For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| WIS5065 | Special Topics on User Experience | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| This course addresses some issues on UI/UX design. Topics of the course include fundamental theories and techniques to design and evaluate computer-based products or systems. Also, the course address various studies on user experience - usability and aesthetics, affordance and emotion, design strategies, context in use, mental workload, fatigue problems, user preference, etc. By experiencing many real research problems, students would become familiar with conducting an independent study on user experience. | |||||||||
| WIS5069 | Theories of Digital Information | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| To study deep into digital content, an accurate understanding of “Digital Information” which is the basic unit of digital content is necessary. This course is an introductory course on digital information. This course will introduce various characteristics of digital information, and investigate it in terms of pricing information, digital intellectual property, and so on. | |||||||||
| WIS5072 | Analysis of IT Enterprise Data | 3 | 6 | Major | Master/Doctor | Interaction Science | - | No | |
| In this class, we analyze the efficiency of companies with the financial data of IT companies. We study Data Envelope Analysis (DEA) and Stochastic Frontier Analysis (SFA) methodologies that are most widely used in enterprise efficiency analysis and compare efficiency with corporate financial data. Efficiency research is one of the key research areas covered in technology management. | |||||||||
| WIS5073 | Introduction to research methods | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| This class provides the basic of research methodology and is recommended for those who are starting their studies at graduate level. In this class, students will learn what it means to build scientific knowledge in the academic community and then learn important concepts such as validity, reliability, and causality, and various research methodologies (e.g., experiments and surveys) and how to design a study using specific research methodology. Further, this class also covers basic statistics including both descriptive and inferential statistics. | |||||||||
| WIS5074 | Data Analytics in Action with Python | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | - | No |
| This course uses Python programming language for practicing examples of descriptive statistics, inferential statistics, regression, clustering analysis, as well as machine learning and deep learning. Its focus is more on applications than theory building. Students are encouraged to present the examples they found, and instructor and other students are doing questions and answers. This study is a social science-based trans-disciplinary course, rather than just a methodology or programming course. | |||||||||
| WIS5075 | Data-Driven Service Design and AI Application | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | Korean | Yes |
| This class addresses new service design approaches based on customer in-service usage datasets. It means that this class aims to explore the role of various datasets in conducting data-drive service design, to investigate how to provide new and unique services based on the datasets. Moreover, applying recent artificial intelligence approaches to the current ICT services is conducted. Finally, both academic implications and industrial applications are conducted. In order to address above-mentioned issues, all students are recommended to enroll in this class in his/her last semester after having a better understanding of research methodologies and data analytics. | |||||||||
| WIS5078 | User experience and artificial intelligence | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | English | Yes |
| This course address several research issues on user experience(UX) and artificial intelligence(AI), focusing on UX trends and lifestyles (Human), AI and computing technologies (Computer), and AI-based UX services (Interaction). The course is conducted through individual or group meeting. Students are required to prepare and complete a research paper through a few steps. The papers can be submitted to an international conference (e.g., CHI, UIST, DIS) or an international journal (e.g., International Journal of Human-Computer Studies, International Journal of Human-Computer Interaction, Computers in Human Behavior). | |||||||||
| WIS5079 | Design and Artificial Intelligence | 3 | 6 | Major | Master/Doctor | Interaction Science | - | No | |
| This course covers several research topics on design and artificial intelligence (AI). Specifically, it focuses on 1) concepts and methods for designing several AI-based services and products, and 2) design decision-making based on the data collected from several technologies. The course aims to learn and discuss state-of-the-art academic literature and industrial cases, inspiring students to find an individual research subject and moreover write a research proposal. | |||||||||
| WIS5080 | Evaluation of User Experience | 3 | 6 | Major | Master/Doctor | 1-4 | Interaction Science | Korean | Yes |
| User experience is not just a measure of whether it is good or bad, it is important to know how much value the experience provides in terms of money. This class helps developing products and services that can provide greater value to users in the future by subdividing IT-related technologies and learning the methodology of how the subdivided functions can be evaluated to users. | |||||||||
| WIS5081 | HCI basic statistics and data science | 3 | 6 | Major | Master/Doctor | Interaction Science | Korean | Yes | |
| Course Overview This course is designed to introduce students to the fundamental concepts of statistics and data science as they apply to Human-Computer Interaction (HCI). Utilizing R programming, a famous tool in data-science, the course aims to equip students with the skills necessary to analyze, interpret, and visualize HCI data effectively. The course covers a range of topics, from basic statistical techniques to more advanced concepts like text mining and data visualization, all within the context of HCI. | |||||||||