Course Schedule

The curriculum design of the Department’s master’s program emphasizes efficient specialization, enabling students to develop solid research skills and a distinct area of expertise within two years of study. Reflecting the Department’s unique characteristics and the trends in communication technology, the curriculum consists of required courses on research methods and two specialized course modules. A minimum of 27 credits is required for graduation.

The “Interactive Marketing and Design” module focuses on fields such as interactive media, marketing communication, and interaction design. The training goal is to cultivate managerial talent equipped with skills in data analysis, marketing, and design.

The “Popular Culture and Technology” module centers on fields such as popular culture, technology and society, and multimedia integrated creation. This module aims to train professionals with the ability to analyze the cultural and social impacts of communication technologies, understand policy and industry trends, and integrate innovative media practices.

Each module offers 9 to 10 specialized courses, allowing students to choose one as their primary focus for in-depth study. However, students are also required to select a limited number of courses from the other module to ensure a balanced breadth of knowledge.

Required and Prerequisite Courses（6 Credits）

Prerequisite Course: One course in Statistics is required but does not count toward graduation credits. Students who have taken related courses during their undergraduate studies must apply for a course waiver within the first week after enrollment. Those who have not completed such courses during their undergraduate studies are required to take an undergraduate-level Statistics course.

Required Courses: Two courses, Quantitative Research Methods in Communication and Qualitative Research Methods in Communication, are mandatory, totaling 6 credits.

This course introduces the basic concepts, research design, data analysis and organization, and writing approaches of qualitative research methods. It focuses on several commonly used methods in the field of communication, including textual analysis methods such as semiotics, narrative analysis, and discourse analysis, as well as in-depth interviews, participant observation, and focus group interviews. Through literature reading, case discussions, and hands-on practice, students will become familiar with the essence and application of qualitative research and are required to complete an academic paper by the end of the semester.

This course aims to examine your cognitive approach and the scientific approach from both philosophical and empirical perspectives, with a focus on quantitative research methods. What do we know? How do we know what we know? How do we use what we know to establish causal relationships? Are there limitations and barriers? A well-designed study (what constitutes a well-designed study?) enables us to understand, explain, predict, and, more importantly, control phenomena in the real world.

This seminar-based course is divided into three sections:

  1. Analyzing communication research through Kuhn’s paradigm concept and other perspectives.
  2. Discussing core scientific questions—the philosophy of inquiry—and examining the research process from the perspective of social sciences.
  3. Reviewing three research methodologies—experiments, survey research, and content analysis.

Critical reading and writing are essential components of research. Writing without reading is empty talk, while reading without writing is mere bookworming. However, it is through writing that research takes form and materializes.

This course aims to provide students with a solid introduction to statistical thinking and the statistical computing language R. Nowadays, data is pervasive across all aspects of society, and the ability to understand data and translate it into actionable insights has become an essential skill for the future. Additionally, the course will progressively teach students how to use R for statistical analysis, integrating real-world datasets from the media industry to develop students’ practical skills.

Elective Courses（21+ Credits）

Professional Electives: Courses are divided into two modules. Students must select one as their primary module and complete 12 credits from it, while completing 3 credits from the other module, for a total of 15 credits.

Free Electives: Includes 1 credit for research internship. Students may freely choose courses from other departments, totaling 6 credits.

Supplementary Credits: Master’s students with an undergraduate background unrelated to communication studies must take an additional 3 credits of master’s courses or 6 credits of undergraduate courses. Undergraduate Statistics courses can be included in the supplementary credits. However, credits from undergraduate supplementary courses will not count toward graduation credits.

[Interactive Marketing and Design]

This course provides an introductory overview of the General Linear Model (GLM), aiming to develop students’ statistical thinking and practical skills for solving both academic and real-world problems, while also laying the groundwork for advanced statistics courses offered at the university. The course focuses on conceptual understanding and problem-solving rather than mathematical computation. Additionally, it is highly hands-on, requiring students to engage in extensive practical exercises to ensure long-term mastery of core data analysis techniques.

The course is divided into three sections:

  1. Introduction to Data wrangling, Analysis of variance (ANOVA), Analysis of covariance (ANCOVA), Multilevel modeling

  2. Linear Regression and Process Analyses, Linear regression, Mediation analysis, Moderation analysis, Conditional process analysis

  3. Advanced Statistical Methods, Confirmatory factor analysis (CFA), Structural equation modeling (SEM), Cluster analysis

This course explores crisis management strategies from the perspective of public relations theories, examining how different types of organizations respond to various forms of crises. Additionally, the course considers public and societal perspectives, analyzing how an organization’s crisis response impacts public interests and social welfare.

Media psychology is a dynamic and interdisciplinary field that applies to a wide range of topics. This course not only examines traditional research topics (such as media violence) in the context of new media but also focuses on emerging communication phenomena driven by new technologies, such as video games and virtual reality. Through this course, we will explore the psychological mechanisms behind these interactive experiences, gaining a deeper understanding of user motivations and potential effects.

The primary goal of this course is to help students develop a solid understanding of media psychology, analyze the complex interactions between media and individuals, and examine the effects of media on well-being, cognition, and emotions.

The course also encourages students to ask critical research questions, fostering a deeper understanding of media experiences for future exploration. Students will be expected to critically analyze and interpret research to understand how scholars formulate research questions and study media’s impact on human behavior, construct logical and precise arguments to contribute to the field’s knowledge base, and develop a well-structured research proposal by the end of the semester, demonstrating a clear research design and strong theoretical foundation.

