In addition to trained work skills, employed individuals with intellectual disabilities (IDs) possess unique competencies that are often insufficiently supported or overlooked by both themselves and their work environments. This study proposes using reflective practices to help employees with IDs and employers rediscover competencies beyond job-related skills. To facilitate participation, we employed personalized crafting with iron beads, supported by a custom-developed application integrated with text-to-image generative AI. We conducted two workshops involving 5–7 employees with IDs to explore and enhance our approach to competency discovery. In the first workshop, facilitators manually created templates for participants, while in the second, we leveraged an AI-assisted application for self-creation of personalized templates. Findings from group discussions reveal (1) the development of a framework that positions AI-enhanced crafting activities as an effective way for uncovering and fostering competencies, and (2) insights into reflection on self-concept as a foundation for competency development.
https://dl.acm.org/doi/10.1145/3706598.3713984
Extended reality (XR) learning environments result in greater knowledge gains when coupled with opportunities to reflect on one's actions and learning. However, when and how one should prompt reflection in XR learning environments (XRLEs) to effectively enhance learning, without breaking immersion, remains an open question. In this work, we argue that we can extract insights on how to design effective, immersive reflection for XRLEs from the expertise of escape room game masters (GMs) who regularly provide reflective hints and prompts in complex, immersive problem solving environments. To explore what we can learn from GMs, we conducted exploratory semi-structured interviews with 13 escape room GMs and, via iterative open coding, captured their best practices in how they provide hints and give nudges to escape room players.
https://dl.acm.org/doi/10.1145/3706598.3713811
Decomposition is a fundamental skill in algorithmic programming, requiring learners to break down complex problems into smaller, manageable parts. However, current self-study methods, such as browsing reference solutions or using LLM assistants, often provide excessive or generic assistance that misaligns with learners' decomposition strategies, hindering independent problem-solving and critical thinking. To address this, we introduce Decomposition Box (DBox), an interactive LLM-based system that scaffolds and adapts to learners' personalized construction of a step tree through a "learner-LLM co-decomposition" approach, providing tailored support at an appropriate level. A within-subjects study (N=24) found that compared to the baseline, DBox significantly improved learning gains, cognitive engagement, and critical thinking. Learners also reported a stronger sense of achievement and found the assistance appropriate and helpful for learning. Additionally, we examined DBox's impact on cognitive load, identified usage patterns, and analyzed learners' strategies for managing system errors. We conclude with design implications for future AI-powered tools to better support algorithmic programming education.
https://dl.acm.org/doi/10.1145/3706598.3713748
To ensure that technology serves as a tool for empowerment rather than oppression, Human-Computer Interaction (HCI) scholars have examined the ethical considerations of HCI research to explore pathways that inspire social change. In this work, we consider post-secondary education as one such pathway to social change. We engaged in a qualitative content analysis of the course, Introduction to Social Justice Informatics, with 47 students to understand how students developed knowledge of social justice and what sociotechnical tools facilitated their learning. We found that course materials coupled with peer discussion and reflective practice contributed to their development of critical consciousness. We discuss the significance of critical consciousness as a grounding theoretical approach within a social justice computing curriculum and the role of hope within social justice efforts and the workplace. We conclude by providing collectivist design strategies to nurture hope in the workplace.
Self project-based learning (SPBL) is a popular learning style where learners follow tutorials and build projects by themselves. SPBL combines project-based learning’s benefit of being engaging and effective with the flexibility of self-learning. However, insufficient guidance and support during SPBL may lead to unsatisfactory learning experiences and outcomes. While LLM chatbots (e.g., ChatGPT) could potentially serve as SPBL tutors, we have yet to see an SPBL platform with responsible and systematic LLM integration. To address this gap, we present AutoPBL, an interactive learning platform for SPBL learners. We examined human PBL tutors’ roles through formative interviews to inform our design. AutoPBL features an LLM-guided learning process with checkpoint questions and in-context Q&A. In a user study where 29 beginners learned machine learning through entry-level projects, we found that AutoPBL effectively improves learning outcomes and elicits better learning behavior and metacognition by clarifying current priorities and providing timely assistance.
https://dl.acm.org/doi/10.1145/3706598.3714261
We hypothesize that online movement videos have untapped potential for teaching physical skills, and we developed a platform that automatically generates practice plans from raw TikTok dance videos. The practice plans teach one segment at a time using fading guidance and part-learning principles and are presented using a web-based interface featuring concurrent visual aids. Two user studies (n=54, n=38) were conducted. The first showed significant improvements in learning outcomes compared to standard tutorials, underscoring the importance of well-structured practice plans and offering nuanced insights into the design and effectiveness of visual aids. The second study found that segmentation and emoji-based dual-coding only benefit learning when integrated into a well-designed lesson structure. We provide a set of practical recommendations for enhancing online movement learning, focusing on the need for substantive part-learning activities and careful use of visual aids to prevent cognitive overload.
https://dl.acm.org/doi/10.1145/3706598.3714062
Self-directed learning of computational skills online poses significant challenges, particularly the lack of effective tools for tracking progress and fostering reflection. To address this, we designed and implemented MILESTONES, a semi-automated self-monitoring tool that tracks online learning sessions and organizes web resources through three visual overviews: Time Pulse, Cue-Connect, and Sortify. In a week-long field deployment study (N=17), learners found MILESTONES intuitive and effective, even without prior experience with self-monitoring. The on-demand visual overviews encouraged learners to pause, reflect, and adjust their learning habits to better align with their goals. These overviews further fostered micro-reflections - brief, spontaneous reflections during learning. We also explored the role of a companion journal, which, although used inconsistently, helped learners form and reflect on their goals after learning sessions. Our findings contribute insights for designing learner-centered semi-automatic self-monitoring tools that can cater to diverse learning needs.
https://dl.acm.org/doi/10.1145/3706598.3714295