| Academic Year |
2025Year |
School/Graduate School |
Graduate School of Humanities and Social Sciences (Master's Course) Division of Educational Sciences Education Data Science Program |
| Lecture Code |
WNF09050 |
Subject Classification |
Specialized Education |
| Subject Name |
教育における機械学習活用法 |
Subject Name
（Katakana） |
|
Subject Name in
English |
Application of Machine Learning in Education |
| Instructor |
To be announced. |
Instructor
(Katakana) |
タントウキョウインミテイ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, Second Semester, 3Term |
| Days, Periods, and Classrooms |
(3T) Mon1-4：EDU K115 |
| Lesson Style |
Seminar |
Lesson Style
(More Details) |
Online (simultaneous interactive) |
| Lecture-oriented, Practice-oriented, |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
J
:
Japanese |
| Course Level |
5
:
Graduate Basic
|
| Course Area（Area） |
24
:
Social Sciences |
| Course Area（Discipline） |
07
:
Education |
| Eligible Students |
|
| Keywords |
Evidence-based education, Statistical analysis, Open data |
| 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 goal of this class is to learn practical skills for using machine learning in educational settings. |
| Class Schedule |
lesson1 Introduction to the Use of Machine Learning and AI in Education (Guidance)
lesson2 The History of AI and Its Technological Developments
lesson3 The Mechanisms of Machine Learning and Its Applications
lesson4 The Mechanisms of Large-Scale Language Models and Their Performance in Education
lesson5 AI-Assisted Subject Instruction (1)
lesson6 AI-Assisted Subject Instruction (2)
lesson7 AI-Assisted Educational Materials Development
lesson8 AI-Assisted School Management Improvement
lesson9 AI-Assisted Personalized Learning
lesson10 AI-Assisted Educational Assessment
lesson11 Approaches to Implementing AI in Educational Settings
lesson12 AI and the Role of Teachers
lesson13 Ethical Issues of AI in Education
lesson14 Designing AI-Enhanced Education (1)
lesson15 Designing AI-Enhanced Education (2)
Your grade will be based on a final exam or a final report. We will tell you the exact method of grading once we have decided on it. We will post the information on the Momiji bulletin board. |
Text/Reference
Books,etc. |
Nothing |
PC or AV used in
Class,etc. |
Handouts, Microsoft Teams, Zoom |
| (More Details) |
|
| Learning techniques to be incorporated |
Discussions |
Suggestions on
Preparation and
Review |
The Japanese version of the syllabus tells students what they need to study for each class (through first lesson and fifteen lesson). Please refer there.
It is preferable to have already completed the lecture of basic statistical analysis (course code WNF02000) or to have the equivalent knowledge and skills in statistical analysis and the use of R.
|
| Requirements |
Again, it is preferable to have already completed the lecture of basic statistical analysis (course code WNF02000) or to have the equivalent knowledge and skills in statistical analysis and the use of R. |
| Grading Method |
Your grade will be based on a final exam or a final report. We will let you know as soon as we know which one. You can check the Momiji bulletin board or other places for this information. |
| 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. |