AY 2022 Undergraduate School Course Catalog

/EL3 Elective English 3

2023/01/30

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開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
2.0
責任者
/Coordinator
PERKINS Jeremy
担当教員名
/Instructor
PERKINS Jeremy
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/11
授業の概要
/Course outline
Students will learn to use R in order to apply basic statistical techniques on linguistic data. The course will focus on analysis of acoustic data using the Praat software package. Students will learn how to run an acoustic study using Praat scripts, with techniques for organizing, processing and analyzing the results via R.
授業の目的と到達目標
/Objectives and attainment
goals
The following objectives will be of particular interest:
1. A basic understanding of acoustic phonetics.
2. How to use Microsoft Excel to work with data.
3. How to use Praat software to analyze acoustic data.
4. How to write scripts that allow processing large amounts of data.
5. Statistical techniques and hypothesis testing using R software.
授業スケジュール
/Class schedule
** Classes will be held at scheduled times via online Zoom lectures:
Class 1: Introduction
Classes 2-3: An Introduction to Acoustic Phonetics
Classes 4-5: Using Praat to analyze Acoustic Phonetics
Classes 6-7: Writing Praat Scripts
Classes 8-9: Review & Midterm Exam
Classes 10-11: Statistical Techniques
Classes 12-14: Using R for statistical analysis
教科書
/Textbook(s)
No textbook will be used. Course material will be made available online to students via Moodle.
成績評価の方法・基準
/Grading method/criteria
Students are required to take the TOEIC test in class during the final exam week to pass this course.

Homework Assignments (6 assignments worth 5% each)        30%
Midterm Exam (in class – class 8 or 9)                25%
Take-home Final Project (assigned class 10; due end of quarter)        25%
Active Participation                        20%
履修上の留意点
/Note for course registration
The class does not have a final exam. Instead, a final project is assigned.


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開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
2.0
責任者
/Coordinator
ROY Debopriyo
担当教員名
/Instructor
ROY Debopriyo
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/02
授業の概要
/Course outline
This is a basic technical communication course. As part of this course, students will learn the basics of information design and content management. The idea would be for students to learn how product information could be designed and delivered by creating ontologies based on product information classification standards and use case scenarios.  Students will be working on project-based activities with the IT industry in mind. This course will help develop entrepreneurial thinking in our students, and provide a context for understanding the IT market while developing basic technical communication skillset.
授業の目的と到達目標
/Objectives and attainment
goals
We will look forward to attaining multiple goals during the quarter.
• Understand the basics of content management and delivery
• How to analyse products in the market?
• How to organise product information?
• How to use effective strategies to make information easily available?
• How to use effective strategies to help retrieve relevant information?
• How to perform simple technical writing practices
• How to think critically and analytically?
• How to author business and procedural documents?
授業スケジュール
/Class schedule
Week # 1:
Session # 1: Syllabus Explanation and Introductory Assignment (How Smart
                   Products Function) - 10%
Session # 2: Intro to Content Management Systems & Content Delivery Portals

Week # 2:
Session # 1: PROJECT REPORT # 1 - Amazon Website Design - 20%
Session # 2: Continue Project

Week # 3: Ontology: Smart Product Description Object (SPDO)
Session # 1 & 2: PROJECT REPORT # 2 - Create Smart Product Ontology with
                          MindMeister (Apple Watch) - 20%

Week # 4:
Session # 1: Introduction to Intelligent Content Delivery / Intelligent Content &
                   How can it help Marketers
Session # 2: PROJECT REPORT # 3 - Create PI Classification for a Smart
                   Product - 20%

Week # 5:
Session # 1& 2: Continue with PROJECT REPORT # 3

Week # 6:
Session # 1: PROJECT REPORT # 4: Technical Presentation - Intelligent Content
                   & How can it help Marketers? / Using AI in E-Commerce  - 20%
Session # 2: Continue with PROJECT REPORT # 4

Week # 7:
Session # 1: PROJECT REPORT # 5: Analysing Microsoft HaloLens - 20%
Session # 2: Continue with PROJECT REPORT # 5

