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Our team of culturally Deaf ASL-signing and hearing non-signing HCI researchers conduct research with the Deaf community to create ASL resources. This case study summarizes reflections, learning, and challenges with HCI user study protocols based on our experience conducting five user studies with deaf ASL-signing participants. The case study offers considerations for researchers in this space related to conducting think-aloud protocols, interviews and surveys, getting informed consent, interpreter services and data analysis and storage. Our goal is to share the lessons we learned, and offer recommendations for future research in this area. Going beyond accommodations and accessibility, we hope these reflections contribute to a shift toward ASL-centric HCI research methodologies for working with the Deaf Community. 

Video components are a central element of user interfaces that deliver content in a signed language (SL), but the potential of video components extends beyond content accessibility. Sign language videos may be designed as user interface elements: layered with interactive features to create navigation cues, page headings, and menu options. To be effective for signing users, novel sign language video-rich interfaces require informed design choices across many parameters. To align with the specific needs and shared conventions of the Deaf community and other ASL-signers in this context, we present a user study involving deaf ASL-signers who interacted with an array of designs for sign language video elements. Their responses offer some insights into how the Deaf community may perceive and prefer video elements to be designed, positioned, and implemented to guide user experiences. Through a qualitative analysis, we take initial steps toward understanding deaf ASL-signers’ perceptions of a set of emerging design principles, paving the way for future SL-centric user interfaces containing customized video elements and layouts with primary consideration for signed language-related usage and requirements. 

While existing student modeling methods focus on predicting students’ knowledge states, they often overlook the underlying cognitive processes contributing to learning. In this work, we integrate cognitive processes, specifically phases of rule learning, into student modeling, drawing inspiration from cognitive science. Rule learning involves rule search, discovery, and following, providing a systematic framework for understanding how individuals acquire and apply knowledge. We conduct two studies to explore rule learning phases in a real-world learning context. Moreover, we present a two-step approach to first predict the phases of rule learning students experience during problem solving with an intelligent tutoring system and then estimate the time spent on each predicted phase. Furthermore, we identify the relationships between the time spent on specific phases of rule learning and student performance. Our findings underscore the importance of integrating cognitive processes into student modeling for more targeted interventions and personalized support. 

The United Nations Sustainable Development Goals (UN SDGs) are the focus for a Research Experience for Teachers (RET) Site in Engineering at X University. The relevant and meaningful contexts of the SDGs allow middle and high school teachers and their students to easily make connections between research in a university lab setting to Science, Technology, Engineering, and Math (STEM) concepts in their classroom. Lesson plans inspired by the UN SDGs research experience were developed as an “integrated STEM” problem solving activity by each of the RET teachers. Ten (10) teachers comprising of both pre-service and in-service middle or high school teachers have participated in each cohort over the two years of the NSF RET grant thus far. Six weeks of authentic summer research takes place in 5 different faculty labs at X University under the mentorship of faculty and their graduate students or postdoc. Examples of the research projects include “Photocatalysis for Clean Energy and Environment,” “Genetically Engineering Plasmid DNA molecules to address Tuberculosis Antibiotic Resistance,” and “New Water-Based Technology for Plastic Recycling.” RET participants also attend a weekly coffee session to help guide the teachers through the research process and a weekly ½-day professional development (PD) session to translate the research experience into a classroom lesson plan that aligns to state standards, as well as evidence-backed curriculum design and teaching strategies. Teacher cohort building and community is fostered through group lunches and additional activities (e.g., coordinated lab visits, behind the scenes tour of a local science museum, and industry panel). For evaluation of the RET program, pre/post-surveys measured the teacher’s self-reported ability, confidence, understanding, and frequency of use of the Engineering Design Process (EDP), Integrated STEM, and the UN Sustainable Development Goals. Formative assessment was conducted throughout the summer on various aspects of the RET through surveys and regular check-ins with the teachers. At the end of the summer, focus groups were conducted by an external evaluator for both the teacher participants and the research mentors. Both teachers and mentors declared the program was well planned and executed. The teachers developed close bonds and connections, learned a lot from each other, had meaningful research experiences, and developed a sense of community. The research mentors reported that the teachers provided useful research contributions, were enthusiastic about the research, had genuine lab experiences, developed professional skills, and built good community connections. Areas for improvement included clear expectations for everyone, reducing steep learning curves, and consistency of mentoring across the labs. The RET program continues into the academic year with occasional meetings to report on the implementation of their research-inspired lesson plan in their classroom. The RET participants share that they are bringing in the “real world” relevance to their students with an integrated STEM lens (e.g., climate change and UN SDGs) and that they refer back to their own lab experiences (e.g., importance of measuring chemicals accurately). The research experience has made several positive impacts on the teacher participants that also benefit their students. 

In human-computer interaction (HCI), there has been a push towards open science, but to date, this has not happened consistently for HCI research utilizing brain signals due to unclear guidelines to support reuse and reproduction. To understand existing practices in the field, this paper examines 110 publications, exploring domains, applications, modalities, mental states and processes, and more. This analysis reveals variance in how authors report experiments, which creates challenges to understand, reproduce, and build on that research. It then describes an overarching experiment model that provides a formal structure for reporting HCI research with brain signals, including definitions, terminology, categories, and examples for each aspect. Multiple distinct reporting styles were identified through factor analysis and tied to different types of research. The paper concludes with recommendations and discusses future challenges. This creates actionable items from the abstract model and empirical observations to make HCI research with brain signals more reproducible and reusable.