For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| PSY4007 | Multivariate analysis and statistical learning | 3 | 6 | Major | Bachelor/Master | Psychology | - | No | |
| This course covers principles and practice of multivariate data analysis in psychology and related fields. Applications of multiple regression, logistic regression, principal component analysis, factor analysis, cluster analysis and other multivariate techniques for psychological research will be illustrated. In addition, supervised learning and unsupervised learning will be discussed in relation to the multivariate techniques. This course is designed for graduate students or 3rd- or 4th-year undergraduate students in psychology. | |||||||||
| PSY5132 | Regression and Factor Analysis | 3 | 6 | Major | Master/Doctor | 1-4 | Psychology | - | No |
| This course is divided into two parts: regression analysis, factor analysis. In the first part, regression method of identifying optimal combination of predictors are covered. In the second part, estimation of factor structure latent in the data of measured variables are covered. Students are required to understand the statistical theories underlying the methods, analyze practical data, and interpret the results finally. | |||||||||
| PSY5136 | Structural Equation Modeling and Related Methods | 3 | 6 | Major | Master/Doctor | 1-4 | Psychology | - | No |
| This course is an introduction to structural equation modeling(SEM) which is quite broad and deep as a multivariate method. Students are required to understand the basic theory of SEM and to analyze multivariate data with hypotheses of linear structural relations. Also they are trained to be able to understand published articles demonstrating use of SEM as the major analytic method. | |||||||||
| PSY5188 | Structural Equation Modeling | 3 | 6 | Major | Master/Doctor | Psychology | Korean | Yes | |
| This course covers fundamentals of latent variable modeling, path analysis, and structural equation modeling, combining theoretical and practical perspectives. The course is designed to provide details of structural equation modeling, from the statistical underpinnings to how to conduct various types of structural equation analyses. | |||||||||
| SIC5011 | Collaborative Research Workshop | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| Individual research project on closing multiple gaps including economic gap, health/life cycle gap, and AI information technology gap. The topic will be determined under the guidance of an advisor. | |||||||||
| SIC5012 | Social Innovation Convergence Internship | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course is intended to teach students how to translate knowledge learned from the first two semesters to social innovation practices. Students will learn methods for applying theoretical knowledge to real-world industrial settings and making decisions using information. | |||||||||
| SIC5013 | Smart device and mental health | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| The use of smart devices has become a big part of our lives. this class will teach the effects of the smart devices on the mental health of infants, children, adults and the elderly according to each stage of development. Specifically, we will focus on the impact of smart devices on children's awareness and brain/physical/emotional development and mental illness . also we will discuss improving mental health using smart devices. | |||||||||
| SIC5020 | Longitudinal Data Analysis | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course covers empirical frameworks for drawing causal inferences from longitudinal data. Topics include longitudinal study design, exploring longitudinal data, random effects and fixed effects models; and quasi-experimental research design such as diff-in-diffs regression, propensity score matching, and regression discontinuity design. | |||||||||
| SIC5021 | Social Big Data Analysis | 3 | 9 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims to provide the students with a knowledge and skill about how to collect, save and analyze online text data. Specifically it seeks to help students scrap and crawl text data via online news sites, blogs, and SNS and analyze the data using unsupervised machine learning techniques. It focuses on how to use beginners or intermediate levels of natural learning process (NLP) techniques and how to visualize the corpus. The analyzes center around probabilistic topi models in different levels, ranging from LDA, to DTM and ETM. This course is designed to help students apply the techniques obtained to the data the students themselves crawl and write a research note that could potentially be submitted for journal publication. | |||||||||
| SIC5022 | Predictive Modeling using Regression Analysis | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course offers an introduction to predictive analytics and statistical learning using regression techniques. Students will be exposed to technical aspects of regression analysis, model selection, regularization, and data pre-processing, and learn how to use a programmable software in estimating and validating predictive models. This course prepares students for a more advanced course in machine learning. | |||||||||
| SIC5023 | Children’s education and social change | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| As the social environment changes, children’s educational systems and methods, access and demand for education are changing, and educational disparities are widening. On the other hand, we can lead social change in the desired direction through children’s education. Theories and examples of these two directions will be examined, and educational gaps and solutions will be discussed based on data. | |||||||||
| SIC5025 | Family Crisis Poverty and Children | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This class introduces students structural problems and crisis within the various family situations and its relations to social economics status, cultural background and gender. In particular, the influences of family crisis on children and adolescent’s development and mental health. | |||||||||
| SIC5028 | Machine Learning with Python | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | Korean | Yes | |
| This course aims that students implement machine learning algorithms with Python programming. In the beginning of this course, students will learn the basics about Python programming. In the latter part, students will implement various machine learning algorithms such as supervised and unsupervised learning with Python so that they could exactly understand the algorithms. | |||||||||
| SIC5030 | Child and Family: Risk, Human Right, and Health | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| This course will provide an overview of theoretical issues pertaining to the study of child and adolescence health in contexts of family risk and protective factors. We will begin by discussing various approaches to conceptualizing exposure to risk and adversity. Further, we will review theoretical frameworks and recent empirical studies to improve the understanding of developmental processes, comorbidity, preventive intervention, and individual and family rights that are associated with health outcomes. | |||||||||
| SIC5031 | Risk Behaviors and Resilience in Adolescence and Adulthood | 3 | 6 | Major | Master/Doctor | Convergence for Social Innovation | - | No | |
| The course focuses on risk behaviors and resilience in adolescence and adulthood. Emphasis is on examining adolescent’s and adult’s risk behaviors from developmental and ecological perspectives. We will review theoretical frameworks and current methodological application based on recent empirical studies. In addition, we will discuss developmental pathways of various risk behaviors, cooccurrences with mental health. Lastly, multilevel contexts that influence resilience in adolescence and adulthood will be discussed. | |||||||||