Final exam week: TOEIC online
教科書
/Textbook(s)
No text books needed. Lecture materials and readings will be posted in Moodle.
成績評価の方法・基準
/Grading method/criteria
Introduction Assignment (How a smart products function?) - Detailed Report - 10%
PROJECT REPORT # 1 - Amazon Website Design - 20%
PROJECT REPORT # 2 - Create Smart Product Ontology with Mindmeister- 20%
PROJECT REPORT # 3 - Create PI Classification for a Smart Product - 20%
PROJECT REPORT # 4: Technical Presentation - Intelligent Content - 20%
PROJECT REPORT # 5: Analysing Microsoft Halolens - 20%


- Minor alternation to the grading criteria might be needed
- 10% bonus marking applicable
履修上の留意点
/Note for course registration
Students must pass 10 credits of English coursework with minimum 400 in TOEIC before registering for this course. This is an advanced course. So, developing English language skills are important.

Note: Some of the selected teams or individuals may be making a conference presentation in July based on a major class project.

Students are required to take the TOEIC test in class during the final exam week to pass this course. This TOEIC requirement has been included to motivate students towards continuous improvement in English proficiency which is a strong indicator for success in professional environments.

参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)


https://www.antidot.net/content-delivery-platform/

https://www.cmswire.com/digital-marketing/what-is-intelligent-content-and-how-can-it-help-marketers/

https://www.linnworks.com/blog/artificial-intelligence-in-ecommerce



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開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
2.0
責任者
/Coordinator
BLAKE John
担当教員名
/Instructor
BLAKE John
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/11
授業の概要
/Course outline
Language is permeated with patterns. In English, the letter q tends to be followed by the letter u. The word moon is almost always preceded by the word the. Passive voice is usually formed with the verb be and a past participle (e.g. was done). Students study the patterns in the words, grammar, meaning and functions in texts. Less obvious patterns can be discerned using specialist pattern-matching tools.

This course enables students to investigate the relationship between patterns and language by exploring the vocabulary, grammar and structure of texts. Statistical tools and visualization tools are used to discover the less obvious patterns.
授業の目的と到達目標
/Objectives and attainment
goals
By the end of the course students will be able to:

(a) describe patterns and pattern-seeking behaviour
(b) identify patterns at lexical, grammatical and discourse levels
(c) use statistical and visualization tools to identify language patterns
(d) adapt or create a language visualization tool to discover language patterns

授業スケジュール
/Class schedule
Patterns and pattern seeking
Session 01 Patterns and pattern seeking
Session 02 Language system and its patterns
Session 03 Patterns in written English
Session 04 Patterns in spoken English
Session 05 Review and consolidation

Specific language patterns
Session 06 Functional patterns
Session 07 Clausal and phrasal patterns
Session 08 Lexical and grammatical patterns
Session 09 Lexical and grammatical patterns II
Session 10 Review and consolidation

Pattern detection and visualization
Session 11 Word patterns I
Session 12 Word patterns II
Session 13 Review
Session 14 Consolidation

Session 15 Final exam
教科書
/Textbook(s)
No textbook. Materials will be provided.
成績評価の方法・基準
/Grading method/criteria
Active participation: 30%
Quizzes: 30%
Final exam: 40%  

Students are required to take the TOEIC test in class during the final exam week to pass this course.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
None


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開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
3rd year , 4th year
単位数
/Credits
2.0
責任者
/Coordinator
ILIC Peter
担当教員名
/Instructor
ILIC Peter
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
This course will cover Information and Communications Technology (ICT) in Education particularly focusing on affordances offered by the technology. It will address the “what” and “how” of ICT in education, by providing students with theoretical and practical knowledge invaluable in the planning, implementation, and evaluation of ICT supported education. Students will learn about various affordances of educational technology, from current trends to future possibilities. Also, the course will help students develop awareness of the current advantages and limitations of ICT in the educational context. It will be of particular interest to students who are interested in becoming educators.

