From Data to Argument: Scientific Writing in Practice

Course Description

This course aims to develop graduate students’ ability to produce academic and professional scientific writing in EMI contexts, with a focus on transforming data into argument across key disciplinary genres: 1) Lab Reports, 2) Progress Reports, and 3) Recommendation Reports. Through a genre-based and task-based approach, students will learn to construct data-supported arguments and communicate effectively based on authentic statistical materials in research and professional contexts. To better align the course content with its pedagogical objective, the course instructor will also adopt a consultation-integrated model to incorporate structured drafting, feedback, and revision cycles for each course members.

Course Objective

Core Competence (CC)

This course aims to adhere to the NTU educational objectives by fostering students’

CC 1: independent judgment and ability to innovate

CC 2: professional knowledge and skills

CC 3: communicative and team skills

Course Intended Learning Objective (CILO)

By the end of this course, students should be able to:

CILO 1: demonstrate familiarity with the structure and purpose of lab, progress, and recommendation reports, and apply this knowledge to their writing (CC 1 & 2)

CILO 2: present data, figures, and results in clear written explanations and use them to support claims (CC 1 & 2)

CILO 3: revise their writing based on feedback to improve clarity, organization, and accuracy (CC 1 & 3)

CILO 4: explain their reasoning and present scientific and professional findings for different purposes and audiences (CC 2 & 3)

Weekly Schedule

Week	Topic	Materials Used	Note
1	l Course introduction l Overview of report genres (lab/progress/recommendation reports)	Self-prepared materials	Module 1: Foundations of Scientific & Professional Writing CILOs 1 & 2
2	l Writing with strong, active verbs l Introduction to data presentation (tables, figures, trends)	Oehlerking, F. (2025).
3	l Writing with various sentences structures l From linking data to making claims	Berger (2014).
4	l Short writing task (data explanation) l In-class workshop & Peer Feedback	Iverson et al. (2023).
5	l Writing procedural clarity l IMRD structure (focus on Methods & Results)	Finkelstein et al. (2022). (Ch 5, 6)	Module 2: Lab Report Writing CILOs 1, 2, and 4
6	l Results: describing data and patterns l Avoiding over-interpretation	Santosh, K. C. (2024).
7	l Discussion: linking results to claims l Drafting Lab Report (In-class Task)	August, E. (2026).
8	l Individual Consultation 1 Ø Feedback on Lab Report Draft Ø Revision planning	Self-prepared materials	CILO 3: (revision)
9	l Purpose and structure of progress reports l Reporting progress	Finkelstein et al. (2022). (Ch. 8)	Module 3: Progress Report Writing CILOs 2, 3, & 4
10	l Purpose and structure of progress reports l Describing results	Finkelstein et al. (2022). (Ch. 9)
11	l Writing about problems, limitations, and delays l Describing future plans	Lindsell-Roberts, S. (2023).
12	l Structure of recommendation reports l Comparing options and criteria	Baker, M. (2013).	Module 4: Recommendation Report Writing & Giving Presentations CILOs 2, 3, & 4
13	l Writing data-supported recommendations l Decision-making language	Finkelstein et al. (2022). (Ch. 10 & 11)
14	l Drafting Recommendation Report l In-class feedback session	Finkelstein et al. (2022). (Ch. 12)
15	l Individual consultation 2 Ø Feedback on structure, clarity, and argument Ø Revision guidance	Self-prepared materials
16	l Individual oral presentation l Final report portfolio submission	Peer Evaluation Forms

Others

Required Reading

Finkelstein, L., Aune, J. E., & Potter, L. A. (2022). Technical Writing for Engineers and Scientists. McGraw-Hill Education (Australia) Pty Limited.

*Materials and handouts prepared by the instructor.

Reference Materials

August, E. (2026). Teaching Academic Writing in the Health Sciences: A Guide for Instructors, Mentors, and Trainers. Ann Arbor: University of Michigan Press.
Baker, M. (2013). Every page is page one: topic-based writing for technical communication and the web. XML Press.
Berger, R. E., Miller, W., & Library, I. E. (2014). A scientific approach to writing for engineers and scientists (1st ed.). Wiley
Hart, S. (2016). Writing in English for the medical sciences: a practical guide (1st ed.). CRC Press.
Iverson, C., Ehrenfeld, D., & Open Textbook, L. (2023). Processes: Writing Across Academic Careers. Open Textbook Library.
Oehlerking, F. (2025). Convince with Data, Inspire with Stories: Data Storytelling in Project Management.
Santosh, K. C. (2024). Advances in Artificial-Business Analytics and Quantum Machine Learning: Select Proceedings of the 3rd International Conference, Com-IT-Con 2023, Volume 2.
Lindsell-Roberts, S. (2023). Technical writing for dummies (Second edition. ed.). Wiley Publishing, Inc.

Grading Policy

Item	Weight	Description
HW Assignments	45%	Students should complete a series of report-based assignments by producing individual or groupwork lab, progress, and recommendation reports. Across these tasks, students organize information, present data, and support claims or decisions in writing. Emphasis is placed on structure, clarity, and appropriate use of report conventions, with revision following feedback and consultations.
In-class Tasks	20%	In line with the course topics in each module, students will be required to complete several text analysis and writing tasks. In cases where students are asked to produce written texts, AI-based editing tools will be incorporated into selected in-class writing activities to facilitate their understanding of the task objectives.
Oral Presentation	15%	Students will need to deliver a presentation based on their recommendation report or their research topic.
Participation	20%	Students’ participation will be graded based on their engagement in class discussions, COOL task completion rate, consultations, and attendance.