30103001 Data science for education

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School Liberal Arts Education Program

Lecture Code 30103001 Subject Classification Information and Data Science Courses

Subject Name 教育のためのデータサイエンス[1教一,1教自,1教音,1教造]

Subject Name
（Katakana）キョウイクノタメノデータサイエンス

Subject Name in
English Data science for education

Instructor TANAKA HIDEYUKI,HASHIMOTO JUNYA,SUZUKI HIROYUKI,NAGAMATSU MASAYASU

Instructor
(Katakana) タナカ　ヒデユキ,ハシモト　ジュンヤ,スズキ　ヒロユキ,ナガマツ　マサヤス

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Inte：Online

Lesson Style Lecture Lesson Style
(More Details) 　

Credits 2.0 Class Hours/Week 　 Language of Instruction J : Japanese

Course Level 1 : Undergraduate Introductory

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 08 : Curriculum and Instruction Sciences

Eligible Students

Keywords problem solving, decision making, and critical thinking

Special Subject for Teacher Education 　 Special Subject 　

Class Status within
Liberal Arts Education This course is designed for students who want to become teachers. Students will learn the basics of data science specifically for teacher training.

In this course, students in the liberal arts and science courses will learn data science for education using Excel. Students who have backgrounds in STEMs and are interested in a data science courses with more scientific contents are recommended to take such courses.

The School of Education offers a special course "Data utilization and data science for solving problems in educational scenes"
Expected Outcome The aim of this course is that students will be able to use various data obtained in school to improve education, when they will become teachers.

Class Objectives
/Class Outline Data-based education is expected to become more and more important in the field of education. This course deals with data science at the literacy level that those who want to be teachers should learn. Students will learn data science, handling data in educational settings.

Following topics are covered:
-Relationship between data science and education.
-Data in education: data collection and research, quantitative and qualitative data.
-Collection of data of students performance: data aggregation, sorting, data cleansing, etc.
-Understanding students performance: visualization of data such as graphs and histograms, representative values, etc.
-Analyzing factors of performance: correlation analysis.
-Things to keep in mind when handling data (ethics and precautions).

Students will use Excel for exercises.

Class Schedule Lesson 1: (online, lecture)
Guidance: Data science and education (Introduction to data science, motivation, questionnaire, etc.)
Lesson 2: (online, lecture)
Data in education (Data of students attendance, grades, etc., and learning progress, PPDAC cycle, types of data)
Lesson 3: (on-demand, lecture)
Collecting student results (Tabulation, sorting, data cleansing, etc.).
Lesson 4: (on-demand, exercise)
Collecting student results (Tabulation, sorting, data cleansing, etc.).
Lesson 5: (on-demand, exercise)
Understanding trends in students results (representative values, etc.).
Lesson 6: (on-demand, lecture)
Understanding trends of students results at a glance (graphs, histograms, etc.)
Lesson 7: (on-demand, lecture)
Exploring factors affecting students results (correlation analysis)
Lesson 8: (on-demand, exercise)
Exploring factors affecting students results (correlation analysis)
Lesson 9: (online, lecture)
Ethical issues and things to keep in mind in dealing with data.
Lesson 10: (online, exercise)
Ethical issues and things to keep in mind in dealing with data.
Lesson 11: (online, lecture)
Data science for pupils and students (A sample for data science for children, observation based on data)
Lesson 12: (online, lecture)
Data science for pupils and students (basis for data in education, discussion on students results)
Lesson 13: (online, exercise)
Analysis of data in education
Lesson 14: (online, exercise)
Analysis and discussion of data in education
Lesson 15: (online)
Summary of the course.

Evaluation will be made comprehensively by reports and examinations.

Lessons for "online" are given by simultaneous interactive classes in Tema. You can find the link from the Bb9.
In lessons for "on-demand", watch the lesson movie and make exercises.

Text/Reference
Books,etc. Texts and references, etc. are distributed in class or in advance, or students are informed how to obtain them.

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Lesson 1: Understand data science and education.
Lesson 2: Learn several data in education.
Lesson 3: Deepen your understanding of how to collect data upcoming practices.
Lesson 4: Focus on handling data and collecting data.
Lesson 5: Think about how the trends of students results are used in school education.
Lesson 6: Learn how to visualize students results to understand the trends.
Lesson 7: Learn correlation analysis.
Lesson 8: Explore the factors that affect students performance through correlation analysis.
Lesson 9: Learn ethical issues and things to keep in mind in dealing with data.
Lesson 10: Deepen understanding of things to keep in mind in dealing with data.
Lesson 11: Understand data in education
Lesson 12: Deepen understanding of data in education
Lesson 13: Analyze data in education based on the knowledge you learned here
Lesson 14: Make discussion based on data in education
Lesson 15: Review what you have learned and deepen your understanding.

Requirements Lessons are conducted through online (on-demand and simultaneous interactive). Use of your computer is required. Spreadsheet (Excel) will be used.

Grading Method Students will be evaluated comprehensively based on tests and reports and etc. Attendance and reports on the 13th and 14th classes are important. Note that some assignments are online.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Dates: July 13 (Saturday), July 27 (Saturday), August 3 (Saturday), Time: Sessions 1-5 (8:45-17:50)
We basically expect you to study the on-demand contents within the above time frame. Your questions will be answered during the above time period.

If you are unable to attend the class due to a conflict with an official class, please email your supervisor and tutor in CC. If you are unable to attend the class for any other reason, it is recommended that you take another class.

