| 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 |
WNF12000 |
Subject Classification |
Specialized Education |
| Subject Name |
学校教育におけるDX |
Subject Name
（Katakana） |
|
Subject Name in
English |
DX in School Education |
| 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 K203 |
| 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） |
07
:
Education |
| Eligible Students |
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 |
In this course, in collaboration with Higashi-Hiroshima City, students will utilize and analyze data from school settings to identify educational outcomes and perspectives for improvement. Through this process, the findings will be fed back into educational practice, thereby fostering practical competencies in educational data science. |
| Class Schedule |
lesson1 Guidance
lesson2 Task Setting ①: Sharing Tasks in School Education
lesson3 Task Setting ②: Collecting Materials
lesson4 Task Setting ③: Group Discussion
lesson5 Task Setting ④: Determining Tasks
lesson6 Examining Solutions ①: Exploring Possible Approaches
lesson7 Examining Solutions ②: Developing an Implementation Plan
lesson8 Examining Solutions ③: Finalizing the Implementation Plan
lesson9 Examining Solutions ④: Preparation for Midterm Presentation
lesson10 Examining Solutions ⑤: Midterm Presentation
lesson11 Implementing Solutions ①: Preparing for Implementation
lesson12 Implementing Solutions ②: Carrying Out the Implementation
lesson13 Implementing Solutions ③: Reviewing and Summarizing the Implementation
lesson14 Implementing Solutions ④: Final Presentation
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 school education.
Lessons 6-10: Examine and consider appropriate methods for addressing the identified issue.
Lessons 11-15: Based on the discussions from the midterm presentation, explore more appropriate directions for implementation. |
| Requirements |
This course is closely coordinated with “Practice of Data Analysis for Education and Social Data Science Programs II” and is designed to complement its content. |
| 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. |