Integrating computational thinking (CT) into early K-12 education is increasingly recognized as essential for preparing students to navigate a technology-driven world. Digital storytelling, with its capacity to combine narrative expression with programming, offers a promising interdisciplinary strategy for promoting CT. This experience report presents a narrative-centered learning environment that integrates digital storytelling, block-based programming, and hands-on maker activities to foster CT and interdisciplinary learning in upper elementary classrooms. Grounded in a problem-based storyline, the environment engages students in solving real-world-inspired challenges through physical science experimentation and interactive narrative creation. We describe a multi-week classroom implementation with fourth- and fifth-grade students and analyze survey responses and programming artifacts from 41 participants to explore how CT concepts and practices, including sequencing, conditionals, and debugging, emerged in their work. While students demonstrated statistically significant gains in CT, they also encountered challenges related to narrative coherence, science alignment, and conditional logic. We reflect on what did and did not work, offering design insights for educators and designers adopting interdisciplinary, story-driven approaches to computing education.

There is a growing need for 21st-century workers to be digitally literate and to possess computational thinking and collaborative problem-solving skills. Computer-supported collaborative learning (CSCL) focused on computational thinking can guide students toward the co-development of these skills. In this work, we present our approach to integrating virtual and physical learning modalities into InfuseCS, a CSCL environment. InfuseCS uses problem-based learning scenarios to situate upper elementary school students (ages 8 to 11) in a CSCL setting to foster their computational thinking and science knowledge construction as they collaborate to create digital narratives.

Digital storytelling, which combines traditional storytelling with digital tools, has seen growing popularity as a means of creating motivating problem-solving activities in K-12 education. Though an attractive potential solution to integrating language arts skills across topic areas such as computational thinking and science, better understanding of how to structure and support these activities is needed to increase adoption by teachers. Building on prior research on block-based programming for interactive storytelling, we present initial results from a study of 28 narrative programs created by upper elementary students that were collected in both classroom and extracurricular contexts. The narrative programs are evaluated across multiple dimensions to better understand the types of narrative programs being created by the students, characteristics of the students who created the narratives, and what types of support could most benefit the students in their narrative program construction. In addition to analyzing the student-created narrative programs, we also provide recommendations for promising system-generated and instructor-led supports.

Recent years have seen growing interest in modeling visitor engagement in museums with multimodal learning analytics. In parallel, there has also been growing concern about issues of fairness and encoded bias in machine learning models. In this paper, we investigate bias detection and mitigation techniques to address issues of algorithmic fairness in multimodal models of museum visitor visual attention. We employ slicing analysis using the Absolute Between-ROC Area (ABROCA) statistic to detect encoded bias present in multimodal models of visitor visual attention trained with facial expression and posture data from visitor interactions with a game-based museum exhibit about environmental sustainability. We investigate instances of gender bias that arise between different combinations of modalities across several machine learning techniques. We also measure the effectiveness of two different debiasing strategies—learned fair representations and reweighing—when applied to the trained multimodal visitor attention models. Results indicate that patterns of bias can arise across different modality combinations for the different visitor visual attention models, and there is often an inherent tradeoff between predictive accuracy and ABROCA. Analyses suggest that debiasing strategies tend to be more effective on multimodal models of visitor visual attention than their unimodal counterparts

We developed a haptically-enhanced physics simulation to investigate the effects of haptics on the understanding of conceptual concepts related to forces—specifically those related to buoyancy. We evaluated the effects of haptic force feedback, as well as traditional visual representations of forces, on learning via a between-participant user study. Participants completed a buoyancy assessment before and after interacting with the simulation. Haptics enhanced performance regardless of prior knowledge. However, the combined effect of haptics with visual cues differed based on participant prior knowledge. Participants with high prior knowledge significantly improved performance when given both abstract visual cues and haptic feedback combined. Participants with low prior knowledge significantly improved when given haptic feedback alone, and the combination of haptics with visual cues did not improve performance. Our results suggest that the prior knowledge of users and the visual cues used impact the effectiveness of haptically-enhanced simulations with respect to learning outcomes.

One of the most efficient ways for elementary school students to gain exposure to computational thinking is when it is integrated into other disciplinary areas; however, elementary school teachers often lack the necessary resources to do this effectively. By leveraging the motivation force of narrative to engage students and the scaffolding affordances of block-based programming to support students, computationally-rich narrative-centered learning offers promise to address this need. In this work, we review design principles from prior work for engaging elementary students in computational thinking as well as results from initial pilot studies to investigate how computationally-rich narrative-centered learning in the context of science problem solving can support the integration of computational thinking into other disciplinary areas.

Modeling visitor engagement is a key challenge in informal learning environments, such as museums and science centers. Devising predictive models of visitor engagement that accurately forecast salient features of visitor behavior, such as dwell time, holds significant potential for enabling adaptive learning environments and visitor analytics for museums and science centers. In this paper, we introduce a multimodal early prediction approach to modeling visitor engagement with interactive science museum exhibits. We utilize multimodal sensor data including eye gaze, facial expression, posture, and interaction log data captured during visitor interactions with an interactive museum exhibit for environmental science education, to induce predictive models of visitor dwell time. We investigate machine learning techniques (random forest, support vector machine, Lasso regression, gradient boosting trees, and multi-layer perceptron) to induce multimodal predictive models of visitor engagement with data from 85 museum visitors. Results from a series of ablation experiments suggest that incorporating additional modalities into predictive models of visitor engagement improves model accuracy. In addition, the models show improved predictive performance over time, demonstrating that increasingly accurate predictions of visitor dwell time can be achieved as more evidence becomes available from visitor interactions with interactive science museum exhibits. These findings highlight the efficacy of multimodal data for modeling museum exhibit visitor engagement.

Recent years have seen a growing recognition of the importance of enabling K-12 students to engage in computational thinking, particularly in elementary grades where students' dispositions toward STEM are developing. Block-based programming has emerged as an effective tool for engaging these novice learners in computational thinking. At the same time, digital storytelling has emerged as a promising avenue for creating motivating problem-solving scenarios that engage students in science investigations. Although block-based programming and digital storytelling are in many ways synergistic, there is a lingering question of how to design block-based languages at an age-appropriate level to enable effective and engaging storytelling. In this work, we review design principles from prior block-based and digital storytelling systems as well as propose the design of block-based programming language features to enable the creation of rich, interactive science narratives by upper elementary students.