Andy Smith’s research while affiliated with North Carolina State University and other places

Digital learning environments are used frequently in K-12 classrooms. Such use can require skillful orchestration as teachers need to understand the affordances of the learning environment, sequence of activities, and when and how to intervene with students. Using a digital learning environment in a multidisciplinary classroom context makes the design of support materials for teachers and students even more essential. To design for effective teacher orchestration in the classroom, we created a comprehensive set of materials for our multidisciplinary digital learning environment. We employ the design-based intervention research framework to trace the contextual and practical iterations these materials underwent. Additionally, we provide next steps for our work and considerations for the broader community.

Digital storytelling in combination with makerspace activities holds significant potential to engage students and support their learning. When students play, such as through makerspace activities, they engage in critical thinking and problem solving. In our work, we are joining storytelling with computational thinking (CT) practices, physical science exploration, and makerspace activities through a digital narrative-centered learning environment for elementary school. Learning within the environment is undergirded by makerspace play that centers on finding solutions to an open problem—how can stranded scientists on a remote island power up their village using found materials? The learning environment supports students’ CT practices and science content learning as they use and problem solve with physical energy conversion kits, culminating in their creation of an interactive story. We present here a brief case study of the ways students’ experiences with makerspace play support their problem solving and storytelling.

Recent years have seen growing awareness of the potential digital storytelling brings to creating engaging K-12 learning experiences. By fostering students’ interdisciplinary knowledge and skills, digital storytelling holds great promise for realizing positive impacts on student learning in language arts as well as STEM subjects. In parallel, researchers and practitioners increasingly acknowledge the importance of computational thinking in supporting K-12 students’ problem solving across subjects and grade levels, including science and elementary school. Integrating the unique affordances of digital storytelling and computational thinking offers significant potential; however, careful attention must be given to ensure students and teachers are properly supported and not overwhelmed. In this paper, we present our work on a narrative-centered learning environment that engages upper elementary students (ages 9 to 11) in computational thinking and physical science through the creation of interactive science narratives. Leveraging log data from a pilot study with 28 students using the learning environment, we analyze the narrative programs students created across multiple dimensions to better understand the nature of the resulting narratives. Furthermore, we examine automating this analysis using artificial intelligence techniques to support real-time adaptive feedback. Results indicate that the learning environment enabled students to create interactive digital stories demonstrating their understanding of physical science, computational thinking, and narrative concepts, while the automated assessment techniques showed promise for enabling real-time feedback and support.

Recent years have seen growing interest in utilizing digital storytelling, where students create short narratives around a topic, as a means of creating motivating problem-solving activities in K-12 education. At the same time, there is increasing awareness of the need to engage students as young as elementary school in complex topics such as physical science and computational thinking. Building on previous research investigating block-based programming activities for storytelling, we present an approach to block-based programming for interactive digital storytelling to engage upper elementary students (ages 9 to 10) in computational thinking and narrative skill development. We describe both the learning environment that combines block-based narrative programming with a rich, interactive visualization engine designed to produce animations of student generated stories, as well as an analysis of students using the system to create narratives. Student generated stories are evaluated from both a story quality perspective as well as from their ability to communicate and demonstrate computational thinking and physical science concepts and practices. We also explore student behaviors during the story creation process and discuss potential improvements for future interventions.

This study examined undergraduate STEM students’ experiences using an online introductory computer programming learning environment equipped with an automated hint generation system. Following a convergent parallel mixed methods design, this study utilized both quantitative and qualitative data from student experiential data. Analysis by level of prior knowledge demonstrated that elements of the learning environment did not cater to their learning needs and cognitive architecture. Cognitive Load Theory was used to contextualize system elements against both higher and lower prior experience learners, ultimately pointing to a need to design better scaffolds and hints to the needs of novice CS learners.

Advances in artificial intelligence (AI) and machine learning can be leveraged to tailor training based on the goals, learning needs, and preferences of learners. A key component of adaptive training systems is tutorial planning, which controls how scaffolding is structured and delivered to learners to create dynamically personalized learning experiences. The goal of this study was to induce data-driven policies for tutorial planning using reinforcement learning (RL) to provide adaptive scaffolding based on the Interactive, Constructive, Active, Passive framework for cognitive engagement. We describe a dataset that was collected to induce RL-based scaffolding policies, and we present the results of our policy analyses. Results showed that the best performing policies optimized learning gains by inducing an adaptive fading approach in which learners received less cognitively engaging forms of remediation as they advanced through the training course. This policy was consistent with preliminary analyses that showed constructive remediation became less effective as learners progressed through the training session. Results also showed that learners’ prior knowledge impacted the type of scaffold that was recommended, thus showing evidence of an aptitude–treatment interaction. We conclude with a discussion of how AI-based training can be leveraged to enhance training effectiveness as well as directions for future research.