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EP30.FSF1117 — English for Applied Mathematics | Chương trình đào tạo NEU

EP30.FSF1117 — English for Applied Mathematics

Credits
3
Self-study hours
90
Showing intake
K68-2026

Course description

This course serves as a gateway to academic English in Applied Mathematics for first-year students. It develops students’ ability to use English as a tool for studying foundational mathematics while gradually introducing the terminology and discourse conventions of core applied mathematical fields. The course strengthens reading, writing, listening, and speaking skills in academic contexts, with emphasis on mathematical definitions, explanations, and structured reasoning.

In addition to reinforcing English used in calculus, linear algebra, and introductory probability, the course provides early exposure to key concepts and terminology from optimization, operations research, numerical methods, mathematical modeling, and data-oriented applications. The focus is not on technical depth, but on understanding how mathematical ideas are formulated, described, and communicated in English-language textbooks and academic materials.

Through lectures and seminar-based activities, students practice interpreting mathematical texts, writing structured explanations and short summaries, presenting mathematical ideas, and participating in academic discussions. The course prepares students for advanced study in Applied Mathematics where English is used as a primary medium for learning, research, and professional communication.

Main Topics

(A) Academic English Foundations

Academic English for foundational mathematics (calculus, linear algebra, probability)

Mathematical definitions, theorems, examples, and explanatory language

Academic vocabulary and terminology in Applied Mathematics

(B) Terminology in Core Applied Mathematics Fields

Introduction to terminology in optimization and operations research

Introduction to terminology in numerical methods and computational mathematics

Introduction to terminology in mathematical modeling and data analysis

(C) Academic Skills Development

Reading strategies for mathematical textbooks and academic materials

Writing structured summaries and mathematical explanations

Presenting mathematical ideas and interpreting quantitative results

Seminar discussion and academic interaction skills

Course requirements

Requirements for Final Examination

Students are allowed to take the final/terminal exams (50%) if their attendance score (10%) is 5 or higher (on a 10-point scale).

See Appendix 2, Rubric 1 for attendance evaluation.

Requirements for Class Attendance

Students are responsible for attending all classes. In case of absence due to force majeure, students must provide sufficient and reasonable proof. For each absence, 1 point will be deducted from the attendance score. Students who miss more than 3 classes, with or without valid reasons, will be considered as failing to complete the course and must re-register.

Students will be awarded points for constructive comments. These points will be added to attendance and group assignment scores.

Groups that do not submit their group work will receive a score of 0. Late submissions will incur a deduction for each day they are late.

Requirements for In-class Behaviour

The course is conducted on the principle of mutual respect between learners and lecturers. Any behavior that disrupts the teaching and learning process is strictly prohibited.

Students must arrive on time for class. Those who are more than 10 minutes late will not be allowed to attend. Noise and other disturbances that affect the learning process are not permitted.

Laptops and tablets may only be used for taking notes and performing calculations related to lectures and exercises. Use for any other purpose is strictly prohibited.

Lecturers

  • Nguyen Van Quy
  • Tong Thanh Trung
  • Vuong Van Yen
  • Nguyen Duc Nam

Teaching plan

Week 1

Lecture 1: Introduction to Academic Mathematical English Course overview Structure of mathematical definitions Logical connectors (if, then, suppose, therefore) Reading symbols and formulas aloud Academic vocabulary building
References
[1] Unit 1; [3] Preface & notation
Activities
Lecturer introduces syllabus; models definition analysis; students practice reading and paraphrasing definitions
Assessment
Rubric 1

Week 2

Seminar 1: Presenting Mathematical Definitions Function, matrix, derivative definitions Identifying hypothesis and conclusion Paraphrasing formal statements Peer feedback techniques
References
[1] Unit 1
Activities
Student presentations; peer correction; group discussion
Assessment
Rubric 1

Week 3

Lecture 2: English for Calculus Terminology of limits and continuity Derivative concepts and interpretation Integral meaning and applications Optimization vocabulary Structure of theorem statements
References
[1] Calculus Unit; [2] selected sections
Activities

