Syllabus

HONR 46400: Evidence-Driven Research — Honors College, Purdue University (Fall 2026)

Author

Davi Moreira


IMPORTANT

This document does not replace the official syllabus in the course Brightspace page. It is subject to change.


Course Description and Objectives

In this course, Honors students learn how to turn curiosity into credible, evidence-based insight without requiring a strong background in quantitative methods or computing. Students will learn to identify meaningful gaps in a chosen field, translate those gaps into well-scoped research problems, and formulate research questions that can be answered with data. Through a structured, supported workflow, students learn how to select and justify an appropriate quantitative method approach (description, statistical inference, predictive modeling, and/or causal reasoning) and how to use computational tools and AI to locate sources, operationalize concepts, analyze evidence, verify results, and document decisions responsibly. By the end of the course, students will be enabled to design and defend a rigorous research approach, interpret findings with appropriate uncertainty and limitations, and communicate results effectively in written and oral formats.

Course Website: https://davi-moreira.github.io/2026F_evidence_driven_research_purdue_HONR464/

Instructor

Instructor: Professor Davi Moreira

  • Email: dcordeir@purdue.edu
  • Class meetings: Monday / Wednesday / Friday — in person (time and room in the course syllabus). Every Monday and Wednesday lecture from Week 2 opens with a ten-minute lab meeting: I ask the room how the projects are going, and we talk through what you decided since last time, what the evidence looks like, and where anyone is stuck. Nobody is assigned to report, there is nothing to prepare beforehand, and nothing said in the lab meeting is graded. I lead the rest of the session.
  • Office Hours: see the course syllabus, or by appointment.

Note: Email responses are typically within 24 business hours. If you do not receive a response by the 24-hour mark, please email me again.

Learning Outcomes

By the conclusion of this course, you will be able to:

  1. Identify meaningful gaps in a chosen field and translate them into well-scoped research problems.

  2. Formulate research questions that can be answered with data.

  3. Select and justify an appropriate quantitative method approach: description, statistical inference, predictive modeling, and/or causal reasoning.

  4. Use computational tools and AI to locate sources, operationalize concepts, analyze evidence, verify results, and document decisions responsibly.

  5. Design and defend a rigorous research approach.

  6. Interpret findings with appropriate uncertainty and limitations.

  7. Communicate results effectively in written and oral formats.

Course Materials

  • Course book (free, required): EDR|AI — Evidence-Driven Research in the Age of AI: How to Design, Analyze, Verify, and Defend. It walks through the material at an undergraduate reading level, and every chapter has its own companion Colab notebook where you run the chapter’s code and complete its closing “It is your turn” section. EDR|AI is a work in progress: it is still under development and will keep growing and improving during the semester.

  • Recommended (free): Blair, Coppock & Humphreys (2023), Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign (RDSS, Princeton University Press). Read free online at book.declaredesign.org.

  • Computing (required): A laptop or desktop with internet access and a modern web browser. Course activities run in the browser through Google Colab.

  • AI tools: The course teaches responsible, documented AI use (see the AI Policy). Approved tools:

    • A general-purpose AI assistant of your choice is your everyday research assistant, used in and alongside Colab. No single tool is required: pick one you can reach reliably (for example, Google Gemini), record in your AI Research Ledger which one you used, and hold it to the same verification rules whichever you choose.
    • Purdue GenAI Studio supplies custom reviewer roles (for example, the Causal Identification Skeptic, the Poster Critic, and the Reproducibility Auditor) that you are encouraged to use when attacking your own work.
    • Microsoft Copilot (Purdue-provided, with data protection) and other general-purpose AI tools are permitted with disclosure.

Course Infra-structure

Brightspace: The Course Brightspace Page https://purdue.brightspace.com/ should be checked on a regular basis for announcements and course material.

Assessments

The course assesses the research chain itself: a semester-long research project carried through milestones, presented publicly at the Purdue Undergraduate Research Conference, and closed with a reproducible package, a bounded research note, and a written reflection on the semester’s work.

Assessment component Weight
Attendance (iClicker; 85% target) 1%
Participation (rubric) 9%
IYT Practice (“It is your turn” submissions) 15%
Lecture Notebooks (weekly, completion) 20%
Final Project 55%
Total 100%

Grading scale: A ≥ 93, A− ≥ 90, B+ ≥ 87, B ≥ 83, B− ≥ 80, C+ ≥ 77, C ≥ 73, C− ≥ 70, D ≥ 60, F < 60 (no curve).

Attendance (1%)

Attendance is checked with iClicker. The target is attendance at 85% or more of class meetings because the course depends on live investigation, feedback, and project work that cannot be fully reconstructed afterward.

Participation (9%)

Participation is graded as one block. It covers the required course surveys, the course-closing reflection, and other constructive contributions to the course. These items are scored for completion and timeliness, never for the opinions they express, and the lowest few scores are dropped automatically. The full list, the due dates, and the submission instructions are posted on the course page.

IYT Practice (15%)

Each required EDR|AI chapter closes with an “It is your turn” section that you complete in that chapter’s companion Colab notebook. Each section is due at 11:59 PM on the date that chapter’s reading was due. This work is graded for completion, not for the conclusions you reach: full credit when it arrives on time, half credit when it arrives within seven days of the deadline. The lowest few credits are dropped automatically. The dated list lives on the course schedule page, where each meeting row marks the sections due that night, and on Brightspace, where you submit them.

