We explore users' behavior on various computer-supported systems. Based on the findings of the user behavioral data analysis, we can predict users' future behavior or mediate users' problematic behaviors.
We analyze users' interactive and collaborative behavior on online systems such as social media (for example, Youtube and Twitter) or collaborative services (for example, GitHub and Wikipedia).
We design and build novel intelligent systems to help people interact with AI or computers. We then explore new useful insights for future intelligent systems.
UXC Lab, User eXperience Computing Lab, is a data-drivn Human-Computer Interaction and User eXperience (Data-driven HCI/UX) research group focusing on user behavioral pattern analysis in the Department of Software Convergence at Kyung Hee University, led by Sangkeun Park.
Our HCI/UX research utilizes data-driven approaches to address various areas of Human-centered AI, Social Computing, and Intelligent System Design. We aim to improve the way people design systems and make decisions by exploring human behavioral patterns and designing novel intelligent systems.
July 2024
We are delighted to announce that Cheolhyeon Han(한철현), an undergraduate student in our lab, has been awarded the Encouragement Award for his paper titled "Development of a Predictive Model for Personalized Video Playback Speed Based on Viewing Patterns" at the KCC2024 (한국컴퓨터종합학술대회).
Feb 2024
We are delighted to announce that Gunu Park(박건우), an undergraduate student in our lab, has been awarded the Encouragement Award for his paper titled "A Study of Short-Form Video Watching Patterns and a Predictive Model" at the KSC2023 (한국소프트웨어종합학술대회).
Feb 2024
We are very proud that our undergraduate students in our lab, Eunnho Kim(김은호) and Cheolhyeon Han(한철현), presented two undergraduate student papers (one paper each) at KCSE2024 (한국소프트웨어공학학술대회).
Dec 2023
We are excited to announce that Professor Park has won the Special Achievement Award at the KSC2023 (한국소프트웨어종합학술대회) for publishing 9 papers.
June 2023
We are happy to announce that we have won a new research grant, Basic Science Research Program, from the National Research Foundation of Korea (NRF). We will be studying Mobile Interaction Receptivity Prediction Model based on Users' Context.
January 2023
Sangkeun Park has actively participated in Q&A on StackOverflow and earned top 1% reputations in 2022. His main area covers #Python and #Pandas
September 2022
It is very exciting to announce that the the lab is newly launched with an assistant professor, Sangkeun Park.
We are actively looking for highly motivated undergraduate students and graduate students who are interested in research of data-driven Human-Computer Interaction and User eXperience (HCI/UX) using AI/ML.
If you are interested in working with me, feel free to email me with your CV to sk.park (a) khu.ac.kr.
Sangkuen Park | 박상근
📧 sk.park (a) khu.ac.kr
🏠 Personal Homepage
Jaehwan Kim | 김재환
📅 2023.09 ~ .
📧 jhwankim (a) khu.ac.kr
🏠 Personal Homepage
Eunnho Kim | 김은호
📅 2024.03 ~ .
📧 taemin4u (a) khu.ac.kr
🏠 Personal Homepage
Minjeong Cha | 차민정
📅 2024.03 ~ .
📧 minjeongcha (a) khu.ac.kr
🏠 Personal Homepage
Cheolhyeon Han | 한철현
📅 2023.09 ~ .
📧 hch2454 (a) khu.ac.kr
🏠 Personal Homepage
Intae Ji | 지인태
📅 2024.05 ~ .
📧 jit0309 (a) khu.ac.kr
🏠 Personal Homepage
Jeongwon Kim | 김정원
📅 2024.09 ~ .
📧 micky4 (a) khu.ac.kr
🏠 Personal Homepage
User-driven intervention tools such as self-tracking help users to self-regulate problematic smartphone usage. This paper proposes GoldenTime, a mobile app that promotes self-regulated usage behavior via system-driven proactive timeboxing and micro-financial incentives framed as gain or loss for behavioral reinforcement. We conducted a large-scale user study (n = 210) to explore how our proactive timeboxing and micro-financial incentives influence users’ smartphone usage behaviors.
Vehicle dashboard cameras or dashcams, among other smart vehicle technologies, are increasingly attracting interest across the globe. Furthermore, dashcam videos as objective witnesses are often shared to resolve various traffic incidents. In this work, we aim to understand cross-national differences in motives and privacy concerns of dashcam video-sharing, which are closely related to the factors that vary across countries, such as cultural values, traffic regulation, driving environments, and privacy perception.
We built BeActive, a mobile intervention system for preventing prolonged sedentary behaviors. We collected users' responses taking into account relevant contextual factors such as: what they were doing, where they were, and whom they were with, for three weeks. Using a multi-stage model, we systematically analyzed the responses to deepen our understanding of the receptivity of the JIT intervention.