Small assignments may be given in simultaneous interactive lessons. If your network environment is not good, access the site from the university.

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2024Year	School/Graduate School	Liberal Arts Education Program
Lecture Code	30103001	Subject Classification	Information and Data Science Courses
Subject Name	教育のためのデータサイエンス[1教一,1教自,1教音,1教造]
Subject Name （Katakana）	キョウイクノタメノデータサイエンス
Subject Name in English	Data science for education
Instructor	TANAKA HIDEYUKI,HASHIMOTO JUNYA,SUZUKI HIROYUKI,NAGAMATSU MASAYASU
Instructor (Katakana)	タナカ　ヒデユキ,ハシモト　ジュンヤ,スズキ　ヒロユキ,ナガマツ　マサヤス
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Inte：Online
Lesson Style	Lecture	Lesson Style (More Details)

Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	1 : Undergraduate Introductory
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	08 : Curriculum and Instruction Sciences
Eligible Students
Keywords	problem solving, decision making, and critical thinking
Special Subject for Teacher Education		Special Subject
Class Status within Liberal Arts Education	This course is designed for students who want to become teachers. Students will learn the basics of data science specifically for teacher training. In this course, students in the liberal arts and science courses will learn data science for education using Excel. Students who have backgrounds in STEMs and are interested in a data science courses with more scientific contents are recommended to take such courses. The School of Education offers a special course "Data utilization and data science for solving problems in educational scenes"
Expected Outcome	The aim of this course is that students will be able to use various data obtained in school to improve education, when they will become teachers.
Class Objectives /Class Outline	Data-based education is expected to become more and more important in the field of education. This course deals with data science at the literacy level that those who want to be teachers should learn. Students will learn data science, handling data in educational settings. Following topics are covered: -Relationship between data science and education. -Data in education: data collection and research, quantitative and qualitative data. -Collection of data of students performance: data aggregation, sorting, data cleansing, etc. -Understanding students performance: visualization of data such as graphs and histograms, representative values, etc. -Analyzing factors of performance: correlation analysis. -Things to keep in mind when handling data (ethics and precautions). Students will use Excel for exercises.
Class Schedule	Lesson 1: (online, lecture) Guidance: Data science and education (Introduction to data science, motivation, questionnaire, etc.) Lesson 2: (online, lecture) Data in education (Data of students attendance, grades, etc., and learning progress, PPDAC cycle, types of data) Lesson 3: (on-demand, lecture) Collecting student results (Tabulation, sorting, data cleansing, etc.). Lesson 4: (on-demand, exercise) Collecting student results (Tabulation, sorting, data cleansing, etc.). Lesson 5: (on-demand, exercise) Understanding trends in students results (representative values, etc.). Lesson 6: (on-demand, lecture) Understanding trends of students results at a glance (graphs, histograms, etc.) Lesson 7: (on-demand, lecture) Exploring factors affecting students results (correlation analysis) Lesson 8: (on-demand, exercise) Exploring factors affecting students results (correlation analysis) Lesson 9: (online, lecture) Ethical issues and things to keep in mind in dealing with data. Lesson 10: (online, exercise) Ethical issues and things to keep in mind in dealing with data. Lesson 11: (online, lecture) Data science for pupils and students (A sample for data science for children, observation based on data) Lesson 12: (online, lecture) Data science for pupils and students (basis for data in education, discussion on students results) Lesson 13: (online, exercise) Analysis of data in education Lesson 14: (online, exercise) Analysis and discussion of data in education Lesson 15: (online) Summary of the course. Evaluation will be made comprehensively by reports and examinations. Lessons for "online" are given by simultaneous interactive classes in Tema. You can find the link from the Bb9. In lessons for "on-demand", watch the lesson movie and make exercises.
Text/Reference Books,etc.	Texts and references, etc. are distributed in class or in advance, or students are informed how to obtain them.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Lesson 1: Understand data science and education. Lesson 2: Learn several data in education. Lesson 3: Deepen your understanding of how to collect data upcoming practices. Lesson 4: Focus on handling data and collecting data. Lesson 5: Think about how the trends of students results are used in school education. Lesson 6: Learn how to visualize students results to understand the trends. Lesson 7: Learn correlation analysis. Lesson 8: Explore the factors that affect students performance through correlation analysis. Lesson 9: Learn ethical issues and things to keep in mind in dealing with data. Lesson 10: Deepen understanding of things to keep in mind in dealing with data. Lesson 11: Understand data in education Lesson 12: Deepen understanding of data in education Lesson 13: Analyze data in education based on the knowledge you learned here Lesson 14: Make discussion based on data in education Lesson 15: Review what you have learned and deepen your understanding.
Requirements	Lessons are conducted through online (on-demand and simultaneous interactive). Use of your computer is required. Spreadsheet (Excel) will be used.
Grading Method	Students will be evaluated comprehensively based on tests and reports and etc. Attendance and reports on the 13th and 14th classes are important. Note that some assignments are online.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Dates: July 13 (Saturday), July 27 (Saturday), August 3 (Saturday), Time: Sessions 1-5 (8:45-17:50) We basically expect you to study the on-demand contents within the above time frame. Your questions will be answered during the above time period. If you are unable to attend the class due to a conflict with an official class, please email your supervisor and tutor in CC. If you are unable to attend the class for any other reason, it is recommended that you take another class. Small assignments may be given in simultaneous interactive lessons. If your network environment is not good, access the site from the university.
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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