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1. Lessons Learned About Designing and Conducting Studies From HRI Experts

   https://doi.org/10.3389/frobt.2021.772141  

   Fraune, Marlena R.; Leite, Iolanda; Karatas, Nihan; Amirova, Aida; Legeleux, Amélie; Sandygulova, Anara; Neerincx, Anouk; Dilip Tikas, Gaurav; Gunes, Hatice; Mohan, Mayumi; et al (January 2022, Frontiers in Robotics and AI)

   The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research. 

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2. Visualizing Values to Analyze Designers’ Ethical Responsibility

   Gray, Colin M; Chivukula, Shruthi S; Toombs, Austin L; Light, Ann (June 2018, Design Research Society)

   In this workshop, we seek to facilitate a shared understanding regarding the role of ethics and values in design practice and research, using this shared understanding to develop methods to investigate ethical decision-making. While existing study of ethics and values has largely focused on design methods for implementation in practice in an explicit and structured way (e.g., value-sensitive design, values at play), our focus is on the ways in which values might be discovered and generatively explored through qualitative and critical means, both by researchers and practitioners. Through collaborative activities and discussions, workshop participants will be engaged in analyzing existing design artifacts and processes, critiquing them through ethical lenses, and subsequently visualizing their process of value discovery. Outcomes from this workshop are expected to further deepen existing methods for uncovering ethics and values in a design process, highlighting potential opportunities for supporting practitioners’ work and ethical awareness. 

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3. Designing an Introductory HRI Course

   https://doi.org/10.1145/3610978.3638165  

   Admoni, Henny; Johal, Wafa; Szafir, Daniel; Sandygulova, Anara (March 2024, Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction)

   Human-robot interaction is now an established discipline. Dozens of HRI courses exist at universities worldwide, and some institutions even offer degrees in HRI. However, although many students are being taught HRI, there is no agreed-upon curriculum for an introductory HRI course. In this workshop, we aim to reach community consensus on what should be covered in such a course. Through interactive activities like panels, breakout discussions, and syllabus design, workshop participants will explore the many topics and pedagogical approaches for teaching HRI. They will then distill their findings into a single example introductory HRI curriculum. Output from this workshop will include a short paper explaining this curriculum and an example syllabus that can be used and adapted by HRI educators. 

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4. Exploration of Multi-layered Mentorship Approaches in Summer Engineering Programs

   Cook-Chennault, Kimberly (July 2018, ASEE Annual Conference proceedings)

   The purpose of this study was to examine the influence of multi-layered mentoring in summer engineering programs on confidence in understanding engineering research, engineering disciplines and the ability to conduct engineering research. This paper describes the work in progress towards incorporating this approach into summer programs at Rutgers University. The participants in the study included high school students from over 6 different high schools in New Jersey, coupled with in-service teachers who were participants in a National Science Foundation RET Site: Rutgers University Research Experience for Teachers in Engineering for Green Energy Technology and undergraduate scholars who participated in the REU Site: Green Energy Technology Undergraduate Program. The perceptions, understanding and evaluation of the program before the implementation of the multi-layered mentorship program are compared to the multi-layered program. High school students expressed higher confidence levels in the engineering design cycle and knowledge of the engineering discipline in the multi-layered mentorship program. Undergraduate students who were in labs where they peer-mentored teachers expressed higher levels of confidence in their skills as researchers than undergraduate students who did not peer-mentor in-service teachers or high school students. Future work will include enhanced data sampling, a revision of interview questions and assessment of participant’s understanding of concepts via quizzes. 

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5. Using Subgoal Labeling in Teaching CS1

   https://doi.org/10.1145/3287324.3287540  

   Morrison, Briana; Margulieux, Lauren; Decker, Adrienne (February 2019, Proceedings of the 50th ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   Subgoal labeling is an instructional design framework for breaking down problems into pieces that are small enough for novices to grasp, and often difficult for instructors (i.e., experts) to articulate. Subgoal labels have been shown to improve student performance during problem solving in disciplines both in and out of computing. Improved student performance occurs because subgoal labels improve student transfer and retention of knowledge. With support from NSF (DUE-1712025, #1712231), subgoal labels have been identified and integrated into a CS1 course (variables, expressions, conditionals, loops, arrays, classes). This workshop will introduce participants to the materials and demonstrate how the subgoal labels and worked examples are integrated throughout the course. Materials include over 100 worked examples and practice problem pairs that increase in complexity and difficulty within each topic. The materials are designed to be integrated into CS1 courses as homework or classroom examples and activities. Assessment of topics using subgoal labels will also be discussed. Participants will also engage in an activity where they create an example for their own course using subgoal labels. 

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