Designing Human and Social Education Contents for AI Convergence Education AI 융합교육을 위한 인문사회 컨텐츠 설계

Main Focus

• This course focuses on how technological advances associated with data science and artificial intelligence could enhance social studies education.

• Students are expected:

  • to understand possibilities and limitations of those technologies for social studies education,

  • to develop their abilities to design teaching-learning contents by fusing the technologies and particular subject matters, and

  • to get to possess an introductory level of computer programming for the technologies.

Class Setting

LMS (Learning Management System)

• eTL at Seoul National University (SNU eTL)

  • Lecture notes, materials, references, and etc.

• Class homepage: https://sangillee.snu.ac.kr/classes/2024_1_AI_Edu.html

Class Design

• Classes are conducted in a combination of lectures and lab exercises.

• Lectures include various topics such as:

  • Concepts and Principles of Data Science

  • Population Topics for Contents Development

  • Exploratory Data Analysis

  • Data Science and Communication

  • Data Science and Education

  • Web Scraping as Data Collection

  • Statistical Analysis and Machine Learning

• Each lecture is accompanied by an R exercise.

• During the final two weeks, each student is expected to give a presentation about designing social studies class contents for AI-Integrated education.

Student Responsibilities

Presentation

• Each student should give a presentation on designing social studies class contents for AI-Integrated education.

  • Subject: one or two population Issues (low birth rate, aging, population cliff, regional extinction, school-age population)

  • Data: KOSIS or World Population Prospects 2022

  • Form: 20-minute-long presentation

Term Paper

• Each student should submit a term paper about the presentation above at the end of the semester.

Grading Method

Attendance and Participation (20%)

• Penalty will be given for each absence/lateness.
• Active participation might be awarded.

Presentation (50%)

• Overall performance will be evaluated.

Term Paper (30%)

• The format of the term paper can be freely chosen.
• Due date: 6/18/2024 (Tuesday)

References

Data Science

• Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund, 2023, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, 2nd edition, Boston: O’Reilly. (김설기ㆍ최혜민 옮김, 2019, R을 활용한 데이터 과학: 데이터 불러오기, 정리하기, 변형하기, 시각화하기, 모델링하기, 1판, 서울: 인사이트.)
• Bonnell, Jerry, and Mitsunori Ogihara. 2023. Exploring Data Science with R and the Tidyverse. 1st edition. Chapman; Hall/CRC.
• Irizarry, Rafael A. 2020. Introduction to Data Science: Data Analysis and Prediction Algorithms with R. Boca Raton: CRC Press, Taylor & Francis Group.
• Sarafian, Ron.  Introduction to Data Science. https://bookdown.org/ronsarafian/IntrotoDS/.
• 백영민. 2023. R 기반 데이터 과학: 타이디버스(tidyverse) 접근. 서울: 한나래아카데미.
• 차영준ㆍ박진표, 2022, 데이터과학 입문을 위한 R과 타이디버스, 파주: 자유아카데미.
• 양윤석ㆍ오일석ㆍ강래형, 2019, R로 배우는 데이터 과학: 분석에서 예측을 위한 모델링까지, 서울: 한빛아카데미.

Data Visualization

• Allchin, Carl. 2021. Communicating with Data. O’Reilly Media. (이한호 옮김. 2022. 데이터로 전문가처럼 말하기: 효율적 의사 전달을 위한 데이터 시각화와 비즈니스 스토리텔링의 기술. 서울: 한빛미디어.)

• Dougherty, Jack, and Ilya Ilyankou, 2021, Hands-on Data Visualization: Interactive Storytelling from Spreadsheets to Code, Boston: O’Reilly. (김태헌 옮김, 2022, 핸즈온 데이터 시각화: 효과적인 데이터 시각화 전략부터 20가지 시각화 도구 사용법까지, 한빛미디어)

• Kabacoff, Robert, 2024, Modern Data Visualization with R, Boca Raton: CRC Press.

• Knaflic, Cole Nussbaumer. 2015. Storytelling with Data: A Data Visualization Guide for Business Professionals. New York: John Wiley & Sons. (정사범. 2016. 데이터 스토리텔링: 설득력 있는 프리젠테이션을 위한 데이터 시각화 기법. 서울: 에이콘.)

• Knaflic, Cole Nussbaumer. 2019. Storytelling with Data: Let’s Practice! New York: John Wiley & Sons. (변혜정. 2019. 데이터 스토리텔링 연습: 연습 문제와 다양한 사례로 익히는 데이터 시각화 기법. 서울: 에이콘.)

• Wilke, C., 2019, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, Sebastopol, CA: O’Reilly. (권혜정, 2020, 데이터 시각화 교과서: 데이터 분석의 본질을 살리는 그래프와 차트 제작의 기본 원리와 응용, 의왕: 책만.)

• Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. 2nd ed. Switzerland: Springer. (박진수 옮김, 2017. R로 분석한 데이터를 멋진 그래픽으로 ggplot2. 부천: 프리렉.)

Statistical Foundation

• Ismay, Chester, and Albert Y. Kim. 2019. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse: A ModernDive into R and the Tidyverse. 1st edition. Boca Raton: Chapman; Hall/CRC. (양승훈 옮김, 2023, 데이터 과학을 활용한 통계, 파주: 자유아카데미.)

• Bruce, Peter C., Andrew Bruce, and Peter Gedeck, 2020, Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Second edition, Sebastopol, CA: O’Reilly. (이준용 옮김, 2021, 데이터 과학을 위한 통계, 서울: 한빛미디어.)

• 오진호, 2022, 기초통계학과 데이터 사이언스: R 활용, 파주: 자유아카데미.

Web Scraping

• Pittard, Steve. 2022. Web Scraping with r. https://steviep42.github.io/webscraping/book/.

• 곽기영, 2022, R을 이용한 웹스크레이핑과 데이터분석, 서울: 청람.

Text Analysis

• Hvitfeldt, Emil, and Julia Sigle, 2022, Supervised Machine Learning for Text Analysis in R, Boca Raton: CRC Press.

• Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. 1st edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.

• 한국R사용자회ㆍ안도현, 2022, R 텍스트마이닝, 오픈전자책.

• 김영우, 2021, 쉽게 배우는 R 텍스트 마이닝, 서울: 이지스퍼블리싱.

• 백영민, 2020, R을 이용한 텍스트 마이닝, 파주: 한울엠플러스.

Web Application Development

• Granjon, David, 2022, Outstanding User Interfaces with Shiny, Boaca Raton: CRC Press.

• Fay, Colin, Vincent Guyader, Sebastien Rochette, and Cervan Girard. 2022, Engineering Production-Grade Shiny Apps, Boca Raton: CRC Press.

• Wickham, Hadley, 2021, Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Power by R, Sebastopol, CA: O’REILLY. (이영록 옮김, 2022, R 사용자를 위한 Shiny 마스터 가이드, 서울: 인사이트.)

Machine Learning and Deep Learning

• Lantz, Brett, 2023, Machine Learning with R: Learn Techniques for Building and Improving Machine Learning Models, from Data Preparation to Model Tuning, Evaluation, and Working with Big Data, Fourth edition, Birmingham Mumbai: Packt. (이병욱, 2024, R을 활용한 머신 러닝, 4판, 서울: 에이콘출판사.

• Chollet, François, Tomasz Kalinowski, and J. J. Allaire, 2022, Deep Learning with R, Second edition, Shelter Island, NY: Manning. (박진수 옮김, 2019, 케라스 창시자의 딥러닝 with R, 1판, 파주: 제이펍.)

• Lesmeister, Cory, 2019, Mastering Machine Learning with R: Advanced Machine Learning Techniques for Building Smart Applications with R, Third edition, Birmingham, UK: Packt Publishing. (김종원ㆍ김태영ㆍ류성희ㆍ이호. 2018, R로 마스터하는 머신 러닝: 업무에 활용할 수 있는 선형모델에서 딥러닝까지, 2판, 서울: 에이콘.)

• 장용식ㆍ최진호, 2020, 머신러닝을 활용한 R 데이터 분석, 파주: 생능출판.

• Kuhn, Max, and Julia Silge. 2022. Tidy Modeling with R: A Framework for Modeling in the Tidyverse. 1st edition. Sebastopol, CA: O’Reilly Media.

• Boehmke, Brad, and Brandon M. Greenwell. 2019. Hands-on Machine Learning with R. 1st edition. Boca Raton London New York: Chapman; Hall/CRC.

Introduction to R

• RStudio Education Team, Finding Your Way To R.

• 김기환, 2023, R 프로그래밍(개정판), 웹북

• 곽기영, 2022, R 기초와 활용, 서울: 청람.

• 강전희ㆍ엄동란, 2018, 처음 시작하는 R 데이터 분석, 서울: 한빛미디어.

• 김영우, 2017, 쉽게 배우는 R 데이터 분석, 서울: 이지스퍼블리싱.

Calendar

Week	Date	Contents
1	3/5	Orientation
2	3/12	Concepts and Principles of Data Science 1
3	3/19	Concepts and Principles of Data Science 2
4	3/26	Population Topics for Contents Development 1
5	4/2	Population Topics for Contents Development 2
6	4/9	Population Topics for Contents Development 2
7	4/16	Idle
8	4/23	Exploratory Data Analysis
9	4/30	Data Science and Communication
10	5/7	Data Science and Education
11	5/14	Web Scraping
12	5/21	Statistical Analysis and Machine Learning
13	5/28	Idle
14	6/4	Designing Class Contents for AI-Integrated Education 1
15	6/11	Designing Class Contents for AI-Integrated Education 2