*Students are required to take the TOEIC test in class during the final exam week to pass this course.
授業の目的と到達目標
/Objectives and attainment
goals
1. Develop theoretical and practical knowledge of ICT in education.
2. Develop critical understanding of the limits of ICT in education.
3. Acquire skills in the planning and implementation of ICT in education.
授業スケジュール
/Class schedule
1. Introduction to ICT in Education
     a. Homework:
          i. Online Reading
          ii. Quiz 1 (8%)
2. History of ICT in Education
     a. Homework:
          i. Online Reading
          ii. Quiz 2 (8%)
3. Synchronous/Asynchronous
     a. Homework:
          i. Online Reading
          ii. Quiz 3 (8%)
4. Ubiquitous Learning
     a. Homework:
          i. Online Reading
          ii. Quiz 4 (8%)
5. Active Knowledge Making
     a. Homework:
          i. Online Reading
          ii. Quiz 5 (8%)
6. Multimodal Meaning
     a. Homework:
          i. Online Reading
          ii. Quiz 6 (8%)
7. Recursive Feedback
     a. Homework:
          i. Online Reading
          ii. Quiz 7 (8%)
8. Collaborative Intelligence
     a. Homework:
          i. Online Reading
          ii. Quiz 8 (8%)
          iii. Project Outline (5%)
9. Metacognition
     a. Homework:
          i. Online Reading
          ii. Quiz 9 (8%)
10. Differentiated Learning
     a. Homework:
          i. Online Reading
          ii. Quiz 10 (8%)
11. The Future
     a. Homework:
          i. Presentation PowerPoint upload
12. Project Presentations (15%)
13. Project Presentations
14. Project Presentations
15.   Final exam week: TOEIC online
教科書
/Textbook(s)
No textbook will be used. Course material will be made available online to students via Moodle.
成績評価の方法・基準
/Grading method/criteria
Online Reading Quizzes: 80%
Project: Total 20%
     • Project Outline: 5%
     • Project Presentation (PowerPoint): 15%
Notes:
Late assignments will lose 10% per day.
After 5 days, a late assignment will receive a mark of 0%.
Not participating in class activities will result in -2%.
Being late 3 times will be equivalent to 1 absence.
Being more than 30 minutes late will equal 1 absence.
履修上の留意点
/Note for course registration
Students are required to take the TOEIC test in class during the final exam week to pass this course.


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開講学期
/Semester
2022年度/Academic Year  3学期 /Third Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
2.0
責任者
/Coordinator
WILSON Ian
担当教員名
/Instructor
WILSON Ian
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/28
授業の概要
/Course outline
In this course, open-source acoustic analysis software ("Praat") will be mastered so that students have skills to analyze speech/pronunciation beyond the classroom. Students will be taught about properties of the acoustic signal and they will be taught how to write Praat scripts to automate acoustic analysis. Students will sometimes be required to submit voice recordings demonstrating their pronunciation. This course provides basic phonetic knowledge that is necessary for work in fields such as speech recognition and speech synthesis.
授業の目的と到達目標
/Objectives and attainment
goals
By the end of the course, students will:
(a) have learned the English vocabulary for acoustics concepts
(b) have acquired detailed knowledge of how to use Praat software, including writing scripts to automate acoustic measurements and to manipulate sounds
(c) know how to use waveforms and spectrograms to analyze properties of speech such as formant patterns, duration, intensity, pitch (including sentence intonation), voice onset time (VOT), etc.
(d) know the acoustic properties of English vowels, including schwa
(e) have had much practice pronouncing English words and sentences
授業スケジュール
/Class schedule
Class 1: Introduction to the course and to Praat software; initial recording
Class 2: Acoustics glossary; English vowel acoustics
Class 3: Using TextGrid files to label sounds in Praat
Class 4: Praat script writing 1
Class 5: Praat script writing 2; homework assignment
Class 6: Manipulation of Pitch and Duration
Class 7: Intonation and rhythm in English
Class 8: Midterm exam
Class 9: English schwa - acoustic properties
Class 10: Praat environments; acoustics of /r/ and /l/
Class 11: Praat script writing 3; Form...Endform
Class 12: Reading speed; Pausing during speech; voice onset time (VOT)
Class 13: World Englishes; Final recording
Class 14: Review activity/test
Final exam week: TOEIC online
教科書
/Textbook(s)
Materials will be distributed in class or on the class website. This course will be organized in Moodle.  Students will use Praat open-source software in class on iMac computers, make recordings, and analyze their pronunciation. Note that Praat is free to download from the Praat website, if you want to download it to your own computer.
成績評価の方法・基準
/Grading method/criteria
Active participation and quizzes:  20%
Homework assignments:  20%
Midterm exam:  30%
Review activity/test:  30%
Students are required to take the TOEIC test in class during the final exam week to pass this course.
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
The course instructor Ian Wilson has practical working experience. He worked for GEOS Corporation for 8 years, where he was involved in teaching English-as-a-Foreign-Language (EFL) and training EFL teachers. Based on his experience, he can teach all 4 skills (speaking, listening, reading, writing) of EFL.

