Syllabus data

Teacher name ： Saddam Khalid
Course Title
Seminar II (A)
Course Title in English
Seminar II (A)
Course Type
Basic specialized courses (Specialization-related courses)
Graduate Students
Eligible Students
Graduate School of Social Sciences
Target Grade
2Year
Course Numbering Code
KCWMS6MCA3
Credits
4.00Credits
The course numbering code represents the faculty managing the subject, the department of the target students, and the education category (liberal arts / specialized course). For detailed information, please download the separate manual from the upper right 'question mark'.
Type of Class
演習 (Seminar)
Eligible Year/Semester
Spring semester 2026,Fall semester 2026
(Spring semester)
Instructor
Saddam Khalid
Affiliation
Graduate School of Social Science
Language of Instruction
English
Related SDGs
8／9／12
Office Hours and Location
9 am to 6 pm Monday to Friday research building one room 323
Contact

Corresponding Diploma Policy
A double circle indicates the most relevant DP number and a circle indicates the associated DP.
Corresponding Undergraduate School DP
ー
Corresponding Graduate School DP
1◎／3◎／4◎
Corresponding University-Wide DP
1-1◎／N/a
Academic Goals of Teacher Training Course
Ability to keep polishing／Ability to teach and lean on

Course Objectives and Learning Outcome
Students will learn to conduct the research independently 
Subtitle and Keywords of the Class
Course Overview and Schedule
Lectures, class participation, discussions on research papers, and presentations are planned as class activities.
All students are advised to participate in said activities, failing which students will suffer in respect of learning
and grades. In order to assess achievement of course objectives and attainment of learning outcomes, there will
be a mechanism of formative evaluation. The students also need to submit the term paper//research proposal at
end of semester. Guidelines will be provided to write the term paper.

In-person/Remote Classification
In-person
Implementation Method and Remote Credit Limit Application
Uses of Generative AI
Fully permitted
Precautions for using Generative AI
Textbook
References
Contents and Estimated Time for Pre- and Post- Learning (Preparation and Review)
Contents of Active Learning
Week 01
Research Methodology for data collection
Week 02
Research design
Week 03
Selecting the Qualitative and Quantitative studies
Week 04
Data Analysis Using R
Week 05
Data Analysis Using R
Week 06
Data Analysis Using R
Week 07
Data Analysis Using R
Week 08
Data Analysis Using R
Week 09
Data Analysis Using R
Week 10
Data Collection Results Presentation
Week 11
Data Collection Results Presentation
Week 12
Analysis of Quantitative Data of Collected Data
Week 13
Analysis of Quantitative Data of Collected Data
Week 14
Analysis of Quantitative Data of Collected Data
Week 15
Final Presentation
Grading Criteria and Methods
Presentations 25%
Assignments  25%
Thesis Report Final Presentation 50%
How to Disclose Assignments and Exam Results
Through UNIPA
Precautions and Requirements for Course Registration
Practical Education
Remarks
In cases where any differences arise between the English version and the original Japanese version, the Japanese version shall prevail as the official authoritative version.