Hiroshima University Syllabus

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Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA130001 Subject Classification Specialized Education
Subject Name 実用英語II
Subject Name
Subject Name in
Practical English II
ティン ヒェン アン,ドヒ タダシ
Campus Higashi-Hiroshima Semester/Term 3rd-Year,  Second Semester,  3Term
Days, Periods, and Classrooms (3T) Mon1-4:ENG 116
Lesson Style Seminar Lesson Style
(More Details)
Each lecture is followed by a tutorial as a set of two lessons. Lectures are exclusively in English. There will be simple quizzes and exercises to keep students alert. Each week, before 1 PM, the in-class exercises must be submitted.  Solutions for each homework assignment are to be typeset in English with latex and submit as a pair of latex source file and the resulting pdf file. 
Credits 1.0 Class Hours/Week   Language of Instruction E : English
Course Level 3 : Undergraduate High-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 01 : Mathematics/Statistics
Eligible Students 3rd year students in School of Informatics and Data Science
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
・D1. Knowledge and skills required for understanding the theoretical system of statistics and data analysis, and for precisely and efficiently analyzing qualitative/quantitative information in big data.
・A. Skills related to the development of an information infrastructure, information processing techniques, and technology for producing new added value through data analysis.
・ B. Ability to identify and solve new problems on their own by quantitative and logical thinking based on data, diverse perspectives, and advanced skills for information processing and analysis. 
Criterion referenced
Informatics and Data Science Program
(Comprehensive Abilities)
・C2. Skills for communication, reading, and writing in English, capabilities required for giving a good, clear oral presentation, and documentation and communication skills that contribute to active discussion.
Class Objectives
/Class Outline
Rapid globalization brings about a need to develop a practical proficiency in English that is applicable internationally. As a foundation to attain the required English proficiency, this course is about learning a wide range of vocabulary so as to be able to read and comprehend, in English, textbooks, articles, and manuals related to informatics and data science. The ability to express such academic contents in English will be developed. Moreover, the basic communication capability for various areas related to informatics and data science will be cultivated. This course will also be beneficial to students in helping them to acquire the ability to learn English on their own.   
Class Schedule Lesson 1 Relations and Orders
lesson2 Tutorial (Exercise with LaTeX)
lesson3 Limits and Continuity of Functions
lesson4 Tutorial (Exercise with LaTeX)
lesson5 Differentiation of Functions
lesson6 Tutorial (Exercise with LaTeX)
lesson7 Applications of the Derivative
lesson8 Tutorial (Exercise with LaTeX)
lesson9 Indefinite and Definite Integrals
lesson10 Tutorial (Exercise with LaTeX)
lesson11 Applications of Integrals
lesson12 Tutorial (Exercise with LaTeX)
lesson13 Double Integrals
lesson14 Tutorial (Exercise with LaTeX)
lesson15 Summary

Each student's performance is evaluated based on the in-class exercises and homework assignment reports. No examination will be conducted.

Critical thinking skills will be introduced through a wide range of applications from radiometric dating to cosmology. 
微分積分学 by 阿部, 岩本, 島, and 向谷 published by 培風館 (2018)
Everything you need to master Calculus and Differential Equations, online resource https://www.math24.net/
PC or AV used in
(More Details) Slides in the pdf format, which are written with latex's beamer, will be projected with Microsoft Teams 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Recommended to revise the contents after each class because they are linearly connected.   Each week, approximately 3 to 5 hours outside of the class are needed for revision and preparation for a report. 
Requirements Students need to bring their own laptops for online lectures and tutorials.   
Grading Method The credit will be evaluated based on individual reports and in-class exercises. 60/100 point is the minimum requirement. The evaluation is based on (i) fundamental understanding of linear algebra in English, (ii) problem solving skill, (iii) English proficiency demonstrated in the report, (iv) communication skill, (v) Class participation   
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
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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