Learning resources

  • N.V. Solovey & I.V. Letunovskaya, English for Students of Mathematics (Lecture notes)
  • Dan Bressoud, Calculus Reordered: A History of the Big Ideas (selected terminology sections)
  • David C. Lay, Linear Algebra and Its Applications (terminology & reading extracts)
  • Sheldon Ross, A First Course in Probability (terminology sections)
  • Hamdy A. Taha, Operations Research: An Introduction (introductory terminology)
  • Jorge Nocedal & Stephen Wright, Numerical Optimization (introductory terminology)
  • Selected short academic articles (provided by lecturer)
Lecturer explains terminology and theorem structure; guided explanation practice
Assessment
Rubric 1

Week 4

Seminar 2: Explaining Calculus Concepts Presenting Mean Value Theorem Explaining optimization problems step-by-step Interpreting graphs in English Structured oral explanation practice
References
[1] Calculus Unit
Activities
Student presentations; structured explanation exercises
Assessment
Rubric 1

Week 5

Lecture 3: English for Linear Algebra Vector space terminology Linear independence and basis Eigenvalues and eigenvectors Matrix applications in modeling Explaining proofs in English
References
[1] Algebra Unit; [3] selected sections
Activities
Lecturer explains terminology; demonstration of structured explanation
Assessment
Rubric 1

Week 6

Seminar 3: Linear Algebra Presentation Skills Explaining eigenvalue problems Describing matrix transformations Writing short structured explanations Academic discussion practice
References
[3] selected sections
Activities
Group presentations; short academic writing task
Assessment
Rubric 1

Week 7

Lecture 4: English for Probability & Statistics Sample space and events Random variables and distributions Expectation and variance Regression terminology Interpreting statistical results
References
[1] Probability Unit; [4] selected terminology
Activities
Lecturer explains statistical terminology; reading comprehension practice
Assessment
Rubric 1

Week 8

Seminar 4 + Midterm 1: Review & Assessment Terminology consolidation Reading comprehension practice Short structured writing Midterm Test 1 (Multiple-Choice Assessment)
References
[1], [4]
Activities
Review session and practice activities; followed by multiple-choice midterm assessment.
Assessment
Rubric 2

Week 9

Lecture 5: Optimization & Operations Research Terminology Objective function and constraints Feasible region Linear programming structure Modeling language in OR Interpretation of solutions
References
[5] Chapter 1; [6] Introduction
Activities
Lecturer explanation; modeling vocabulary exercises
Assessment
Rubric 1

Week 10

Seminar 5: Case Study in Linear Programming Reading a real-world problem Translating into mathematical form Explaining constraints Presenting solutions clearly
References
[5] Chapter 1
Activities
Group work; structured solution explanation; presentation
Assessment
Rubric 1

Week 11

Lecture 6: English for Numerical Methods Approximation and numerical error Iterative methods terminology Algorithm description structure Convergence vocabulary Scientific computing expressions
References
[6] Intro; Lecturer materials
Activities
Lecturer explanation; algorithm description modeling
Assessment
Rubric 1

Week 12

Seminar 6: Explaining Numerical Procedures Step-by-step algorithm explanation Discussing error and accuracy Interpreting computational output Peer review practice
References
Lecturer materials
Activities
Student explanation of algorithm steps; peer feedback
Assessment
Rubric 1

Week 13

Lecture 7: Applied Mathematics in Data Science Modeling terminology Data fitting and regression language Simulation and prediction vocabulary Ethical considerations in modeling Interdisciplinary communication
References
[7] selected article
Activities
Lecturer explains terminology in applied contexts; reading discussion
Assessment
Rubric 1

Week 14

Seminar 7 + Midterm 2: Academic Presentation & Writing Oral presentation structure Academic report organization Argument development Midterm Test 2 (Essay Assessment)
References
All materials
Activities
Group presentations; followed by written midterm assessment.
Assessment
Seminar activities → Rubric 1

Week 14

Seminar 7 + Midterm 2: Academic Presentation & Writing Oral presentation structure Academic report organization Argument development Midterm Test 2 (Essay Assessment)
References
All materials
Activities
Group presentations; followed by written midterm assessment.
Assessment
Midterm Test 2 → Rubric 3

Week 15

Seminar 8: Final Integrated Presentation Group applied topic presentation Question & answer session Peer evaluation Course reflection and summary
References
All materials
Activities
Group presentation; peer review; reflection
Assessment
Rubric 1