CC232004 Data Utilization and Data Science for Solving Problems in Educational Scenes

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Education

Lecture Code CC232004 Subject Classification Specialized Education

Subject Name 教育現場の問題解決に向けたデータ活用・データサイエンス

Subject Name
（Katakana）キョウイクゲンバノモンダイカイケツニムケタデータカツヨウ・データサイエンス

Subject Name in
English Data Utilization and Data Science for Solving Problems in Educational Scenes

Instructor TANAKA HIDEYUKI,HASHIMOTO JUNYA,NAGAMATSU MASAYASU,KAWADA KAZUO

Instructor
(Katakana) タナカ　ヒデユキ,ハシモト　ジュンヤ,ナガマツ　マサヤス,カワダ　カズオ

Campus Higashi-Hiroshima Semester/Term 2nd-Year, Second Semester, 3Term

Days, Periods, and Classrooms (3T) Tues1-4：EDU L310

Lesson Style Lecture/Seminar Lesson Style
(More Details) 　

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

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 08 : Curriculum and Instruction Sciences

Eligible Students

Keywords Data science, pedagogy ,problem solving, decision making, critical thinking

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) Students are recommended to take the course after taking data science education in liberal arts.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Secondary School Technology and Information Education
（Knowledge and Understanding）
・To have understanding and knowledge of information education.

Class Objectives
/Class Outline Data-based education will become more and more important in the future. This class covers data science at the applied basic level for pre-service teachers.

Students will learn regression and statistical hypothesis test using Excel, and machine learning and AI basics using Python. They will study the basics of data utilization and data science for solving problems in the field of education.

Class Schedule Lesson 1 (Class is given by lecture)
Guidance, questionnaire, etc.

Lesson 2 (Class is given by lecture)
On learning in Education and Machine Learning

Lesson 3 (Class is given by lecture with exercise)
Predicting student's learning status: predicting the future by regression (Excel is used)

Lesson 4 (Class is given by lecture with practice)
Is there a difference between data of two class of students?: Statistical hypothesis tests (Excel is used)

Lesson 5 (Class is given by lecture with practice)
Introduction to programming with Python

Lesson 6 (Class is given by lectures and exercises)
Practice of programming with Python.

Lesson 7 (Class is given by lecture and exercises)
About basics of machine learning

Lesson 8 (Class is given by lecture and practice)
Classifying data (Data visualization with Python)

Lesson 9 (Class is conducted through exercises)
Clustering Data (Clustering with Python)

Lesson 10 (Class is given by lecture and exercises)
Regression analysis and least-squares methods (Python is used)

Lesson 11 (Class is given by lecture and exercises)
Modeling of human skills using TKL parameters.

Lesson 12 (Class is given by lecture and exercise)
Modeling of human skills using TKL parameters. (prediction, decision)

Lesson 13 (Class is conducted through exercises)
Planning the use of data science in education. Problems.

Lesson 14 (Class is conducted through exercises)
Planning the use of data science in education. Solutions.

Lesson 15 (Class is given by lecture)
Summary

Students should submit reports and take exams.

Text/Reference
Books,etc. Textbooks, reference books, etc. will be specified and distributed as needed.

PC or AV used in
Class,etc. 　

(More Details) You will use your laptop computer.

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Lesson 1: Understand the background of data science in education.
Lesson 2: Think about the differences and similarities between human learning and machine learning.
Lesson 3: Understand prediction by regression.
Lesson 4: Understand how to perform a statistical hypothesis test.
Lesson 5: Learn Python programming.
Lesson 6: Deepen your understanding of Python.
Lesson 7: Understand the basics of machine learning.
Lesson 8: Understand classification of educational data using Python.
Lesson 9: Understand clustering of educational data using Python.
Lesson 10: Understand regression analysis and least-squares methods.
Lesson 11: Learn how to model the learning process of human.
Lesson 12: Deepen your understanding of acquisition models.
Lesson 13: Students will deal with educational data. Review lessons 1-12.
Lesson 14: Students will study in groups. Please discuss actively and deepen your understanding.
Lesson 15: Review the whole class and deepen your understanding.