Digital marketing is an emerging field, where technological innovation is transforming consumer communication and persuasion. This course aims to provide both theoretical and practical perspectives on digital marketing for students aspiring to become researchers or practitioners in the field. Course readings and discussions will cover a broad range of knowledge and skills, including online marketing, mobile marketing, social media marketing, and MarTech (Marketing Technology) and data analytics.

Students taking this course are required to complete an industry case study and a research paper. This course is designed with three key objectives:

  1. To help students understand key concepts, professional terminology, and tools in digital marketing.
  2. To familiarize students with planning and writing research papers related to digital marketing.
  3. To develop students’ analytical skills for evaluating digital marketing campaigns.

Social media has become deeply integrated into the lives of millions, serving a wide range of purposes. But what exactly is social media? What impacts might social media usage have? And how can we enhance interactions and user experiences on these platforms? This course aims to explore these questions.

Throughout the course, students will learn key concepts, terminology, and theories related to social media, explore various social media platforms, critically analyze the social, political, and psychological effects of social media usage, and propose strategies for improving personal social media habits or redesigning social media platforms. This course is designed to help students develop expertise as social media researchers, designers, or informed users.

This course provides an introduction to Social Network Analysis (SNA), covering both theoretical foundations and practical techniques. Students will explore core theories and concepts in the development of social network analysis, including homophily, triadic closure, network evolution, dyadic relationships, dependency characteristics of network data. Additionally, students will engage in hands-on exercises to develop skills in both basic and advanced social network analysis.

The final course requirement is a research paper, in which students must apply social network theory and analysis methods to investigate a research question. The paper should meet the academic standards of communication or related social science conferences.

This course provides a methodological foundation for big data analysis in the fields of communication and social sciences (computational communication research) through hands-on practice. Using real-world datasets, the course explores various applications of big data analysis, particularly text mining, covering data collection (web scraping), data cleaning, transformation, and annotation (data wrangling), model selection and analysis, and result interpretation and data visualization. If time permits, the course will briefly introduce popular open-source machine learning tools, such as: scikit-learn (https://scikit-learn.org/), TensorFlow (https://www.tensorflow.org/), and Weka (https://www.cs.waikato.ac.nz/ml/weka/).

A key challenge for communication researchers and social scientists is the hype surrounding big data, which claims to revolutionize scientific discovery and innovation, leading to intense debates in the field. Some argue that big data is merely an increase in volume, and that searching for patterns in observational data often results in spurious correlations that are difficult to reproduce and generalize. Others believe that big data excels in predictive capabilities, offering new analytical possibilities. The core controversy lies in the data-driven, inductive, and predictive nature of big data, which seemingly contradicts the theory-driven, deductive, and explanatory nature of traditional scientific methods.

This course will utilize R and Python as primary tools. Students are required to bring a laptop with the latest versions of R, RStudio, and the Anaconda distribution of Python installed for every class.

This is a graduate-level course that introduces topics and research in Human-Computer Interaction (HCI) while teaching relevant research methods and skills. The course helps students learn how to apply these methods in the HCI context and effectively communicate user research findings.

User research aims to learn how to effectively gather information from people—whether subject matter experts, project stakeholders, or the general public. Effective user research can extract reliable and actionable insights from key individuals related to a project. Whether you aspire to work in customer-facing roles at startups or learn how to collaborate with colleagues in professional settings, acquiring the skill to collect, plan, and analyze valuable information is a thoughtful, structured, and analytical process. This course provides an overview and introduction to various user research techniques. Each week, the instructor will lecture on HCI-related readings. The class includes student presentations on methodological papers, hands-on workshops, and assignment reviews with feedback.

Throughout the semester, students will engage with multiple user research methods, including field studies, interviews, and diary studies. We will explore sample applications for each method and examine contexts where they are applicable. Additionally, we will discuss the research process, including selecting research sites, sampling methods, and interacting with participants. The course will also cover how to effectively and informatively communicate user research findings and explore ethical issues related to user research.

This course aims to integrate communication theories and data science to address real-world problems through practical applications.

This course is conducted as a synchronous distance learning program and is designed for graduate students from the Department of Communication and Technology, the College of Hakka Studies, the College of Humanities, Arts, and Social Studies, the College of Computer Science, and health-related graduate programs from Yang Ming Campus, including medicine, nursing, and public health disciplines. The course introduces four major theoretical frameworks and methodologies in health communication, exploring health communication within individual, community, and public contexts, as well as its applications across physiological, psychological, and social dimensions. The four theoretical frameworks covered in this course include: (1) Messages and Behavior Change Theories (2) Relationship Theories (3) Managing Information and Risk Theories (4) Health Disparities Theories

Additionally, the course examines the adoption, use, and effects of emerging media technologies in health risk communication processes. Topics include the design of public health persuasion and public relations messages, how the public perceives, processes, and reacts to health risk communication, and the impact of emerging media technologies on public health attitudes and behaviors. The course further investigates the use and impact of various emerging media technologies in health communication, including online news media, mass communication media, social media, mobile media, video games, artificial intelligence, robotics, virtual/augmented/mixed/extended reality, and smart devices. Key discussion topics include the media framing of health communication and public health risk messaging strategies, the search and use behaviors of health information through emerging media technologies, and the effectiveness of user empowerment in shaping health attitudes and behavioral changes.

[Popular Culture and Technology]