What do you think of community safety? In this study, we designed a community-sourced patrolling campaign in which community members would schedule their patrol times and routes, perform bike-based patrolling with video capturing and share the information using their smartphones. We conducted a four-week field study (n=20) on a university campus to verify the campaign's feasibility and observe users' behavior. The results show key findings about users’ task scheduling, event capturing and reporting behaviors, factors affecting task selection and execution, and user motivation and engagement.
On computer-mediated communication, stickers or GIFs, though similar in appearance to emoji, have distinct characteristics because they often contain animation, various gestures, and multiple characters and objects. Their complexity and placement constraint may result in miscommunication. In this study, we aim to understand how people perceive emotion in stickers, as well as how miscommunication related to sticker occurs in actual chat contexts via online survey and interview.
In location-based social Q&A services, people ask a question with a high expectation that local residents who have local knowledge will answer the question. Besides, questions are classified by regions rather than topics, unlike existing topic-based Q&A services. We analyzed a 12-month period Q&A dataset from Naver KiN "Here," a popular location-based social Q&A mobile app in South Korea, in addition to a supplementary survey dataset obtained from 285 mobile users.
We often see various traffic violations on the road while driving. In this study, we consider community policing on the road with pervasive recording technologies. We developed a mobile app so that drivers can easily capture and report various threats to traffic safety to the police via mobile apps. Our two-week user study showed that the mobile app effectively supported community policing activities on the road.
What if your parent knows your bad driving habits? Bad driving behaviors are a major cause of traffic accidents, many of them resulting in fatalities. Our study intends to encourage safe driving habits by increasing drivers' self-awareness about their driving habits, as well as receiving supportive feedback on their driving behavior from a loved one as an intervention method. We built an Android prototype app to deliver feedback for bad driving maneuvers to both drivers themselves and to their corresponding supporters and conducted a field study evaluation.
Dashcams support the continuous recording of external views that provide evidence in case of unexpected traffic-related accidents and incidents. Recently, the sharing of dashcam videos has gained significant traction for accident investigation and entertainment purposes. Furthermore, there is a growing awareness that dashcam video sharing will significantly extend urban surveillance. Our work aims to identify the major motives and concerns behind the sharing of dashcam videos for urban surveillance.
Uber and Airbnb, two well-known sharing economy services, are facing conflicts with traditional taxi and hotel companies because these services have monetary benefits but are free from legacy regulations. However, non-monetary-based sharing services, represented by Couchsurfing, Inc., are free from such conflict and still successful. We investigated the distinctive user participation motivation of non-monetary-based sharing services versus monetary-based ones.
Our goal was to design a tool that can facilitate Q&A activities in offline presentations. We first identified several problems associated with current offline Q&A practices. We then developed SlideQA, an online slide-based Q&A tool, and explored its usability.
In location-based social Q&A, the questions related to a local community (e.g., local services and places) are typically answered by local residents. We wanted to deepen our understanding of the localness of knowledge sharing through investigating the topical and typological patterns related to the geographic characteristics, geographic locality of user activities, and motivations of local knowledge sharing. To this end, we analyzed a 12-month period Q&A dataset from Naver KiN "Here" and a supplementary survey dataset from 285 mobile users.
Dashcams support the continuous recording of external views and help drivers to guard against unexpected accidents and incidents. Recently, sharing dashcam videos has gained significant traction for accident investigation. However, current sharing requests are being made in aninefficient way through online communities. By analyzing 52 sharing request postings from past three years in the existing online community site, we extracted main factors for service implementation, then designed an on campus dashcam video sharing system.
To analyze compus research Q&A behavior of graduate students, we conducted focus group interviews with graduate student of KAIST in Korea. We analyzed the interview data with a grounded theory approach and organized the results by using a framework inspired by activity theory. The result indicated that campus research Q&A behavior of graduate students has theree main themes: 1) the absence of campus research community and the difficulty of Topic expert search, 2) the closed lab culture and the burden for personal question, 3) the dispersed knowledge problem and the absence of collaboratoin. We interpreted three main themes as Korean culture and suggested design guidelines: 1) Campus comprehensive search, 2) Offering detail information of labs/researchers, 3) Supporting online interest group.
To analyze compus research Q&A behavior of graduate students, we conducted focus group interviews with graduate student of KAIST in Korea. We analyzed the interview data with a grounded theory approach and organized the results by using a framework inspired by activity theory. The result indicated that campus research Q&A behavior of graduate students has theree main themes: 1) the absence of campus research community and the difficulty of Topic expert search, 2) the closed lab culture and the burden for personal question, 3) the dispersed knowledge problem and the absence of collaboratoin. We interpreted three main themes as Korean culture and suggested design guidelines: 1) Campus comprehensive search, 2) Offering detail information of labs/researchers, 3) Supporting online interest group.
- By appointment
- sk.park (a) khu.ac.kr
- [Eng] #314, College of Software Convergence, Kyung Hee University. 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, Republic of Korea.
- [Kor] 경기도 용인시 기흥구 덕영대로 1732. 경희대학교 국제캠퍼스 전자정보대학(소프트웨어융합대학). 3층 314호.