Reading Research Papers
A structured method for reading research papers efficiently without confusing technical detail with the central contribution.
- Research
- Methods
- Evidence
- Foundation
Research papers often appear difficult because their question, assumptions, evidence and technical implementation are presented together. This course develops a practical reading sequence that helps learners identify the central contribution first, determine which parts of the paper deserve closer examination, and distinguish genuine evidence from supporting technical detail. The method is designed for repeated use across economics, science, statistics and machine-learning research.
Course facts
| Offered by | Gordon Institute Open School |
| Academic lead | Gordon AI Tutor |
| Level | Foundation–Intermediate |
| Format | Online · Self-paced |
| Expected study | 8–12 hours |
| Language | English |
| Prerequisites | No formal prerequisites; some quantitative literacy is helpful |
| Assessment | Exercises and final assessment |
| Recognition | Certificate of completion |
| Last reviewed | September 2026 |
Overview
Research papers are easier to read when the reader knows what to search for. This course teaches a repeatable sequence for identifying a paper’s question, contribution, assumptions, evidence and limitations.
Learners practice separating the core research design from technical implementation. The same framework is applied to empirical, modelling and machine-learning papers so that differences in method become easier to recognize.
The aim is not to turn every reader into a specialist. It is to help serious readers decide what a paper actually establishes and which parts deserve closer inspection.
Reading every line in order is rarely the most efficient first pass through a research paper.
Learning objectives
By the end of the course, learners should be able to:
- identify a paper’s research question and claimed contribution;
- distinguish assumptions, data, method and conclusion;
- locate the evidence that supports the principal claim;
- recognize common threats to identification, measurement and external validity;
- decide which technical sections require deeper study;
- summarize a paper without overstating its findings.
Intended participants and prerequisites
Intended participants
- students beginning research-oriented study;
- professionals who use academic evidence in decision-making;
- writers who summarize scientific or economic research;
- research assistants and contributors preparing literature reviews.
Prerequisites
No formal prerequisites; some quantitative literacy is helpful.
The course is designed for independent online study. Learners should be prepared to check assumptions, reproduce key reasoning and complete the final assessment rather than treat the material as passive reading.
Course structure
The course is organised into six units. Each unit combines concise explanation, worked examples and short analytical exercises.
| Unit | Subject | Principal questions |
|---|---|---|
| 1 | The research question | What problem is the paper actually trying to solve? |
| 2 | Contribution | What is genuinely new relative to prior work? |
| 3 | Method | Which assumptions connect the question to the empirical or mathematical design? |
| 4 | Evidence | Where does the paper show that its claim is supported? |
| 5 | Limitations | Which conclusions depend on fragile assumptions or narrow data? |
| 6 | Efficient reading | How should a first pass differ from a replication or specialist review? |
Teaching and assessment
Teaching is delivered through concise lectures, technical notes, worked examples and guided exercises. AI-supported interaction may be used to explain concepts, generate additional practice and challenge a learner's reasoning, but assessment remains based on the learner's demonstrated understanding.
| Component | Purpose | Weight |
|---|---|---|
| Short exercises | Check understanding of the principal concepts | 40% |
| Applied investigation | Use the course framework on a new problem or dataset | 30% |
| Final assessment | Integrated reasoning across the course | 30% |
A certificate of completion is issued only when the required assessments have been completed at the stated standard. Participation alone does not constitute completion.
Faculty
Gordon AI Tutor
AI course tutor created by professors at Gordon School of Business at Swiss Institute of Artificial Intelligence (SIAI)
Gordon AI Tutor is designed to support structured explanation, guided questioning and practice across Gordon courses. The tutor follows course materials and assessment logic developed by faculty and is intended to supplement, not replace, academic judgment.
Materials and technical requirements
Learning environment
Learners need a current web browser and access to the Gordon learning platform. Where a course uses code, spreadsheets, datasets or external readings, the relevant requirements are identified inside the unit.
Course materials
Required readings, datasets and practice materials are identified within each unit. Public courses are designed so that required core materials can be accessed without a separate tuition charge.
Continue to the course
Enter the learning platform to access the lectures, readings, exercises and assessments for this course.