Requirements

Grading Method Comprehensive evaluation through reports and tests.

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	2024Year	School/Graduate School	School of Education
Lecture Code	CC232004	Subject Classification	Specialized Education
Subject Name	教育現場の問題解決に向けたデータ活用・データサイエンス
Subject Name （Katakana）	キョウイクゲンバノモンダイカイケツニムケタデータカツヨウ・データサイエンス
Subject Name in English	Data Utilization and Data Science for Solving Problems in Educational Scenes
Instructor	TANAKA HIDEYUKI,HASHIMOTO JUNYA,NAGAMATSU MASAYASU,KAWADA KAZUO
Instructor (Katakana)	タナカ　ヒデユキ,ハシモト　ジュンヤ,ナガマツ　マサヤス,カワダ　カズオ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 3Term
Days, Periods, and Classrooms	(3T) Tues1-4：EDU L310
Lesson Style	Lecture/Seminar	Lesson Style (More Details)

Credits	2.0	Class Hours/Week		Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	08 : Curriculum and Instruction Sciences
Eligible Students
Keywords	Data science, pedagogy ,problem solving, decision making, critical thinking
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	Students are recommended to take the course after taking data science education in liberal arts.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Secondary School Technology and Information Education （Knowledge and Understanding）・To have understanding and knowledge of information education.
Class Objectives /Class Outline	Data-based education will become more and more important in the future. This class covers data science at the applied basic level for pre-service teachers. Students will learn regression and statistical hypothesis test using Excel, and machine learning and AI basics using Python. They will study the basics of data utilization and data science for solving problems in the field of education.
Class Schedule	Lesson 1 (Class is given by lecture) Guidance, questionnaire, etc. Lesson 2 (Class is given by lecture) On learning in Education and Machine Learning Lesson 3 (Class is given by lecture with exercise) Predicting student's learning status: predicting the future by regression (Excel is used) Lesson 4 (Class is given by lecture with practice) Is there a difference between data of two class of students?: Statistical hypothesis tests (Excel is used) Lesson 5 (Class is given by lecture with practice) Introduction to programming with Python Lesson 6 (Class is given by lectures and exercises) Practice of programming with Python. Lesson 7 (Class is given by lecture and exercises) About basics of machine learning Lesson 8 (Class is given by lecture and practice) Classifying data (Data visualization with Python) Lesson 9 (Class is conducted through exercises) Clustering Data (Clustering with Python) Lesson 10 (Class is given by lecture and exercises) Regression analysis and least-squares methods (Python is used) Lesson 11 (Class is given by lecture and exercises) Modeling of human skills using TKL parameters. Lesson 12 (Class is given by lecture and exercise) Modeling of human skills using TKL parameters. (prediction, decision) Lesson 13 (Class is conducted through exercises) Planning the use of data science in education. Problems. Lesson 14 (Class is conducted through exercises) Planning the use of data science in education. Solutions. Lesson 15 (Class is given by lecture) Summary Students should submit reports and take exams.
Text/Reference Books,etc.	Textbooks, reference books, etc. will be specified and distributed as needed.
PC or AV used in Class,etc.
(More Details)	You will use your laptop computer.
Learning techniques to be incorporated
Suggestions on Preparation and Review	Lesson 1: Understand the background of data science in education. Lesson 2: Think about the differences and similarities between human learning and machine learning. Lesson 3: Understand prediction by regression. Lesson 4: Understand how to perform a statistical hypothesis test. Lesson 5: Learn Python programming. Lesson 6: Deepen your understanding of Python. Lesson 7: Understand the basics of machine learning. Lesson 8: Understand classification of educational data using Python. Lesson 9: Understand clustering of educational data using Python. Lesson 10: Understand regression analysis and least-squares methods. Lesson 11: Learn how to model the learning process of human. Lesson 12: Deepen your understanding of acquisition models. Lesson 13: Students will deal with educational data. Review lessons 1-12. Lesson 14: Students will study in groups. Please discuss actively and deepen your understanding. Lesson 15: Review the whole class and deepen your understanding.
Requirements
Grading Method	Comprehensive evaluation through reports and tests.
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.

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