Overview

The final grade consists of the following components:

Component Weight
Class Engagement 45%
Take-home Assignment 20%
Final Project 35%

Class Engagement

Your class engagement score is based on the total credits you earn from class participation. This includes both passive engagement (e.g., being cold-called, assigned tasks like mini-lectures) and active engagement (e.g., answering in-class questions, summarizing group findings).

Activities That Earn Credits

Activity Credits
Summarizing a paper before the discussion session 2
Answering in-class questions 1 (max 3 per week)
Participating group discussion 1
Summarizing group synthesis 2
Presenting a mini-lecture 4
Submitting in-class assignments max 2-3, depends on completeness
Attending others’ user studies 1 (max 3 per week)
TBA  

How Your Grade Is Calculated

We use a dynamic credit system to calculate your class engagement grade. To ensure fairness, the grading benchmark is set by the 5th highest credit earner (the “Anchor”).

  • Threshold: You only need 90% of the Anchor’s credits to receive a perfect 100% Class Engagement grade.
  • Below Threshold: If you have less than 90% of the Anchor’s credits, your grade equals your percentage of the Anchor’s credits plus 10%. For example, if you have 85% of the Anchor’s credits, your score is 95%.
  • Top Performers: The top 4 students receive fixed bonus scores of 110%, 106%, 104%, and 102% respectively.

Take-home Assignment

Assignment Points
CITI IRB Training 5
Project Proposal 5
HW1 Needfinding 10

Grading details for each assignment will be available in the individual handout.

Final Project

You will be expected to conduct original research in the HCI field.

Project Timeline

  1. Proposal Submission: At week 7, you will submit a project idea proposal.
  2. Topic Selection: All students are required to submit a proposal and will receive feedback from the instructor. However, they may join another student’s project or change projects even after submitting the proposal.
  3. Implementation: The second half of the semester will focus on implementing your project, incorporating the research methods learned in class.

Grading Criteria

The project will be graded based on:

  • Final presentation
  • Written report
  • Individual workload contribution

Project Proposal (5 pts + up to 5 bonus pts)

Submission: Individual. All students are required to submit their own project proposal.

Format: A one-page abstract that includes the following sections:

Section Description
Problem Statement What is your research question?
Motivation Why does this project matter? What are the existing work? Why are they not enough?
Proposed Method What research method will you use to address the question?
Expected Output What is the anticipated outcome (e.g., a prototype, a system, or an experiment report)?
Team Size By default, we ask you to complete your project independently. If not, you need justify why need a collaborator.

Grading: Submitting a complete proposal earns 5 points. Up to 5 bonus points may be awarded for proposal that are exceptionally strong in the following criteria:

Criterion Points Description
Soundness 1 Is the proposed method technically sound for addressing the research question?
Significance 1 Is the proposed problem important?
Novelty 3 Is the idea new or clearly underexplored?

Choosing a Project

By default, you will work on the project you proposed. However, if your proposal does not meet the criteria below, we may ask you to revise it or work on something else.

Your proposal should avoid the following issues:

Issue Description
Unrealistic scope The project is too large to complete within one semester, or the challenge is not realistically achievable (e.g., a 3-month ethnography study in a rural area, or building a ultimate interface that can do everything).
Unclear problem The proposal does not clearly explain the problem you plan to solve or the phenomenon you plan to study.
Unrelated to HCI Projects not related to HCI cannot be used as final projects (e.g., solving the Goldbach conjecture, or developing an AI agent for programming contests).
Existing work or too trivial Exactly repeating existing work without new contribution, or proposing work with minimal workload, is not acceptable.

Policy

Academic Integrity

All students are expected to uphold the highest standards of academic integrity. Plagiarism, cheating, and other forms of academic dishonesty are strictly prohibited. You must not submit work that is not your own or misrepresent others’ ideas as your own. Proper citation and attribution are required when referencing external sources. Violations of academic integrity will be handled in accordance with university policy and may result in disciplinary action.

AI Usage Policy

In general, we permit the use of AI tools (e.g., for proofreading, writing code, or QA).

However, there is one strict prohibition:

Under no circumstances should you use AI to generate data that is supposed to be collected from human participants or other measurement (unless the study itself is designed to evaluate AI-generated data).

Discouraged (but not forbidden):

While not prohibited, we discourage using AI to generate reflections or ideas that you should develop yourself. Offloading cognitive work to AI often backfires: you learn less, and the quality of your thinking suffers. Instead, we encourage an AI-collaborative approach—use AI as a partner to iteratively refine your documents, not as a replacement for your own reasoning.

Your responsibility:

You are fully responsible for all content you submit, regardless of how much was generated by AI. Any material you submit will be treated as your own work and graded accordingly. “It was generated by AI” is not a valid excuse for errors, inaccuracies, or integrity issues. You must verify the correctness, validity, and integrity of all AI-assisted content before submission.

Attendance

Regular attendance is expected. We do not take attendance and will not directly penalize absences. However, class engagement credits are closely tied to attendance and participation, so missing class will likely affect your credit accumulation.

Late Submissions

Late submissions may be subject to grade penalties unless prior arrangements have been made with the instructor.