Demo Data from SIGIR 2026 / reviewers in the wild / expert
Serene Cheon
dblp:358/2387
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
0009-0000-1168-5656ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 77% Human-AI interaction · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies
STEM education |
1.0 | 1 | 2026 | Learning Inside the Chip: A Design-Science Approach to Developing MR Lab and an AI Agent for Conceptual, Procedural, and Spatial Understanding in Microelectronics Education · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
think-aloud · 1.0participatory design · 1.0focus groups · 1.0design-science approach · 1.0controlled study · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Learning Inside the Chip: A Design-Science Approach to Developing MR Lab and an AI Agent for Conceptual, Procedural, and Spatial Understanding in Microelectronics EducationabstractMixed reality (MR) and AI-powered pedagogical agents have been increasingly explored in STEM labs context as a means of providing hands-on practice with virtual teaching assistants. However, teaching microelectronics presents unique challenges because learners must integrate conceptual knowledge, procedural skills, and visual–spatial understanding to reason about multilayer chip structures and how fabrication processes optimize performance. We introduce ChipXR, a mixed-reality learning environment developed through a design-science approach and multistakeholder participatory design involving instructors, teaching assistants, students, education researchers, and HCI researchers to systematically refine interaction design and reduce potential cognitive load. The final prototype integrates interactive lecture modules for conceptual understanding, embodied MR activities for procedural skill development, and a LEGO-based building task to foster visual–spatial reasoning, all supported by a GPT-powered agent that explains the principles behind each activity. In a controlled study comparing MR+AI agent, MR-only, and traditional video-based lecture, we found no significant differences in conceptual quiz scores, but the MR+AI condition showed trends associated with visual–spatial understanding and perceived knowledge gains. A deployment in an introductory advanced semiconductor packaging course, supplemented by think-aloud sessions and focus groups, further contextualizes these findings. Together, these results suggest how MR+AI systems can be purposefully orchestrated to target complementary knowledge types and inform the design of future microelectronics curricula and other complex STEM domains requiring conceptual, procedural, and spatial reasoning. Serene Cheon, Austin Jerald Stanbury, Pavanbabu Arjunamahanthi, Himanandhan Reddy Kottur, Hyo Kang |
CHI | 1 |
| 2024 | Addressing the Talent Gap in Semiconductors: Motivators and Barriers to Career ChoicesabstractBackground: The United States has made substantial investments to restore the global competitiveness of the semiconductor industry; however, the nation continues to face a shortage of skilled labor in this sector. Despite the importance of this issue, there is limited research examining the barriers in individuals' educational and career choices in the semiconductor industry. To address this gap, our study aims to identify the contextual and psychological factors influencing decisions to pursue academic degrees and careers in semiconductors, using expectancy-value theory as our theoretical framework. Method: We first conducted interviews with engineering students and industry professionals in the semiconductor field to explore potential motivators and barriers. Thematic analysis of the interviews revealed multiple factors related to the utility, cost, and interest value. Based on these findings, we designed a survey and gathered responses from 178 participants, including students, faculty, and industry professionals in the semiconductor field. Findings: The results indicated that utility value (e.g., financial stability) plays a significant role in career aspirations for both industry professionals and students. When making career choices in semiconductors, its relative cost value compared to software engineering (e.g., lower pay, limited remote working) also played an important role. This suggests that semiconductor companies are competing not only with other semiconductor companies but also with other big tech companies, necessitating the provision of a comparable work environment to attract skilled engineers. Furthermore, both students and faculty identified limited lab activities and online resources as major barriers, highlighting the need to enhance accessibility to learning materials. Contribution: This study examines the varied perspectives of students, faculty, and industry professionals concerning the essential factors influencing career aspirations in the semiconductor field. To accomplish this, a survey questionnaire tailored to semiconductor career aspirations was developed, drawing insights from interviews with students and industry experts. The findings provide valuable insights for educational and industry approaches aimed at fostering the future workforce in the semiconductor industry. Hyo Kang, Serene Cheon, Alice Abia-Okon, Aida Damanpak Rizi, Wanli Xing 0001, Navid Asadizanjani |
FIE | 2 |