WNF11000 Practice of Data Analysis for Education and Social Data Science Programs II

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program

Lecture Code WNF11000 Subject Classification Specialized Education

Subject Name 多分野データ解析実践演習 II

Subject Name
（Katakana）

Subject Name in
English Practice of Data Analysis for Education and Social Data Science Programs II

Instructor HASHIMOTO JUNYA,TANAKA HIDEYUKI,MATUBARA KIMINORI,NAKASHIMA KENICHIRO,FAN YIZHOU,MIYAJIMA KIRIE,MURASAWA MASATAKA,TOYA AKIHIRO

Instructor
(Katakana) ハシモト　ジュンヤ,タナカ　ヒデユキ,マツバラ　キミノリ,ナカシマ　ケンイチロウ,ハン　イシュウ,ミヤジマ　キリエ,ムラサワ　マサタカ,トヤ　アキヒロ

Campus Higashi-Hiroshima Semester/Term 2nd-Year,  First Semester,  First Semester

Days, Periods, and Classrooms (1st) Inte：EDU K115

Lesson Style Practical Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

Exercise-centered, discussion, student presentations

Credits 1.0 Class Hours/Week 　 Language of Instruction J : Japanese

Course Level 7 : Graduate Special Studies

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 05 : Sociology

Eligible Students Second Year Students of Education Data Science program

Keywords

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline The objective of this course is to foster the ability of students from different specialized fields to collaboratively solve problems by analyzing specific data together with experts from other fields. Students from the Educational Data Science Program and the Social Data Science Program will analyze real data and work together to solve problems through presentations and discussions.

Class Schedule lesson1  Guidance
lesson2  Task Setting ①: Sharing Tasks
lesson3  Task Setting ②: Collecting Materials
lesson4  Task Setting ③: Group Discussion
lesson5  Task Setting ④: Determining Tasks
lesson6  Data Analysis ①: Considering Methods of Analysis
lesson7  Data Analysis ②: Conducting Analysis
lesson8  Data Analysis ③: Summarizing Analysis
lesson9  Data Analysis ④: Preparation for Interim Presentation
lesson10  Interim Presentation (Collaborating with the Social Data Science Program)
lesson11  Reporting Analysis Results ①: Considering Re-analysis Based on Interim Presentation
lesson12  Reporting Analysis Results ②: Conducting Analysis
lesson13  Reporting Analysis Results ③: Summarizing Analysis
lesson14  Reporting Analysis Results ④: Final Presentation (Collaborating with the Social Data Science Program)
lesson15  Summary

Reports will be assigned based on interim and final presentations.

Text/Reference
Books,etc. Introduce useful materials for exercises as appropriate.

PC or AV used in
Class,etc. Handouts, Microsoft Teams, moodle

(More Details)

Learning techniques to be incorporated Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report

Suggestions on
Preparation and
Review Lessons 1-5: Organize your own awareness of issues in education.
Lessons 6-10: Consider appropriate methods from the data analysis methods you have learned so far.
Lessons 11-15: Based on the discussions from the interim presentation, consider more appropriate directions for analysis.

Requirements "Practice of Data Analysis for Education and Social Data Science Programs I" is a common subject for the Social Data Science Program and the Education Data Science Program. This course (Lecture Code: WNF11000) is aimed at students in Education Data Science Program. Students in Social Data Science Program should take "Practice of Data Analysis for Education and Social Data Science Programs I" for Social Data Science Program.
Students are required to have completed “Practice of Data Analysis for Education and Social Data Science Program I” during the first year of the Master's program.

Grading Method Evaluation will be based on overall performance in class participation, interim presentation, final presentation, and reports.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

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	2026Year	School/Graduate School	Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program
Lecture Code	WNF11000	Subject Classification	Specialized Education
Subject Name	多分野データ解析実践演習 II
Subject Name （Katakana）
Subject Name in English	Practice of Data Analysis for Education and Social Data Science Programs II
Instructor	HASHIMOTO JUNYA,TANAKA HIDEYUKI,MATUBARA KIMINORI,NAKASHIMA KENICHIRO,FAN YIZHOU,MIYAJIMA KIRIE,MURASAWA MASATAKA,TOYA AKIHIRO
Instructor (Katakana)	ハシモト　ジュンヤ,タナカ　ヒデユキ,マツバラ　キミノリ,ナカシマ　ケンイチロウ,ハン　イシュウ,ミヤジマ　キリエ,ムラサワ　マサタカ,トヤ　アキヒロ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, First Semester, First Semester
Days, Periods, and Classrooms	(1st) Inte：EDU K115
Lesson Style	Practical	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)
Exercise-centered, discussion, student presentations
Credits	1.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	7 : Graduate Special Studies
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	05 : Sociology
Eligible Students	Second Year Students of Education Data Science program
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	The objective of this course is to foster the ability of students from different specialized fields to collaboratively solve problems by analyzing specific data together with experts from other fields. Students from the Educational Data Science Program and the Social Data Science Program will analyze real data and work together to solve problems through presentations and discussions.
Class Schedule	lesson1 Guidance lesson2 Task Setting ①: Sharing Tasks lesson3 Task Setting ②: Collecting Materials lesson4 Task Setting ③: Group Discussion lesson5 Task Setting ④: Determining Tasks lesson6 Data Analysis ①: Considering Methods of Analysis lesson7 Data Analysis ②: Conducting Analysis lesson8 Data Analysis ③: Summarizing Analysis lesson9 Data Analysis ④: Preparation for Interim Presentation lesson10 Interim Presentation (Collaborating with the Social Data Science Program) lesson11 Reporting Analysis Results ①: Considering Re-analysis Based on Interim Presentation lesson12 Reporting Analysis Results ②: Conducting Analysis lesson13 Reporting Analysis Results ③: Summarizing Analysis lesson14 Reporting Analysis Results ④: Final Presentation (Collaborating with the Social Data Science Program) lesson15 Summary Reports will be assigned based on interim and final presentations.
Text/Reference Books,etc.	Introduce useful materials for exercises as appropriate.
PC or AV used in Class,etc.	Handouts, Microsoft Teams, moodle
(More Details)
Learning techniques to be incorporated	Discussions, PBL (Problem-based Learning)/ TBL (Team-based Learning), Post-class Report
Suggestions on Preparation and Review	Lessons 1-5: Organize your own awareness of issues in education. Lessons 6-10: Consider appropriate methods from the data analysis methods you have learned so far. Lessons 11-15: Based on the discussions from the interim presentation, consider more appropriate directions for analysis.
Requirements	"Practice of Data Analysis for Education and Social Data Science Programs I" is a common subject for the Social Data Science Program and the Education Data Science Program. This course (Lecture Code: WNF11000) is aimed at students in Education Data Science Program. Students in Social Data Science Program should take "Practice of Data Analysis for Education and Social Data Science Programs I" for Social Data Science Program. Students are required to have completed “Practice of Data Analysis for Education and Social Data Science Program I” during the first year of the Master's program.
Grading Method	Evaluation will be based on overall performance in class participation, interim presentation, final presentation, and reports.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
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.

Back to syllabus main page