Praat website here: Praat website

Prof. Ian Wilson's YouTube channel: Ian Wilson - YouTube

CLR Phonetics Lab website:  CLR Phonetics Lab

Class:Lecture


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開講学期
/Semester
2022年度/Academic Year  2学期 /Second Quarter
対象学年
/Course for;
3rd year
単位数
/Credits
2.0
責任者
/Coordinator
LINGLE William
担当教員名
/Instructor
LINGLE William
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/02/03
授業の概要
/Course outline
This course aims to provide students with the cognitive tools to distinguish between statements of fact and opinion, between strong and weak logical arguments, and to identify how emotions and cultural values can influence the ways in which arguments are constructed, expressed, and understood.
授業の目的と到達目標
/Objectives and attainment
goals
By the end of the course students will be able to:

1) distinguish statements of fact from statements of opinion
2) identify sources of information by which statements of fact may be
        evaluated as correct or incorrect
3) identify logical arguments
4) identify appeals to cultural values and emotions within arguments
5) identify logical fallacies within arguments
6) evaluate arguments as strong or weak
7) recognize strengths and weaknesses of their own arguments and of
        those they disagree with
授業スケジュール
/Class schedule
Facts vs. Opinions
Session 1: Facts vs. Opinions 1
Session 2: Facts vs. Opinions 2
Session 3: Facts vs. Opinions 3
Session 4: Review and Quiz

Arguments
Session 5: Structure of Arguments
Session 6: Logical Reasoning / Venn Diagrams
Session 7: Fallacies 1
Session 8: Fallacies 2
Session 9: Evaluating Strength of Arguments
Session 10: Review and Quiz

Emotions and Values
Session 11: Logic and Morals
Session 12: Cultural Values
Session 13: Critical Media Literacy
Session 14: Final Review Activity/Test

Final Exam Week: TOEIC online
教科書
/Textbook(s)
No textbook. Materials will be provided by instructor.
成績評価の方法・基準
/Grading method/criteria
Participation activities: 25%
Exercises: 20%
Quizzes: 30%
Final Review Activity/Test: 25%

Students are required to take the TOEIC test in class during the final exam week to pass this course.
履修上の留意点
/Note for course registration
None
参考(授業ホームページ、図書など)
/Reference (course
website, literature, etc.)
All materials will be available on the course Moodle page.


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開講学期
/Semester
2022年度/Academic Year  4学期 /Fourth Quarter
対象学年
/Course for;
3rd year , 4th year
単位数
/Credits
2.0
責任者
/Coordinator
BENSON Stuart
担当教員名
/Instructor
BENSON Stuart
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/25
授業の概要
/Course outline
In addition to knowing general vocabulary, knowledge of specific vocabulary related to the field of Computer Science is critical.
Comprehension of texts, articles, presentations, lectures, and classes is critical to your success in the field.