Lecture Notebooks (20%)

Each week you work in that week’s lecture notebook during class, and you hand that notebook in once a week. This work is graded on completion alone: the notebook was worked through and submitted on time. It is never graded on whether your answers came out right, and nothing you say in the lab meeting is graded at all. Work that arrives a little late still earns partial credit, and the lowest few weeks are dropped automatically. The due dates and the submission instructions are posted on the course page.

Final Project (55%)

Students will complete a practical evidence-driven research project culminating in a poster presentation at the Undergraduate Research Conference. Individual work is the default; you may instead complete the project in a group with the instructor’s approval before shared work begins. A comprehensive set of project guidelines will be provided, and the assessment structure will adhere to the following criteria:

The percentages below are shares of your final course grade, not shares of the project; together they make up the Final Project’s 55%.

  1. Milestone Deliverables (20%): Students will submit incremental project components on specific due dates. These deliverables allow for early feedback and ensure steady progress throughout the semester. Grades will reflect each milestone’s clarity, completeness, and timely submission.
  2. Peer Evaluation (10%): To encourage accountability and productive research work, students will evaluate their peers’ contributions. These assessments help ensure balanced participation and measure collaborative effectiveness.
  3. Peer Review (5%): Each student will review and provide constructive feedback on the other projects’ posters. This process encourages engagement, enhances critical analysis skills, and promotes a culture of constructive critique.
  4. Poster Presentation at the Purdue Undergraduate Research Conference (10%): A poster template and assessment rubric will be shared, and you are encouraged to review previous award-winning student posters for inspiration. Your final posters must be submitted by the due date indicated in the course schedule, after which they will be printed and distributed during a dedicated Poster Presentation Preparation class. Additional details on the conference can be found at https://www.purdue.edu/undergrad-research/conferences/index.php. As the event may not coincide with our regular class time, please communicate with your other course instructors in advance regarding potential scheduling conflicts. If any issues arise, please let me know. We will not hold our usual class immediately following the Poster Presentation, allowing you time to rest and catch up on other coursework. Consult the course schedule for further details.
  5. Instructor/TA Evaluation (10%): After the Undergraduate Research Conference your instructor will evaluate your final submission based on a rubric that will be shared.

Grade Challenges

Students who wish to challenge a grade must do so within 3 calendar days of the grade’s release. An exception applies for the last week of class, when assignments must be challenged within 1 calendar day to ensure timely computation of final course grades. Grade challenges must be grounded in legitimate disputes over research-methods principles or grading accuracy. Challenges based on post-hoc legalistic arguments or subjective dissatisfaction will not be considered.

To challenge an assignment score:

  1. Review posted solutions / rubrics thoroughly.
  2. If you still believe an error has been made, email the instructor with:
    • Course name, section, and date/time
    • Your name and Student ID
    • Assignment number / title
    • Specific deduction being challenged
    • Reason for the challenge, with reference to the solutions or grading rubric

Grades will not be discussed in class — before, during, or after class. Please use office hours for questions related to course content or assignment clarification. After the challenge period, all grades are final and cannot be revised further for purposes of calculating final course grades.

Course Policies and Additional Details

AI Policy

AI is a research assistant in this course, not a substitute for your judgment. You may use approved AI tools for course work unless an activity explicitly says otherwise. This course teaches responsible, documented AI use:

  • AI is your arm and your research assistant, not your brain.
  • Follow three rules: ask clearly, verify the output, and document what you used and decided.
  • Record required uses in your AI Research Ledger.
  • Check every factual claim, citation, source, calculation, and result against real evidence.
  • You remain responsible for everything you submit and must be able to explain and defend it.
  • Research questions, design choices, measurement decisions, ethical judgments, claims, uncertainty, and limitations remain your decisions.
  • Undisclosed or unverified AI output may be treated as an academic-integrity violation.

Some activities may be done without AI. Any no-AI activity will be clearly identified in its instructions.

Netiquette Guidelines

  • Be respectful and clear in communication; contribute constructively to in-class discussion and peer critique.
  • In any online session, join with your full name and mute when not speaking.

Accessibility, Accommodations, and Student Well-Being

  • Accessibility. Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please let me know so we can discuss options. You are also encouraged to contact the Disability Resource Center: drc@purdue.edu or 765-494-1247.
  • CAPS. Purdue University is committed to advancing student mental health and well-being. If you or someone you know is feeling overwhelmed, depressed, or in need of support, services are available. Contact Counseling and Psychological Services (CAPS) at 765-494-6995 and http://www.purdue.edu/caps/ during and after hours, on weekends and holidays, or through counselors located in the Purdue University Student Health Center (PUSH) during business hours.
  • Basic Needs Security. Contact ODOS if you are facing food or housing insecurity; no appointment needed.
  • Non-Discrimination Statement. Please refer to the Nondiscrimination Statement on Brightspace.
  • Emergency Situations. Please refer to the Emergency Preparedness page on Brightspace.

Additional Information

Refer to Brightspace for deadlines, academic integrity policies, accommodations, CAPS information, and non-discrimination statements. Registrar add/drop/modify deadlines: http://www.purdue.edu/Registrar/.

Subject to Change Policy

While I will try to adhere to the course schedule as much as possible, I also want to adapt to your learning pace and style. The syllabus and course plan may change during the term.

Schedule