familiar with the UDL framework or may not have experience with applying UDL within CS/CT instruction. The purpose of this qualitative study was to investigate how four elementary teachers provided UDL-based instruction to academically diverse learners during CS/CT instruction. Teachers received professional development and instructional coaching related to UDL within CS/CT education. Data included teachers' lesson plans, coaching logs, and teacher interviews which were qualitatively analyzed and triangulated. Data revealed that teachers generally addressed all three UDL principles, with an emphasis on two of the principles (multiple means of engagement and multiple means of representing content) above the third principle (multiple means of action and expression). They focused on breaking tasks into steps, emphasizing student choice, and presenting information in multiple ways. Findings revealed nuanced implementation differences among the teachers as well.
This chapter provides specific recommendations about how special education teachers can begin to incorporate both assistive and instructional technologies into their instruction. It begins with general guidelines for choosing assistive technologies. The chapter provides general recommendations that can be applied to a broad range of technologies. It focuses on suggestions for using instructional technologies within the context of content-area instruction to support access, engagement, and learning for students with disabilities. The chapter describes strategies for implementing both types of technologies to support students with disabilities as well as how the Universal Design for Learning (UDL) framework could be used to consider technology integration. It also describes the rationale for incorporating assistive and instructional technologies into teaching and learning for students with disabilities. The chapter provides a model for using UDL framework for integrating both instructional and assistive technologies into instruction. It ends with tips and resources for further exploration into assistive and instructional technologies.
This chapter provides a survey of research on the use of technology to support the inclusion of students with disabilities in elementary schools. Discussion of these technologies is situated within the Universal Design for Learning (UDL) framework in order to contextualize classroom implementation of technology within an instructional planning approach focused on inclusive practices. The chapter includes discussions of common assistive and instructional technologies used in teaching and learning within core academic content (e.g., reading and mathematics) as well as those emerging technologies that are only starting to appear in elementary classrooms (e.g., virtual and augmentative reality) to support the participation and inclusion of elementary students with disabilities. To address common barriers often experienced by elementary teachers when learning to use technology in their classrooms, the chapter ends with practical suggestions that they can be used in implementing technology-mediated instruction that promotes inclusion, along with implementation examples to contextualize these suggestions.
More young people are interacting with smart conversational agents such as Alexa and Google Assistant. These platforms are extensible, providing, in principle, a compelling opportunity for young users to create and tinker with their own conversational agents. However, to date the interfaces for conversational app development are adult-focused. This paper presents the early design process for AMBY (AI Made by You), which we are building to empower young learners to create their own conversational agents. We first conducted a contextual inquiry with 14 middle school students (aged 11-13) in an AI summer camp, followed by two other usability studies. The system design has been refined after each study. Key features of AMBY include a visual dialogue management panel, testing panel with a diverse avatar, and a voice input modality. AMBY is designed to serve as a pedagogically-robust resource for K-12 AI education and as an engaging and creative way for middle schoolers to explore AI.