In this Computer Science vocabulary course, we will learn the most important words in the field through numerous activities of reading, writing, listening, and speaking. In addition, a computer science word list will be the focus of the course, with the goal of learning and actively using these words on context.
授業の目的と到達目標
/Objectives and attainment
goals
The goal of this course is to learn the most frequent Computer Science vocabulary and use these words, receptively and productively.

By the end of the course, learners will:
a) have knowledge of several vocabulary learning strategies
b) have an understanding of the most frequent vocabulary in Computer Science
c) be able to comprehend these words in reading and listening activities
d) be able to use these words in speaking and writing activities
授業スケジュール
/Class schedule
Class 1: Introduction to the course
Class 2-4: vocabulary strategies to learn CS vocabulary
Class 5-9: Learning CS vocabulary through speaking/listening activities
Class 7: Mid-term vocabulary test
Class 10-13: Learning CS vocabulary through reading/writing
Class 14: Review activity / test
Final exam week: TOEIC online

教科書
/Textbook(s)
All materials will be provided by the professor via Moodle and in-class
成績評価の方法・基準
/Grading method/criteria
Active participation: 15%
Weekly vocabulary quizzes: 30%
Mid-term vocabulary test: 25%
End-term vocabulary test: 30%

Students are required to take the TOEIC test in class during the final exam week to pass this course.

Student obligations
It is the responsibility of students to read the syllabus and complete all assignments, quizzes, and tests on time.
If you do not bring the materials you need to class, you will be considered ‘absent.’ If you are exceed the maximum number of absences allowed, the final grade will be an F regardless of your final score for the course.

Academic integrity
Cheating/plagiarizing other’s work is not allowed. Sometimes the instructor will ask you to complete work in pairs or small groups, and sometimes individually. When working by yourself, you are not allowed to copy other students’ work.
履修上の留意点
/Note for course registration
You will need to spend time learning the vocabulary outside of class time.


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開講学期
/Semester
2022年度/Academic Year  1学期 /First Quarter
対象学年
/Course for;
3rd year , 4th year
単位数
/Credits
2.0
責任者
/Coordinator
BLAKE John
担当教員名
/Instructor
BLAKE John
推奨トラック
/Recommended track
先修科目
/Essential courses
更新日/Last updated on 2022/01/11
授業の概要
/Course outline
The primary aim of this content-based course is to enable participants to create a prototype authorship analysis tool using Python. Participants are first introduced to similarity detection and authorship attribution. Texts are then analyzed linguistically by both genre type and author. Case studies involving solving crimes using forensic evidence are used. Participants then develop a natural language processing (NLP) pipeline to detect similarities and differences of language features among a dataset of texts. Detection will involve both rule-based parsing and machine learning. Participants will practice the four language skills required to function in an English language environment. The course culminates in a software demonstration.
授業の目的と到達目標
/Objectives and attainment
goals
By the end of the course participants will:
(a) understand how language analysis can be used to solve crimes
(b) explain how authorship can be detected
(c) create a natural language processing pipeline in Python to analyze authorship
授業スケジュール
/Class schedule
This content of the course will be tailored to the requirements of the participants by the class teacher.

Block 1 Authorship and language

Session 1 Solving crimes using authorship analysis
Session 2 Types of authorship analysis
Session 3 Language as a fingerprint
Session 4 Similarity detection
Session 5 Case study I
Session 6 Case study II
Session 7 Review

Block 2 Prototype development

Session 8 Introduction to NLP using Python
Session 9 Introduction to the Natural Language Toolkit (NLTK)
Session 10 Scenario: Problem breakdown
Session 11 Prototype development I
Session 12 Prototype development II
Session 13 Prototype development III
Session 14 Demonstrations

Session 15 Final Exam
教科書
/Textbook(s)
No textbook. Materials will be provided.
成績評価の方法・基準
/Grading method/criteria
Active participation: 30%
Coursework: 30%
Final exam: 40%

Students are required to take the TOEIC test in class during the final exam week to pass this course.


Responsibility for the wording of this article lies with Student Affairs Division (Academic Affairs Section).

E-mail Address: sad-aas@u-aizu.ac.jp